Systems and methods for objective managment

Abstract

Described herein are systems and methods for objective management. A modeling module may generate a hierarchical optimization model to represent a task involving one or more high-level objectives. Dependencies of higher-level objectives within the objective model may be modeled as lower-level child nodes having defined logical relationships. Metrics of higher-level objective nodes may be derived from metrics determined for lower-level, child nodes based on logical relationships between the higher-level objective nodes and lower-level child nodes defined within the objective model. Optimal technological solutions may be selected based on the determined metrics and / or user-specified optimization criteria.

Description

SYSTEMS AND METHODS FOR OBJECTIVE MANAGMENT CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States Provisional Patent Application No. 63 / 515,835 filed July 26, 2023, which is hereby incorporated by reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Contract Number DE-AC07-05-ID14517 awarded by the United States Department of Energy. The government has certain rights in the invention. BACKGROUND

[0003] Unless otherwise indicated heFarein, the approaches described in this section are not prior art to the claims in this disclosure and are not admitted to be prior art by inclusion in this section.

[0004] Managing the design and implementation of high-level processes can be difficult and complex, which can lead to poor execution, inefficiency, inaccurate risk assessments, and even unsafe operating conditions. For example, a high-level process to modernize aspects of the operation of a facility, such as a power plant, may involve a wide array of diverse objectives, each of which may involve the deployment of different implementing technologies that may or may not be compatible with one another. It may be possible to satisfy the requirements of some objectives using of different technologies. However, it can be difficult to track or evaluate the degree to which technologies selected for these objectives will impact the implementation of other objectives and / or the high-level process as a whole. Moreover, it can be difficult to accurately predict impact of different technological implementations in complex processes, particularly in scenarios involving interrelated objectives involving interrelated technological implementations. SUMMARY

[0005] This overview is provided to introduce a selection of concepts in a simplified form that are further described in greater detail below. This overview is not intended to identify key or essential inventive concepts of the claimed subject matter, nor is it intended for limiting the scope of the claimed subject matter. Some example embodiments, alternative embodiments, and selectively cumulative embodiments are set forth herein. BA-1494-PCT Page 1 of 62

[0006] Disclosed herein are systems and methods configured to perform a systematic, automated, and broad analysis of a high-level task involving a set of interrelated objectives, the analysis configured to, inter alia, determine an implementation scheme for the task. The implementation scheme may be optimized according to specified criteria, such as cost, return on investment, risk, deployment time, sensitivity, and / or the like. The implementation scheme may define an optimal task scope and corresponding action sequence configured to achieve specified objectives. The implementation scheme may specify any suitable information pertaining to implementation of any aspect of the high-level task (and / or objectives), such as hardware (e.g., equipment, infrastructure upgrades, or the like), software (e.g., software deployments, software upgrades, and / or the like), human activities (e.g., installation, configuration, training, and / or the like), and so on.

[0007] By way of non-limiting example, the systems and methods for objective management disclosed herein may be utilized to manage high-level tasks pertaining to the modernization of power generating facilities, such as nuclear power plants or the like. By way of further example, the disclosed systems and methods for objective management could be used to manage the implementation of a net-zero initiative at a particular plant and / or an organization (e.g., management implementation of a net- zero initiative across a plurality of facilities of the organization). The disclosure is not limited in this regard, however, and could be adapted to analyze and / or manage any suitable type of high-level task (and / or objectives) pertaining to any suitable industry.

[0008] Disclosed herein are examples of a method for managing modernization tasks to implement high-level objectives at a facility, which may comprise, inter alia, initializing a hierarchical optimization model configured to represent the modernization task, the optimization model comprising a top-level node configured to represent the high-level objective; and constructing lower levels of the optimization model through top-down modeling operations involving higher-level nodes of the optimization model. A top-down modeling operation involving a higher- level node may comprise generating child nodes of the higher-level node within a lower level of the optimization model, the child nodes configured to represent respective requirements of the higher-level objective node and linking the child nodes to the higher-level node with a control node of the optimization model, the control BA-1494-PCT Page 2 of 62node defining a logical relationship between the higher-level node and the child nodes.

[0009] The disclosed method may further comprise determining metrics for respective nodes of the optimization model through one or more bottom-up metrics analysis procedures, wherein metrics of higher-level nodes of the optimization model are derived from metrics determined for lower-level child nodes. A bottom-up metrics analysis procedure to determine metrics for a higher-level node having lower-level child nodes may comprise determining a metrics relationship between the child nodes and the higher-level node and deriving metrics of the higher-level node from metrics determined for the child nodes based on the determined metrics relationship. A technological solution configured to satisfy one or more requirements of the high-level objective may be selected based, at least in part, on the metrics determined for the respective nodes of the optimization model.

[0010] The logical relationship defined between the higher-level node and the child objective nodes may comprise a Boolean logical relationship, such as an AND logical relationship, an OR logical relationship, an XOR logical relationship, or a NOR logical relationship.

[0011] The bottom-up metrics analysis procedure to determine metrics for the higher-level node having lower-level child nodes may further comprise determining the metrics relationship between the child nodes and the higher-level node based on the logical relationship defined between the child nodes and higher-level node within the optimization model. The logical relationship may comprise an OR logical relationship and the metrics relationship determined between the child nodes and the higher-level node may be configured to assign range values to respective metrics of the higher-level objective node, the range values configured to span metrics determined for each child node of the higher-level node. Alternatively, the logical relationship may comprise an OR logical relationship having a selected child node and the metrics relationship determined between the child nodes and the higher-level node may be configured to assign metrics of the selected child node to the higher-level node. In some examples, the logical relationship may comprise an AND logical relationship and the metrics relationship determined between the child nodes and the higher-level node may be configured to assign a duration metric of the higher-level node comprising a maximum duration of the child nodes. Alternatively, the logical BA-1494-PCT Page 3 of 62relationship may comprise an AND logical relationship and the metrics relationship determined between the child nodes and the higher-level node may be configured to assign a cost metric of the higher-level node comprising a sum of cost metrics of the child nodes. In some implementations, the logical relationship may comprise an AND logical relationship and the metrics relationship determined between the child nodes and the higher-level node may be configured to assign a probability of success metric of the higher-level node comprising a product of probability of success metrics of the child nodes.

[0012] The disclosed method may further comprise constructing a technological solution layer of the optimization model, which may comprise identifying a higher- level node having a requirement that can be satisfied by a technological solution, generating first child nodes for the higher-level node within the technological solution layer, the first child nodes configured to represent respective technological solutions that satisfy the requirement of the identified higher-level node, and generating a control node to link the generated child nodes to the identified higher-level node within the optimization model. Metrics of the first child nodes may be based on characteristics of the respective technological solutions represented by the first child nodes.

[0013] In some implementations, the disclosed method further includes identifying one or more redundant nodes within the optimization model, the redundant nodes comprising nodes having requirements satisfied by other nodes within the optimization model and / or defining multi-parent relationships for the redundant nodes identified within the optimization model, the multi-parent relationships configured to associate two or more higher-level nodes with a same lower-level node. BA-1494-PCT Page 4 of 62BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Examples of systems, methods, devices, and computer-readable storage media comprising instructions configured to implement aspects of objective management are set forth in the accompanying figures and detailed description:

[0015] FIG. 1 is a schematic block diagram illustrating an example operating environment in which aspects of the systems and methods for objective management disclosed herein may be practiced.

[0016] FIG.2A is a schematic block diagram illustrating an example of an objective management module configured to construct data structures configured to model implementation of interrelated objectives at a facility.

[0017] FIG.2B is a schematic block diagram illustrating aspects of a procedure for constructing an optimization model.

[0018] FIGS.2C and 2D are schematic block diagrams illustrating examples of an optimization model comprising control logic.

[0019] FIG. 3 is a schematic block diagram illustrating an example of a user interface configured for the development and / or analysis of an optimization model.

[0020] FIG.4 illustrates an example of an optimization model constructed through implementation of a top-down modeling procedure.

[0021] FIGS. 5A and 5B are schematic block diagrams illustrating examples of a user interface configured for the development and / or analysis of an optimization model configured to represent an objective having a plurality of alternative technological solutions.

[0022] FIGS.5C and 5D are schematic block diagrams illustrating further examples of a user interface configured for the development and / or analysis of an optimization model, as disclosed herein.

[0023] FIGS. 6A-6C are a schematic block diagrams illustrating examples of optimization models configured to define relationships between objectives and technological solutions.

[0024] FIGS.7-10 illustrate further examples of optimization models constructed through implementation of top-down modeling procedures, as disclosed herein.

[0025] FIGS. 11-18 comprise flow diagrams illustrating examples of methods for objective management as disclosed herein. DETAILED DESCRIPTION BA-1494-PCT Page 5 of 62

[0026] FIG. 1 illustrates an example of an operating environment 10 in which aspects of the system and methods for objective management disclosed herein may be practiced. The operating environment 10 may comprise a system 100 for objective management. As disclosed in further detail herein, the system 100 may be configured to manage the implementation of interrelated objectives at a facility 20. The facility 20 may comprise any suitable entity configured to implement and / or utilize processes involving high-level objectives having technological solutions including, but not limited to: a control system, an industrial control system, a cyber-physical control system, a utility, an electrical power utility, a power distribution system, an electrical grid, a power grid, a power generator, a plant, a power plant, a nuclear power plant, or the like. In the FIG.1 example, the facility 20 may comprise a power plant such as a nuclear power plant (NPP) or the like. The disclosure is not limited in this regard, however, and could be adapted to manage objectives pertaining to any suitable type of facility 20.

[0027] As illustrated in the FIG. 1 example, the system 100 may comprise and / or be coupled to an apparatus 101. As disclosed in further detail herein, the apparatus 101 may be configured to manage operations for achieving objectives pertaining to the facility 20. The apparatus 101 may be configured to manage the design, analysis, and deployment of technological solution(s) to achieve specified objectives pertaining to the facility 20. In some implementations, aspects of the apparatus 101 may comprise and / or be embodied by one or more physical components, which may include, but are not limited to: an electronic device, a computing device, a general- purpose computing device, an application-specific computing device, a mobile computing device, a smart phone, a tablet, a laptop, a server device, a distributed computing system, a cloud-based computing system, an embedded computing system, and / or the like.

[0028] In the FIG.1 example, aspects of the apparatus 101 may comprise and / or be embodied by computing resources 104 of a computing device 102. The computing resources 104 may comprise any suitable computing means including, but not limited to, processing resources 104-1, data storage and / or retrieval (DSR) resources (e.g., memory resources 104-2, non-transitory storage (NTS) resources 104-3, and / or the like), human-machine interface (HMI) resources 104-4, data interface (DI) resources 104-5, and / or the like. BA-1494-PCT Page 6 of 62

[0029] The processing resources 104-1 may comprise any suitable processing means including, but not limited to: processing circuity, logic circuitry, an integrated circuit (IC), a processor, a processing unit, a physical processor, a virtual processor (e.g., a virtual machine), an arithmetic-logic unit (ALU), a central processing unit (CPU), a general-purpose processor, a programmable logic device (PLD), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a System on Chip (SoC), virtual processing resources, and / or the like.

[0030] The DSR resources may comprise any suitable means for storing, retrieving, maintaining, and / or otherwise managing data which may include, but are not limited to: memory resources 104-2, NTS resources 104-3, and / or the like. The memory resources 104-2 may comprise any suitable memory means including, but not limited to: volatile memory, non-volatile memory, random access memory (RAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), cache memory, or the like. The NTS resources 104-3 may comprise any suitable non-transitory, persistent, and / or non- volatile storage means including, but not limited to: a non-transitory storage device, a persistent storage device, an internal storage device, an external storage device, a remote storage device, Network Attached Storage (NAS) resources, a magnetic disk drive, a hard disk drive (HDD), a solid-state storage device (SSD), a Flash memory device, and / or the like.

[0031] The HMI resources 104-4 may comprise any suitable means for human- machine interaction including, but not limited to: input devices, output devices, input / output (I / O) devices, visual output devices, display devices, monitors, touch screens, a keyboard, gesture input devices, a mouse, a haptic feedback device, an audio output device, a neural interface device, and / or the like.

[0032] The DI resources 104-5 may comprise any suitable data communication and / or interface means including, but not limited to: a communication interface, a I / O interface, a device interface, a network interface, an interconnect, and / or the like. In some implementations, the data interface 104-5 may be configured to communicatively couple the apparatus 101 to a network, which may include, but is not limited to: an electronic communication network, a computer network, a wired network, a wireless network, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), Internet Protocol (IP) networks, Transmission Control Protocol / Internet Protocol (TCP / IP) networks, the Internet, or the like. BA-1494-PCT Page 7 of 62

[0033] In some implementations, the apparatus 101 may further comprise and / or be coupled to a data storage system (DSS) 106. The DSS 106 may comprise and / or be embodied by DSR resources of the computing device 102, e.g., memory resources 104- 2, NTS resources 104-3, and / or the like. Alternatively, or in addition, aspects of the DSS 106 may be implemented and / or embodied by one or more external devices or systems, e.g., may comprise and / or be embodied by a remote system or device(s), external system, network-attached storage system, network-accessible storage system, and / or the like. The DSS 106 may be configured to maintain any suitable information pertaining to the objective management functionality disclosed herein. In the FIG.1 example, the DSS 106 may comprise a facility datastore (DS) 120 and / or library 130. The facility DS 120 may comprise information pertaining to the facility 20, including optimization models 122 configured to facilitate the design, analysis, and implementation of technological solution(s) that optimally achieve objectives pertaining to the facility 20, e.g., optimally achieve the objectives per specified optimization criteria, such as implementation time, cost, projected cost savings, risk, and / or the like. As disclosed in further detail herein, the library 130 may comprise generalized information pertaining to the systems and methods for objective management disclosed herein. By way of non-limiting example, the library 130 may comprise, inter alia: a) generalized information pertaining to objectives suitable for implementation at the facility 20 (and / or facilities 20 of a particular type, such as nuclear power plants or the like); b) generalized information pertaining to technological solutions for such objectives and / or characteristics of such technological solutions (e.g., economic, technical, and / or risk characteristics); c) generalized information pertaining to deployment schemes for respective technical solutions; and so on.

[0034] As illustrated in the FIG. 1 example, the apparatus 101 may comprise a facility management (FM) module 110 (or management module 110). As disclosed in further detail herein, the FM module 110 may be configured to manage the design, analysis, evaluation, selection, and / or implementation of facility modification strategies. As used herein, a facility modification strategy (FMS) may comprise and / or refer to a scheme for achieving specified objective(s) pertaining to the facility 20 through deployment of specified technological solution(s) within the facility 20. In other words, an FMS may comprise and / or refer to a specified set of technological BA-1494-PCT Page 8 of 62solution(s) and / or scheme for deploying the specified technological solution(s) within the facility 20. As used herein, an objective may comprise and / or refer to a goal, aim, intention, target, requirement, milestone, or other objective to be achieved through implementation of one or more technological solutions. Objectives may pertain to any suitable aspect of the facility 20, including but not limited to: process management (e.g., objectives may pertain to management operations, such as the automation of specified facility operations), data collection, data management, decision making, data presentation, and so on. As used herein, a technological solution (TS) may comprise and / or refer to a technology (future or existing), solution, service, product, hardware, software, deployment, installation, and / or technical functionality capable of being deployed within a facility 20. As used herein, deploying a technological solution may comprise and / or refer to procuring, installing, initializing, configuring, training, testing, implementing, maintaining, and / or otherwise utilizing the technological solution within the facility 20.

[0035] The FM module 110 may be configured to determine FMS for achieving specified objectives pertaining to the facility 20 and / or manage implementation of the determined FMS within the facility 20. In some implementations, the FM module 110 may be configured to determine optimal FMS for achieving specified objectives (and / or specified sets of interrelated objectives). As used herein, an optimal FMS may comprise and / or refer to an FMS that achieves a specified objective (and / or set of interrelated objectives) through technological solution(s) and satisfies specified optimization criteria. For example, determining an optimal FMS may comprise a) constructing an optimization model 122 configured to model a plurality of candidate FMS, each candidate FMS configured to achieve the specified objective(s) through deployment of respective technological solution(s) within the facility 20, b) assess the candidate FMS (e.g., assess economic, technological, and / or risk characteristics of respective candidate FMS), and c) select an optimal FMS from the candidate FMS according to specified optimization criteria (e.g., select the optimal FMS based on the economic, technological, and / or risk assessments of the candidate FMS). Alternatively, or in addition, the FM module 110 may be configured to determine optimal FMS for achieving specified objectives according to an optimization framework; determining an optimal FMS for achieving a specified objective pertaining to the facility 20 may comprise: a) constructing an objective optimization BA-1494-PCT Page 9 of 62model (OOM) configured to formulate the determination of an optimal FMS as an optimization problem per a designated optimization framework; and b) generating and / or evaluating candidate FMS to identify an optimal FMS in accordance with the designated optimization framework, which may comprise iteratively generating and / or evaluating candidate FMS to identify an optimal FMS that satisfies constraints of the OOP, minimizes an objective or cost function of the OOP, maximizes a utility function of the OOP, and / or the like, as disclosed in further detail herein.

[0036] Aspects of the FM module 110 may be implemented and / or embodied by computing resources 104 of the apparatus 101, as disclosed herein. For example, the FM module 110 may be configured for operation on processing resources 104-1 of the apparatus 101, utilize data storage and / or retrieval resources of the apparatus 101 (e.g., memory resources 104-2, NTS resources 104-3, and / or the like), may be embodied by computer-readable instructions stored within NTS resources 104-3 of the apparatus 101, and so on. In some implementations, the FM module 110 may comprise and / or be coupled to an artificial intelligence and / or machine-learning (AI / ML) platform, an AI / ML processing environment, an AI / ML processing toolkit, an AI / ML processing library and / or the like. Alternatively, or in addition, aspects of the FM module 110 may be implemented and / or realized by hardware components, such as application-specific processing hardware, an ASIC, FPGA, an ML processor, dedicated memory resources, and / or the like.

[0037] In the FIG.1 example, the FM module 110 may comprise and / or be coupled to, inter alia, a user interface module 112, an objective modeling module 114, and an optimization module 116.

[0038] The user interface (UI) module 112 may be configured to facilitate user interaction with the apparatus 101 and / or FM module 110, e.g., facilitate interaction with one or more user(s) 15. The UI module 112 may comprise and / or implement a user interface (UI) 115 configured to receive data from user(s) 15 (e.g., receive input data 111). For example, the UI module 112 may be configured to receive input data 111 pertaining to the facility 20, objectives pertaining to the facility 20, technological solutions, deployment schemes, optimization models 122, and so on. The UI module 112 may be further configured to generate UI 115 configured to provide output data 113 to the user 15 (e.g., display and / or otherwise present output data 113 such as information pertaining to alternative implementations of specified objectives, as BA-1494-PCT Page 10 of 62disclosed in further detail herein). The UI 115 may be implemented, embodied and / or realized by computing resources 104 of the apparatus 101, such as HMI resources 104-4 or the like. For example, the UI 115 may be configured to present information to the user 15 by use of HMI output devices such as a monitor or the like, receive data from the user 15 through one or more HMI input devices (e.g., keyboard, mouse, touchscreen, or the like), and so on.

[0039] As disclosed herein, the FM module 110 may be configured to determine FMS for achieving specified objectives pertaining to the facility 20. The objectives may pertain to any aspect of the facility 20 including, but not limited to facility modernization, facility security, enhancing technological capabilities of the facility 20, streamlining processes of the facility 20, reducing operating costs of the facility 20, improving safety, decreasing risks to structures, systems, and components (SSC) of the facility 20, improving personnel safety, increasing facility reliability, improving fault tolerance, increasing resilience of the facility 20, and / or the like. In some embodiments, the FM module 110 may be configured to manage the implementation of objectives configured to satisfy regulatory requirements and / or industry guidelines, such as a plant modernization pathway or the like. For example, the facility 20 may comprise a NPP and the FM module 110 may determine FMS for achieving objectives corresponding to the Plant Modernization Pathway for Light Water Reactor Sustainability (LWRS) program, Integrated Operation for Nuclear (ION), and / or the like.

[0040] The objective modeling (OM) module 114 may be configured to generate models for achieving specified objectives pertaining to the facility 20. More specifically the OM module 114 may be configured to construct models for specified objectives (and / or specified sets of interrelated objectives), the models configured to facilitate the design, implementation, analysis, and deployment of technological solution(s) that optimally achieve the specified objective(s), e.g., achieve objectives while satisfying specified optimization criteria, such as implementation time, cost, projected cost savings, risk, and / or the like. The models generated by the OM module 114 may, therefore, comprise and / or be referred to as optimization models 122. The optimization models 122 may comprise and / or be embodied by computer-readable data maintained within DSR resources of the apparatus 101 (e.g., within memory BA-1494-PCT Page 11 of 62resources 104-2, NTS resources 104-3), the DSS 106, and / or other suitable data storage and / or retrieval means.

[0041] As disclosed herein, in some implementations, the FM module 110 may be configured to store, retrieve, and / or maintain information pertaining to the facility 20 within a facility DS 120. The facility DS 120 may comprise any suitable information pertaining to the facility 20 including, but not limited to: infrastructure deployed within the facility 20 (e.g., information pertaining to existing technologies deployed, utilized and / or otherwise available within the facility 20 such as electrical power infrastructure, network communication infrastructure, and / or the like), facility modernization objectives, industry regulations and / or guidelines (e.g., LWRS, ION, and / or the like), information pertaining to organizational readiness of the facility 20 such as an Organizational Readiness Level (ORL) or the like (e.g., assessment of organization’s overall readiness to adopt new processes and technologies), information pertaining to human readiness of the facility 20 such as a Human Readiness Level (HRL) for facility personnel (e.g., assessment gaging readiness of personnel to implement new technologies and processes within the facility 20), modernization objectives of the facility 20, and so on.

[0042] The facility DS 120 may further comprise information pertaining to proposed and ongoing FMS pertaining to the facility 20. For example, the facility DS 120 may comprise optimization models 122 developed for specified objectives, e.g., optimization models 122 for the design, analysis, and / or optimization of FMS to achieve the specified objectives, as disclosed in further detail herein. The facility DS 120 may comprise optimization models 122 pertaining to objectives being evaluated for implementation at the facility 20. Alternatively, or in addition, the facility DS 120 may comprise implementation models 132, which may comprise information pertaining to FMS (and / or corresponding optimization models 122) that have been selected for implementation at the facility 20 and / or are in the process of being implemented at facility 20.

[0043] FIG.2A is a schematic block diagram illustrating an example of a system 100 configured to generative optimization models 122 for achieving specified objectives pertaining to a facility 20. The OM module 114 may be configured to construct optimization models 122 based on and / or by use of objective modeling (OM) data 220. OM data 220 may comprise any suitable information pertaining to any suitable BA-1494-PCT Page 12 of 62aspect of pertaining to the implementation of an objective (and / or set of interrelated objectives) within the facility 20 including, but not limited to information pertaining to: objectives pertaining to the facility 20, technological solutions for respective objectives, deployment of respective TS within the facility 20, and / or the like. For example, OM data 220 may specify a set of interrelated objectives configured to modernize aspects of the facility 20, improve security at the facility 20, comply with industry regulations or guidelines, and / or the like, as disclosed herein.

[0044] The FM module 110 may be configured to acquire OM data 220 by any suitable means and / or from any suitable source. For example, the FM module 110 may be configured to acquire OM data 220 from users 15, e.g., acquire user input data 111 through user interaction with UI 115 generated by the UI module 112. The OM module 114 may utilize the UI module 112 to generate UI 115 configured to enable users 15 to create, author, refine, modify, edit, program, test, debug, and / or otherwise develop optimization models 122. For example, the OM module 114 may utilize the UI module 112 to provide users 15 with an editor, integrated development environment (IDE), or the like, e.g., may provide an optimization model user interface 315, as disclosed in further detail herein in conjunction with, inter alia, FIG.3.

[0045] Alternatively, or in addition, the OM module 114 may be configured to acquire OM data 220 through and / or by use of DI resources 104-5 of the apparatus 101. The DI resources 104-5 may be configured to retrieve OM data 220 from a data storage system (e.g., DSS 106 or other, external system), a network, a data store, a library 130, and / or the like. As disclosed herein, the library 130 may comprise generalized information pertaining to objective management operations of the apparatus 101 including, but not limited to: an objective library 230, a technological solution library 232, a deployment library 234, and so on.

[0046] The objective library 230 may comprise generalized information pertaining to objectives suitable for implementation at the facility 20. For example, the objective library 230 may comprise information pertaining to objectives configured to modernize aspects of the facility 20, comply with industry regulations and / or guidelines (e.g., comply with the LWRS program, ION initiative, and / or the like), improve security of the facility 20, enhance technological capabilities of the facility 20, streamline processes of the facility 20, reduce operating costs of the facility 20, improve safety, decrease risks to SSC of the facility 20, improve personnel safety, BA-1494-PCT Page 13 of 62increase reliability, improve fault tolerance, increase resilience of the facility 20, and / or the like.

[0047] The technological solution (TS) library 232 may comprise generalized information pertaining to technical solution(s) for respective objectives of the objective library 230. The TS library 232 may comprise generalized information pertaining to alternative TS for implementing respective objectives. For example, the objectives library 230 may comprise generalized information pertaining to high-level objectives to deploy (and / or upgrade) the network infrastructure of a facility 20 and the TS library 232 may comprise generalized information pertaining to alternative TS for satisfying such high-level objectives (e.g., may comprise generalized information pertaining to various wired and / or wireless network implementations).

[0048] The deployment library 234 may comprise generalized information pertaining to deployment of respective TS of the TS library 232 within a facility 20. The deployment library 234 may comprise generalized information pertaining to the resources, activities, and / or activity objectives involved in deploying respective TS within a facility 20. For example, the deployment library 234 may comprise generalized deployment models for respective TS, as disclosed in further detail herein.

[0049] The optimization models 122 generated by the system 100 may comprise and / or be embodied by machine-readable data maintained within DSR resources of the apparatus 101, such as memory resources 104-2, NTS resources 104-3, a DSS 106, or the like. For example, the optimization model 122 illustrated in FIG. 2A may be maintained within the facility DS 120 of the DSS 106.

[0050] The disclosed optimization models 122 may comprise machine-readable data in any suitable format and / or structure. The optimization model 122 illustrated in the FIG.2A example may comprise and / or be embodied by a graph data structure 201 such as a directed graph, mesh, hierarchical tree or the like. Nodes 202 of the optimization model 122 may be configured to represent and / or model the implementation of one or more objectives by alternative technological solutions (and / or alternative combinations of technological solutions). The nodes 202 of an optimization model 122 may be configured to represent and / or model any suitable aspect of an FMS including, but not limited to nodes 202 configured to represent: a) objectives pertaining to the facility 20, b) technological solutions for specified BA-1494-PCT Page 14 of 62objectives (and / or alternative technological solutions for respective objectives), and c) deployment schemes for implementation of specified TS within the facility 20 (e.g., resources, activities, activity objectives, and so on, as disclosed in further detail herein). As used herein, nodes 202 configured to represent objectives may comprise and / or be referred to as objective nodes 202, nodes 202 configured to represent technological solutions may comprise and / or be referred to as technological solution (TS) nodes 202, nodes 202 configured to represent resources involved in the deployment of specified TS within the facility 20 may comprise and / or be referred to as resource nodes 202, nodes 202 configured to represent activities involved in the deployment of specified TS may comprise and / or be referred to as activity nodes 202, nodes 202 configured to represent activity objectives may comprise and / or be referred to as activity objective nodes 202, and so on.

[0051] Relationships between nodes 202 may be realized by edges or links 204 within the optimization model 120; each node 202 of the optimization model 122 may be connected to zero or more upper-level nodes 202 (e.g., parent node(s) 202) and / or zero or more lower-level nodes 202 (e.g., child node(s) 202). Relationships between nodes 202 may be defined by control nodes 220. Relationships between nodes 202 may be modeled by, inter alia, control nodes 220 of the optimization model 120. As disclosed in further detail herein, control nodes 220 may be configured to define parent-child relationships between upper-level parent nodes 202 and lower-level child nodes 202. As illustrated in the FIG. 2A example, a control node 220 may be configured to couple a parent objective node 202 within level L of the optimization model 122 to E child nodes 202 within a next lower level of the optimization model 122 (e.g., level L+1). Although not shown in FIG.2A to avoid obscuring details of the illustrated examples, the objective node 202 may be coupled to a parent node 202 within a next higher level of the optimization model 122 (e.g., the objective node 202 may be a child of a parent node 202 within level L-1 of the optimization model 122) and one or more of the E nodes 202 within level L+1 of the optimization model 122 may be parents to child nodes 202 within a next lower level of the optimization model (e.g., level L+2).

[0052] The optimization models 122 generated by the FM module 110 may be configured to facilitate the design, analysis, and / or optimization of FMS to achieve specified objectives pertaining to the facility 20. Determining an FMS to achieve a BA-1494-PCT Page 15 of 62specified set of one or more interrelated objectives may comprise initializing an optimization model 122 comprising high-level objective nodes 202 configured to represent respective objectives of the set of one or more interrelated objectives and performing top-down modeling procedures to break down higher-level objective nodes 202 into lower-level nodes 202, as disclosed in further detail herein. For example, higher-level objective nodes 202 may be broken down into a plurality of lower-level child objective nodes 202, each configured to model a respective aspect of the higher-level objective (e.g., the child objective nodes 202 may be configured to represent respective subobjectives of the higher-level objective or the like).

[0053] The OM module 114 may be further configured to generate TS nodes 202 for objectives that cannot be broken down into child objectives. The TS nodes 202 may be configured to model alternative technological solutions (and / or combinations of technological solutions) for higher-level objectives. As disclosed herein, TS may comprise and / or refer to a technology (future or existing), solution, service, product, deployment, installation, and / or technical functionality capable of being deployed, installed, performed and / or otherwise utilized within a facility 20. TS nodes 202 of the optimization model 122 may be configured to represent and / or model real-world implementations of abstract objective nodes 202. As disclosed in further detail herein, the disclosed optimization models 122 may be configured to associate objective nodes 202 with multiple TS nodes 202, which may enable such optimization models 122 to, inter alia, model the impact of alternative TS (and / or alternative combinations of TS).

[0054] In some implementations, nodes 202 of the optimization model 122 may comprise and / or be associated with node data 203. As used herein, node data 203 may comprise and / or refer to any suitable information pertaining to a node 202 and / or aspect of the optimization model 122 represented thereby (e.g., information pertaining to the objective, technological solution, resource, activity and / or activity objective represented by the node 202); node data 203 may include, but are not limited to: a name (e.g., a name, title, label, and / or the like), a description, an identifier, a unique identifier, requirement data, dependency data, metrics data (e.g., metrics 205, as disclosed in further detail herein), and so on.

[0055] As used herein, requirement data may comprise and / or refer to information pertaining to requirements, constraints, and / or conditions associated with a node BA-1494-PCT Page 16 of 62202. For example, a “Wireless Networking” objective node 202 configured to represent a high-level objective related to the implementation of wireless networking infrastructure within the facility 20 may comprise requirement data configured to define requirements of such wireless networking functionality, e.g., specify bandwidth requirements, protocol requirements, security requirements, and so on.

[0056] As used herein, dependency data may comprise and / or refer to information pertaining to the dependencies of a node 202 (if any). A dependency may correspond to, inter alia, implementation and / or deployment of specified technologies and / or technical functionality within the facility 20. For example, a high-level objective node 202 may depend on a plurality of lower-level child objective nodes 202 configured to model respective subobjectives of the higher-level objective. By way of further example, a “Network Infrastructure” objective node 202 may depend on child objective nodes 202 configured to represent alternative means for satisfying the “Network Infrastructure” objective, e.g., child objective nodes 202 including a “Wireless Networking” objective node 202 and a “Wired Networking” objective node 202. The “Wireless Networking” objective node 202 may depend on technological solution(s) capable of providing wireless networking functionality within the facility 20. These dependencies may be modeled by, inter alia, parent-child relationships within the optimization model 122. As disclosed above, the “Network Infrastructure” objective node 202 may be linked to child objective nodes 202 representing the alternative “Wireless Networking” and “Wired Networking” child objectives. The “Wireless Networking” and “Wired Networking” objective nodes 202 may be linked to respective child TS nodes 202. The “Wireless Networking” objective node 202 may be linked to child TS nodes 202 configured to represent alternative TS for implementing wireless networking functionality within the facility 20 (e.g., child TS nodes 202 configured to model deployment of respective wireless networking systems) and the “Wired Networking” objective node 202 may be linked to child TS nodes 202 configured to represent alternative TS for implementing wired networking functionality within the facility 20 (e.g., child TS nodes 202 configured to model deployment of respective wired networking systems).

[0057] Dependency data may be further configured to define criteria for determining states of respective nodes 202. As used herein, the state of a node 202 may comprise and / or refer to an indication of whether requirements and / or BA-1494-PCT Page 17 of 62dependencies of the node 202 are satisfied within the optimization model 122. The state of a node 202 may be set to “satisfied” in response to determining that the node 202 is linked to child nodes 202 that satisfy the dependencies thereof (and the dependencies of the child nodes 202 are satisfied). The state of a node 202 may be set to “unsatisfied” in response to determining that one or more of the requirements and / or dependencies of the node 202 are unsatisfied (and / or one or more child nodes 202 thereof are not satisfied).

[0058] In some implementations, the optimization model 122 may be further configured to determine metrics 205 for respective nodes 202. The metrics 205 of a node 202 may be configured to objectively quantify economic, technological, and / or risk characteristics pertaining to implementation of the node 202 within the facility 20. For example, the metrics 205 of nodes 202 configured to represent objectives may be configured to quantify the impact of implementation of the objectives within the facility 20 (e.g., impact on economic costs, safety, and so on), the metrics 205 of nodes 202 configured to represent specific TS may be configured to quantify the impact of implementation of the specific TS within the facility 20, and so on.

[0059] The FM module 110 may be configured to model any suitable metrics 205 pertaining to any suitable aspect of an objective (or technological solution thereof) including, but not limited to: duration (e.g., time required for implementation of the objective at the facility 20), cost (e.g., costs associated with implementation of the objective within the facility 20, such as capital costs or the like), cost savings (e.g., projected economic savings resulting from implementation of the objective at the facility 20), probability of success (e.g., probability of successful implementation of the objective at the facility 20), safety risk (e.g., safety risk associated with implementation and / or utilization of the objective within the facility 20), and so on.

[0060] In some implementations, one or more metrics 205 may be defined as value ranges. For example, the duration metric 205 of an objective node 202 may be defined as a time range such as 30 to 40 months, 35 months + / - 5 months, or the like (e.g., implementation predicted to require a minimum of 30 months and maximum of 40 months). Alternatively, one or more metrics 205 may be defined as single values, values associated with confidence quantities, and / or the like.

[0061] As illustrated in the FIG. 2A example, in some implementations, the OM module 110 may comprise and / or be coupled to a metrics analysis (MA) module 216. BA-1494-PCT Page 18 of 62The MA module 216 may be configured to determine metrics 205 for respective nodes 202. The metrics 205 of a node 202 may be determined in any suitable manner. For example, the metrics 205 of a node 202 may be set manually (e.g., through a UI 115 generated by the FM module 110), may be based on information retrieved from a DSS 106, may be derived from metrics 205 determined for the child nodes 202 thereof (if any), and / or the like.

[0062] As disclosed herein, relationships between nodes 202 may be defined by, inter alia, control nodes 220 of the optimization model 122. Control nodes 220 may be configured to define relationships according to control logic 225, e.g., Boolean logic or the like, as disclosed in further detail herein. Control nodes 220 may be configured to model parent-child relationships such as the parent-child relationship between the parent objective node 202 within level L of the optimization model 122 and the E child nodes 202 within level L+1 of the optimization model 122 illustrated in the FIG. 2A example.

[0063] The OM module 114 may comprise and / or be coupled to a model construction (MC) module 214. The MC module 214 may be configured to construct aspects of the optimization model 122. Constructing an optimization model 122 for a set of one or more interrelated objectives may comprise a) initializing the optimization model 122 (e.g., creating top-level objective nodes 202 configured to represent each of the one or more objectives), and b) constructing lower levels of the objective model 122 through implementation of a top-down modeling (TDM) procedure. The TDM procedure may comprise performing TDM operations on respective nodes 202 of the optimization model 122, the TDM operations configured to break down higher-level nodes 202 into a plurality of lower-level nodes 202. For example, objective nodes 202 may be broken down into child objectives (e.g., child objective nodes 202 configured to represent respective aspects of the higher-level objective, such as subobjectives or the like) and / or child TS (e.g., TS nodes 202 configured to represent TS for the higher-level objective), and so on. The TDM operations may be performed in a hierarchical manner; the TDM procedure may comprise performing TDM operations on nodes 202 within a top layer of the optimization model 122, performing TDM operations on nodes 202 created within a next lower layer of the optimization model 122, and so on. BA-1494-PCT Page 19 of 62

[0064] FIG.2B illustrates an example of a TDM operation performed on an objective node 202 within level L of the optimization model 122. The TDM operation may be configured to disassemble the parent objective node 202 into one or more child nodes 202. By way of non-limiting example, the objective node 202 may be configured to represent a top-level objective; in other words, the objective node 202 may be defined at a top or highest level of the optimization model 122, e.g., L = 1.

[0065] In the FIG.2B example, the TDM procedure may comprise disassembling the objective node 202 into one or more subobjectives, e.g., E subobjectives in the FIG.2B example. The subobjectives may be modeled and / or represented by objective nodes 202-1 through 202-E generated within a next lower level of the hierarchical data structure 201, e.g., may be instantiated within level L+1 of the optimization model 122 as illustrated in FIG.2B. The objective nodes 202-1 through 202-E may be configured to model and / or represent any suitable aspect of the parent objective; the child objective nodes 202-1 through 202-E may be configured to model: a) subobjectives of the higher-level objective (e.g., the child objective nodes 202-1 through 202-E may be configured to model respective dependencies or requirements of the parent objective node 202), b) alternative objectives that satisfy dependencies of the higher-level objective (e.g., the child objective nodes 202-1 through 202-E may be configured to model different approaches to achieving the higher-level objective), and / or the like. Alternatively, or in addition, a higher-level objective node 202 may be broken down into one or more TS nodes 202, each TS node 202 configured to model and / or represent an alternative TS for achieving the higher-level objective (e.g., child nodes 202 representing different wireless networking technologies).

[0066] As disclosed herein, relationships between parent nodes 202 and child nodes 202 may be defined by, inter alia, control nodes 220 of the optimization model 122. The control nodes 220 may comprise control logic 225, which may be configured to manage any suitable aspect of a parent-child relationship including, but not limited to: logical relationships, metric relationships, and so on. As used herein, a logical relationship may comprise and / or refer to means for determining whether requirements of a parent node 202 are satisfied (e.g., means for determining the state of the parent node 202) based, at least in part, on states of the child nodes 202 thereof. Logical relationships may be defined using rules, conditions, criteria, logic, instructions, functions, equations, mathematical relationships, and / or the like. BA-1494-PCT Page 20 of 62Logical relationships may be implemented and / or embodied by control logic 225. Accordingly, as used herein, control logic 225 may comprise and / or refer to any suitable means for implementing a logical relationship, including but not limited to: Boolean logic (e.g., AND logic, OR logic, XOR logic, NOR logic, etc.), rules-based logic, fuzzy logic, machine-readable code, machine-readable instructions, executable code, executable instructions, and / or the like.

[0067] In a first non-limiting example, a control node 220 may be configured to implement AND logic specifying that the parent node 202 is satisfied if, and only if, each child node 202 of the parent is satisfied: ^^= ^^,^∧ … ^^,^Eq.1

[0068] In Eq. 1, ^^represents the state of a parent node 202 (P) having E child nodes 202, and ^^,^,…^^,^represent the states of the E child nodes 202.

[0069] In a second non-limiting example, a control node 202 may be configured to implement OR logic specifying that the parent node 202 is satisfied if any child node 202 is satisfied: ^^= ^^,^∨ … ^^,^Eq.2

[0070] Although particular examples of logical relationships are described herein, the disclosure is not limited in this regard; the control nodes 220 (and / or control logic 225) disclosed herein may comprise and / or implement any suitable type of logical relationship. By way of further non-limiting example, control nodes 220 may be configured to implement XOR logic configured to determine that the parent node 202 is satisfied if one, and only one, child node 202 is satisfied, NOR logic configured to determine that the parent node 202 is satisfied if, and only if, none of the child nodes 202 are satisfied, rules based logic, a logic tree, fuzzy logic configured to determine whether the parent node 202 is satisfied based on a fuzzy evaluation of the states and / or other characteristics of respective child nodes 202, and / or the like.

[0071] The OM module 114 may further comprise a metrics analysis (MA) module 216. The MA module 216 may be configured to determine metrics 205 for respective nodes 202. The MA module 216 may be configured to derive metrics 205 of upper- level, parent nodes 202 from metrics 205 determined for lower-level child nodes 202 of the parent nodes 202. The MA module 216 may determine metrics 205 for parent nodes 202 based, at least in part, on the logical relationship defined between the parent nodes 202 and child nodes 202, e.g., based on control logic 225 of the control BA-1494-PCT Page 21 of 62node 220 coupling the parent node 202 to the child nodes 202. For example, MA module 216 may be configured to determine metrics relationships between nodes 202. As used herein, a “metrics relationship” may comprise and / or refer to a relationship defined between the metrics 205 of a parent node 202 and metrics 205 of one or more of the child nodes 202 of the parent node 202. In other words, a metrics relationship may comprise and / or refer to means for deriving metrics 205 of a parent node 205 from metrics 205 of the child nodes 202 thereof in a bottom-up metrics analysis (MA) operation. A bottom-up MA relationship may, for example, comprise a mathematical relationship per Eq.3 below: ^^= ^^^^^,^, … , ^^,^^ Eq.3

[0072] 205 a parent 202- E thereof, where ^^,^, … , ^^,^represent the metrics 205 of child nodes 202-1 through 202-E, respectively, e.g., ^^,^represents the metrics 205 of a first child node 202 of parent node 202 (P), ^^,^represents the metrics 205 of child node 202 (E) of the parent node 202 (P), and so on.

[0073] In some implementations, the MA module 216 may determine metrics relationships for parent nodes 202 based, at least in part, on logical relationships defined between the parent nodes 202 and the child nodes 202 thereof within the optimization model 122. The MA module 216 may be configured to derive metrics relationships for parent nodes 202 from control logic 225 of the child control nodes 220 of the parent nodes 202.

[0074] The MA module 216 may define metrics relationships for control nodes 220 configured to implement a Boolean AND logical relationships (e.g., determine AND metrics relationships) by aggregating, adding, multiplying, and / or otherwise combining metrics 205 of the corresponding child nodes 202. In some implementations, the MA module 216 may be configured to adapt metrics relationships to different types of metrics 205. As illustrated in Eq. 4-8 of Table 1 below, an AND metrics relationship may comprise applying a max function to duration or date-based metrics 205 (^^) per Eq.4 (since the duration to achieve the objective of a parent node 202 is dependent on the child objective having the longest duration), summing child cost metrics 205 ( ^^^) per Eq. 5, summing child cost savings metrics 205 (^^^) per Eq.6, multiplying PoS metrics 205 (^^^) per Eq.7 (or BA-1494-PCT Page 22 of 62utilizing another technique for evaluating joint probability), summing or multiplying safety risk metrics (^^^) per Eq.8, and so on.

[0075] Metrics Relationships for Logical AND Parent-Child Relationships 4 5 6 7 8

[0076] Although specific examples of metrics relationships for AND logical combinations are described herein, the disclosure is not limited in this regard and could be adapted to utilize any suitable means for deriving metrics 205 of parent nodes 202 from metrics 202 of child nodes 202. For example, probabilistic metrics 205 such as PoS may be combined using statistical techniques such as joint probability, joint distribution, independent event probability, conditional probability, and / or the like.

[0077] As disclosed in further detail herein, OR logical relationships may be configured to model alternative objectives and / or technological solutions for satisfying higher-level objectives, e.g., child nodes 202 may represent alternative technological solutions for the corresponding parent objective node 202. By way of non-limiting example, an OR logical relationship may be configured to model alternative implementations for satisfying an objective to provide wireless networking functionality to the facility 20.

[0078] FIG.2C illustrates in an example of an excerpt from an optimization model 122 comprising an OR logical relationship. The optimization model 122 of the FIG.2C BA-1494-PCT Page 23 of 62example comprises a “Wireless Networking” objective node 202-WIFI representing an objective to deploy wireless communication functionality with a facility 20. As illustrated in the FIG. 2C example, the “Wireless Networking” objective may be satisfied by one of a plurality of suitable TS. As used herein, a “suitable” TS of an objective may comprise and / or refer to a TS that, inter alia, satisfies requirements of the objective. For example, suitable technological solutions of the “Wireless Networking” objective node 202-WIFI may comprise technological solutions determined to satisfy requirement data of the objective node 202-WIFI, e.g., per the node data 203 thereof.

[0079] The optimization model 122 of the FIG.2C example comprises E child nodes 202, each configured to model a respective TS that satisfies the “Wireless Networking” requirement(s) of the objective node 202-WIFI. The technological solutions represented by child nodes 202-WA through 202-WE may represent a plurality of different wireless networking infrastructure solutions available from respective manufacturers and / or vendors, each providing a respective set of wireless networking features and having respective metrics 205 (e.g., respective implementation duration, capital cost, cost savings, PoS, safety risk, and so on). The “Wireless Networking” objective of the parent node 202-WIFI may be satisfied by implementation of any one of the child nodes 202-WA through 202-WE and, as such, may linked in an OR logical relationship by control node 220-WIFI.

[0080] As further illustrated in FIG. 2C, the “Wireless Networking” objective node 202-WIFI is related to child nodes 202-WA through 202-WE through hierarchical, parent-child relationships defined by the control node 220-WIFI. The “Wireless Networking” objective node 202-WIFI represents the abstract, high-level objective of providing wireless networking functionality, whereas the child nodes 202-WA through 202-WE represent specific, real-world technological solutions suitable for achieving the parent objective that can be deployed within the facility 20. Accordingly, the portion of the optimization model 122 comprising the “Wireless Networking” objective node 202-WIFI (and parent objective nodes 202 thereof) may comprise and / or be referred to as an objective portion or objective layer(s) of the optimization model 122 and the portion of the optimization model 122 comprising the corresponding TS for the high-level “Wireless Networking” objective (e.g., child BA-1494-PCT Page 24 of 62nodes 202-WA through 202-WE) may comprise and / or be referred to as a TS portion or TS layer(s) of the optimization model 122.

[0081] According to the OR control logic 225 of the control node 202-WIFI, the metrics 205-WIFI of the “Wireless Networking” node 202-WIFI may be configured to reflect metrics 205 that span full range of alternative TS (prior to selection of a particular technological solution). In this manner, the metrics 205-WIFI of the parent “Wireless Networking” objective node 202-WIFI may accurately reflect the possible impact of the full range of alternative TS during analysis and / or visualization of the optimization model 122. In the FIG.2C example, prior to selection of an OR branch, the upper and lower ranges of the parent metrics 205 (e.g., metrics 205-WIFI) may be set to a maximum and minimum of the upper and lower ranges of the corresponding metrics 205 of the corresponding child nodes 202 thereof (e.g., metrics 205-WA through 205-WE) per Eq.9-13 of Table 2 below: Metrics Relationships for Logical OR (Prior to OR Selection) 0123

[0082] Alternatively, OR metrics relationships may be expressed as probabilities (as opposed to min / max quantities). For example, metrics 205 of parent nodes 202 may be derived from the metrics of child nodes 202 as, "(#)+ "(%)− "(# ^ ' %), where "(#)represents the probability of metric A, "(%)represents the probability of metric B, and "(# ^ ' %) represents the joint probability of A and B.

[0083] The OM module 114 may be configured to adapt OR metrics relationships in response to selection or activation of a particular child node 202 within the optimization model 122. In response to selection of a child node 202 (S) of E child nodes 202, the OR metrics relationship may be configured to incorporate the metrics 205 of the selected child node 202 (S) and ignore metrics 205 of other, unselected sibling nodes 202, e.g., may incorporate the duration, cost, cost savings, PoS, safety BA-1494-PCT Page 25 of 62risk, and / or other metrics 205 of the selected child node 202 (S), such that ^^= ^^,^. As illustrated in FIG.2D, the metrics 205-WIFI of the “Wireless Networking” objective node 202-WIFI may be updated in response to selection of one of the possible technological solutions for the “Wireless Networking” objective, e.g., may be set to metrics 205-WB in response to selection of the technological solution represented by the “Wireless B” child node 202-WB.

[0084] FIG.3 illustrates an example of a TDM operation implemented on a top-level objective node 202 configured to model a high-level objective pertaining to the facility 20, such as automation of aspects of the facility 20, e.g., an “Automated Plant” objective.

[0085] Aspects of the TDM procedure may be implemented by use of a UI 115 generated by the FM module 110. The UI 115 may be configured to facilitate development, visualization, and analysis of optimization models 122. As such, the UI 115 illustrated in the FIG. 3 example may comprise and / or be referred to as an optimization model (OM) UI 315. The OM UI 315 may be generated by the interface module 112. Aspects of the OM UI 315 may be configured for presentation on and / or by use of HMI resources 104-4 of the computing device 102.

[0086] The OM UI 315 may comprise UI components (UIC) 301, which may be configured to represent any suitable aspect of the optimization model 122. The UIC 301 may include but are not limited to: node UIC 302 configured to represent nodes 202 of the optimization model 122, link UIC 304 configured to represent connections between nodes 202, node data UIC 203 configured to display information pertaining node data of respective nodes 202 (e.g., requirement data, dependency data, state data such as an indication of whether requirement(s) of the node 202 have been satisfied, and so on), metric UIC 305 configured to display information pertaining to the metrics 205 determined for respective nodes 202, and so on.

[0087] The UIC 301 may further comprise objective UIC 310 configured to display information pertaining to respective objectives (and / or corresponding objective nodes 202 within the optimization model 122). In some implementations, the interface module 112 may be configured to adapt objective UIC 310 for different types of objectives. For example, the interface module 112 may be configured to represent different types of objectives using different UIC styles, e.g., may utilize different colors, shapes, fonts, and / or UI styles to represent objective nodes 202 pertaining to process BA-1494-PCT Page 26 of 62management, data collection, data management, decision making, data presentation, and so on.

[0088] In some implementations, the UIC 301 may further comprise control UIC 320, which may be configured to display information pertaining to respective control nodes 220 of the optimization model 122. The control UIC 320 may utilize different display styles to distinguish control nodes 220 configured to implement different types of control logic 225. The interface module 112 may be configured to utilize different colors, shapes, and / or styles to represent control nodes 220 configured to implement Boolean logic (e.g., AND, OR, XOR, NOR, and / or other types of Boolean logic), rules-based logic, fuzzy logic, and / or the like. For example, a control UIC 320 configured to represent a control node 220 that implements AND logic may be shaped similar to an AND gate, a control UIC 320 configured to represent a control node 220 adapted to implement OR logic may be shaped similar to an OR gate, and so on.

[0089] As illustrated in FIG. 3, the OM UI 315 may be configured to display information pertaining to an optimization model 122 having a top-level objective. Aspects of the top-level objective may be defined within input data 111 received by the FM module 110, as disclosed herein. In the FIG.3 example, the top-level objective pertains to plant automation. The optimization model 122 generated by the analysis model 110 may be configured to model implementation of the “Automated Plant” objective defined by the input data 111, e.g., may comprise a top-level “Automated Plant” objective node, as disclosed herein. The OM UI 315 may comprise an objective UIC 310 configured to represent the top-level “Automated Plant” objective node 220 of the optimization model 122.

[0090] The OM UI 315 may be configured to facilitate implementation of TDM operations on objective nodes 220 of the optimization model 122. FIG.3 illustrates an example of a TDM operation pertaining to the “Automated Plant” objective. In the FIG.3 example, the “Automated Plant” objective may be disassembled into three child objectives (e.g., the “Automated Plant” objective may have three requirements or dependencies), including objectives to automate plant operations, automate maintenance, automate support, and so on. The child objectives may be defined within the input data 111. Alternatively, or in addition, aspects of the child objectives may be specified through interaction with UI 115 generated by the UI module 112, such as the OM UI 315 illustrated in FIG.3. For example, the OM UI 315 may enable a BA-1494-PCT Page 27 of 62user 15 to implement TDM operations on selected objective nodes 220. In response to user selection of a component UIC 310 the interface module 112 may prompt the user to define and / or edit child objectives, which may be created within the optimization model 122. The TDM operation illustrated in FIG. 3 may, for example, be initiated in response to user selection of the “Automated Plant” objective UIC 310. In some implementations, TDM operations may be facilitated by reference to the library 130. For example, the MC module 214 may retrieve generalized information pertaining to objectives related to plant automation from an objective library 230, e.g., retrieve generalized information identifying subobjectives created for “Automated Plant” objectives.

[0091] In the FIG. 3 example, the top-level “Automated Plant” objective may be broken down into child objective nodes 202, which may be created within a next lower level of granularity within the optimization model 122, e.g., at level L+1, where the “Automated Plant” objective node 220 is instantiated at level L. The OM UI 315 may be updated to display information pertaining to the child objective nodes 220 created under the “Automated Plant” objective node 220 within the optimization model 122, including an “Automated Operations” objective UIC 310-1, an “Automated Maintenance” objective UIC 310-2, and an “Automated Support” objective UIC 310-3.

[0092] The “Automated Plant” objective may be disassembled into a plurality of subobjectives. Accordingly, the “Automated Plant” objective may require each child objective to be satisfied, e.g., per the input data 111, user interaction with UI 115 of the FM module 110 and / or the like. The OM module 114 may implement this logical relationship (e.g., requirement that all children of the “Automated Plant” objective be satisfied) by linking the “Automated Plant” parent objective to the child objectives through a control node 220 configured to implement an AND logical relationship. In other words, the child objective nodes 202 created by the OM module 114 within the optimization model 122 may be coupled to the parent “Automated Plant” objective node 202 by a control node 220 configured to implement AND logic, e.g., an AND logical relationship and an AND metric relationship. As illustrated in FIG. 3, the control node 220 of the “Automated Plant” objective may be represented by an AND control UIC 320.

[0093] FIG.3 further illustrates examples of metrics 205 and metric relationships generated by the OM module 114. The OM UI 315 may be configured to display BA-1494-PCT Page 28 of 62information pertaining to links 204 between nodes 202 and / or metrics 205 determined for respective nodes 202 of the optimization model 122. For example, the OM UI 315 may comprise metrics UIC 305. In the FIG.3 example, metrics UIC 305-1 through 305-3 may be configured to display information pertaining to the metrics 205 determined for the “Automated Operations,” “Automated Maintenance,” and “Automated Support” objectives. In some implementations, the link UIC 304 of respective nodes 202 may be adapted to include and / or reference information pertaining to metrics 205 determined for the nodes 202 (and / or vice versa). In the FIG.3 example, link UIC 304-1 through 304-3 may comprise and / or reference metrics UIC 305-1 through 305-3, e.g., metrics UIC 305-1 through 305-3 may be displayed on, over, and / or with link UIC 304-1 through 304-3, may be linked to the link UIC 304-1 through 304-3, and / or the like. In these examples, the links 204 of the optimization model 122 (and / or corresponding link UIC 305) may comprise and / or be referred to as parameterized connectors 204 (and / or parameterized link UIC 304).

[0094] As illustrated in the FIG. 3 example, implementation of the “Automated Operations” objective is predicted to require between 50 and 77 months, cost between 12000 and 19000 USD, provide cost savings between 1000 and 1200 USD, and have a PoS between 0% and 5%; the “Automated Maintenance” objective is predicted require between 60 and 65 months, cost between 15000 and 17000 USD, provide cost savings of 600 to 800 USD, and have a PoS between 0 and 7; and the “Automated Support” objective node 202-3 is predicted to require between 32 and 49 months, cost between 9890 and 18320 USD, provide savings of 1200 to 1550 USD, and have a PoS between 0% and 5%.

[0095] As disclosed herein, metrics 205 determined for lower-level objectives may flow up to higher levels of the optimization model 122. In other words, metrics 205 may be determined through bottom-up analysis operations in which metrics 205 assigned to lower-level nodes 202 are used to determine metrics 205 of higher-level nodes 202 within the optimization model 122. In the FIG.3 example, the metrics 205 of the child objectives (e.g., metrics 205 of metrics UIC 305-1 through 305-3) may flow up hierarchical parent-child links 204 of the optimization model 122 to the parent “Automated Plant” objective node 202, e.g., the metrics 205 of the “Automated Plant” objective may be derived from metrics 205 determined the child objectives thereof. As disclosed herein, the AND control logic 225 may define mathematical relationships BA-1494-PCT Page 29 of 62whereby metrics 205 of a parent node 202 may be derived from metrics 205 of the child nodes 202 thereof (represented by metrics UIC 310-1 through 310-3). According to the AND control logic 225 of the control node 220 coupled to the “Automated Plant” objective (represented by control UIC 320), the “Automated Plant” objective node 202 is predicted to require 60 to 77 months to complete, have a cost between 36890 and 54320 USD, provide savings between 2800 and 3500 USD, and have a PoS between 0% and 2%.

[0096] The TDM procedure disclosed herein may be implemented on lower-level child objectives. For example, objective nodes UIC 310-1 through 310-3 representing the “Automated Operations,” “Automated Maintenance,” and “Automated Support” objectives may be broken down into respective sets of child objectives, e.g., in respective TDM operations, as disclosed herein.

[0097] FIG. 4 illustrates an example of an optimization model 122 constructed through implementation of a TDM procedure on a lower-level, child node 202 configured to model implementation of a lower-level, child objective. FIG.4 illustrates an example of an OM UI 315 configured to implement aspects of a TDM operation on the “Automated Support” objective node 202 of the optimization model 122 represented by the objective UIC 310-3 illustrated in FIG.3.

[0098] In the FIG. 4 example, the “Automated Support” objective may be broken down into three child objectives, including an “Automated Security” objective, an “Automated RP” (radiation protection) objective, and an “Automated Procurement” objective. The TDM operation may comprise creating corresponding objective nodes 202 within the optimization model 122. The TDM operation may further comprise defining a parent-child relationship between the “Automated Support” objective and the child objectives. The “Automated Support” objective may require satisfaction of each child objective. The TDM operation may, therefore, comprise coupling the “Automated Support” objective node 202 to the child objective nodes 202 thereof through and / or by use of a control node 220 configured to implement AND logic.

[0099] As illustrated in FIG. 4, the OM UI 315 may comprise objective UIC 410-1 through 410-3. The objective UIC 410-1 through 410-3 may be configured to represent the child nodes 202 derived from the parent “Automated Support” objective node 202. The OM UI 315 may further comprise link UIC 404-1 through 404-3 and BA-1494-PCT Page 30 of 62control UIC 420 to couple the “Automated Support” objective UIC 310-3 to the child objective UIC 410-1 through 410-3 in accordance with an AND logical relationship.

[0100] The interface module 112 may be configured to provide information pertaining to the metrics 205 determined for respective nodes 202 of the optimization model 122, such as metrics UIC 305. In the FIG. 4 example, the OM UI 315 is configured to include metrics UIC 405-1 through 405-3, which may be configured to provide information pertaining to the metrics 205 determined for respective child nodes 202 of the “Automated Support” objective; the metrics UIC 405-1 may be configured to provide information pertaining to the metrics 205 determined for the “Automated Security” objective, the metrics UIC 405-2 may be configured to provide information pertaining to the metrics 205 determined for the “Automated RP” objective, and the metrics UIC 405-3 may be configured to provide information pertaining to the metrics 205 determined for the “Automated Procurement” objective.

[0101] As disclosed herein, the metrics 205 of parent nodes 202 within the optimization model 122 may be derived from metrics 205 determined for corresponding child nodes 202. The parent metrics 205 may be derived from the child metrics 205 in accordance with metrics relationships, which may be defined by, inter alia, control nodes 220 (and / or control logic 225) of the optimization model 122. In the FIG. 4 example, metrics 205 of the “Automated Support” objective may be derived from the metrics 205 represented by metrics UIC 405-1 through 405-3 in accordance with an AND metrics relationship, as disclosed herein.

[0102] FIGS.5A and 5B are schematic block diagrams illustrating further examples of OM UI 315 configured for objective management, as disclosed herein. In the FIG. 5A example, the objective node 202 represented by the “Automated RP” objective UIC 410-2 may be disassembled into child nodes 202. In other words, FIG.5A illustrates implementation of a TDM operation on the “Automated RP” objective.

[0103] The “Automated RP” objective node 202 may be broken down into a plurality of child objectives, which may be represented by a “Sample Collection” objective UIC 510-1, an “Automated Sample Analysis” objective UIC 510-2, an “Automated Storage” objective UIC 510-3, and an “Automated Reporting” objective UIC 510-4. The TDM operation may further comprise defining a parent-child relationship for the “Automated RP” objective. The “Automated RP” objective node BA-1494-PCT Page 31 of 62202 may be defined to require implementation of each child node 202 and, as such, the TDM operation may comprise coupling the “Automated RP” objective node 202 to the child nodes 202 by a control node 220 configured to implement AND logic (represented by control UIC 520 within the OM UI 315 illustrated in FIGS 5A-5D).

[0104] The OM UI 315 may further comprise metric UIC 305 configured to provide information pertaining to metrics 205 determined for respective nodes 202 of the optimization model 122. In the FIG. 5A example, the OM UI 315 comprises metrics UIC 505-1 through 505-4 configured to provide information pertaining to the metrics 205 determined for the “Sample Collection,” “Automated Sample Analysis,” “Automated Storage,” and “Automated Reporting” objectives (represented by objective UIC 510-1 through 510-4, respectively).

[0105] As illustrated in FIG. 5A, the “Sample Collection” objective may be broken down into child objectives, including a “Manual Sample Collection” objective (represented by objective UIC 510-1-1) and an “Automated Sample Collection” objective (represented by objective UIC 510-1-2).

[0106] The “Sample Collection” objective may be satisfied by implementation of either child objective. In other words, the “Manual Sample Collection” objective and “Automated Sample Collection” objective may represent alternative technological solutions for the “Sample Collection” objective. Accordingly, the “Sample Collection” objective may be coupled to the child objectives thereof by a control node 220 configured to implement OR logic (represented by control UIC 520-1).

[0107] Alternatively, the “Sample Collection” objective node 202 may be omitted from the optimization model 122 as illustrated in FIGS.5B and 5D. In these examples, the output of the OR control node 220 (represented by control UIC 520-1) may flow directly to the “Automated RP” AND control node 220 (represented by control UIC 520).

[0108] As illustrated in FIG.5A, the “Manual Sample Collection” objective node 202 may have no children, e.g., may comprise a “leaf” node of the optimization model 122. The metrics 205 of leaf nodes 202 may be assigned by the FM module 110 (e.g., may be assigned through the OM UIC 315, retrieved from a library 130, or the like).

[0109] In the examples illustrated in FIGS. 5A and 5B, the “Manual Sample Collection” objective may already be implemented within the facility 20. Accordingly, the metrics 205 of the “Manual Sample Collection” (represented by metrics UIC 505- BA-1494-PCT Page 32 of 621-1) may indicate a duration of 0 months, cost of 0 USD, cost savings of 0 USD, and a 100% PoS. By contrast, the “Automated Sample Collection” objective may not be implemented and, as such, may comprise non-zero implementation metrics 205, as illustrated in FIG.5A.

[0110] As disclosed herein, metrics 205 of the “Sample Collection” objective may be derived from the metrics 205 determined for the child objectives thereof, e.g., the “Manual Sample Collection” and “Automated Sample Collection” objectives. The “Sample Collection” metrics 205 may be determined in accordance with the OR logical relationship implemented by the control node 220 of the “Sample Collection” objective (represented by control UIC 520-1). FIG. 5A illustrates an example of the optimization model 122 prior to selection of a child of the “Sample Collection” objective. According to the OR control logic 225 of the “Sample Collection” control node 220 (represented by control UIC 520-1), prior to selection, the metrics 205 of the “Sample Collection” objective may be configured to span the full range of child metrics per Eq. 8-13 of Table 2 above, e.g., duration between 0 and 49 months, cost from 0 to 8000 USD, cost savings of 200 to 300 USD, PoS from 0 to 100%, and so on.

[0111] In response to selection of a child node 202, the metrics 205 of the “Sample Collection” objective (represented by 505-1) may be updated to incorporate the metrics 205 of the selected node 202 (and ignore metrics 205 of unselected nodes 202), as disclosed herein.

[0112] In some implementations, the OM UI 315 may be further configured to model selection of child nodes 202. For example, the OM UI 315 may model selection of the “Manual Sample Collection” child of the “Sample Collection” objective, as illustrated in FIGS.5C and 5D. FIG.5C illustrates an example in which the optimization model 122 comprises an objective node 202 configured to represent a “Sample Collection” objective logically disposed between the OR control UIC 520-1 and upper- level AND control UIC 520 and FIG. 5D illustrates an example in which the “Sample Collection” objective node 220 is omitted (and the output of the OR control node 220 flows directly to the AND control node 220).

[0113] Selection or activation of the “Manual Sample Collection” objective may be implemented by any suitable means, e.g., in response to user interaction with the OM UIC 315, one or more UIC 301, user selection and / or other interaction with the “Sample Collection” objective UIC 510-1, “Manual Sample Collection” objective UIC BA-1494-PCT Page 33 of 62510-1-1, or the like. The OM UI 315 may update the graphical representation of the optimization model 122 in response to the selection. The interface module 112 may be configured to modify the style and / or visual representation of UIC 301 impacted by the selection, such as link UIC 304, metrics UIC 305, node UIC 302, objective UIC 310, control UIC 320, and so on. For example, the interface module 112 may be configured to change the color and / or other aspects of the control UIC 520-1 to indicate activation of the OR control logic 225. The interface module 112 may be further configured to highlight the link UIC 504-1-1, metrics UIC 505-1-1, and / or objective UIC 510-1-1 associated with the selected “Manual Sample Collection” node 202. For example, the link UIC 504-1-1 may be displayed in an alternative color, have an increased thickness, overlay the control UIC 520-1, and / or the like. In some implementations, the interface module 112 may be configured to modify the style of UIC components 301 associated with unselected nodes 202. In the FIG.5C example, the style and / or other aspects of the link UIC 504-1-2, metrics UIC 505-1-2, and objective UIC 510-1-2 may be modified to indicate that the associated “Automated Sample Collection” node 202 is unselected (or deactivated). For example, the link UIC 504-1-2 may be removed, displayed with dashed lines, a lower thickness, or the like, the color of the objective UIC 510-1-2 may be modified, and so on.

[0114] The FM module 110 may be configured to update metrics 205 of respective nodes 202 in response to the selection. The metrics 205 may be updated in a bottom- up MA procedure. The MA procedure may comprise a bottom-up analysis of the optimization model 122 in which metrics 205 determined for lower-level nodes 202 flow up to higher level nodes 202. As disclosed herein, it can be difficult to accurately predict the metrics 205 associated with implementation of high-level, abstract objectives. For example, estimating the cost and implementation time for high-level objectives such as “Automated Plant” or “Automated RP” may be complex and error prone since such estimates must account for many different combinations of different technological solutions.

[0115] Estimation accuracy may increase, however, at lower levels of the optimization model 122. Metrics 205 determined for lower-level, less abstract objectives (and / or specific technological solutions) may be highly accurate. In fact, estimates for some lower-level objectives may be publicly available. For example, information pertaining to the cost and / or duration required for the deployment of BA-1494-PCT Page 34 of 62technical solutions for lower-level objectives, such as wireless network deployment, may be well known. Information pertaining to lower-level objectives may be derived from historical data and / or retrieved from one or more external systems, which may include but are not limited to: project management systems, enterprise resource planning (ERP) systems, enterprise project portfolio management (EPPM) systems, and / or the like. For example, known and / or previously determined metrics 205 for particular objectives or technical solutions may be maintained within a library 130, as disclosed in further detail herein.

[0116] The systems and methods for objective management disclosed herein may be configured to improve the accuracy of metrics 205 determined for higher-level objectives. The bottom-up MA procedures disclosed herein may comprise leveraging metrics 205 determined for lower-level nodes 202 to estimate metrics 205 for higher- level nodes 202. As disclosed herein, lower-level nodes 202 of the optimization model 122 represent lower-level objectives and / or technological solutions that can be accurately modeled, e.g., may be associated with known and / or previously determined implementation data. Therefore, metrics 205 determined for lower-level nodes 202 may be more accurate and less complex than estimates independently determined for higher-level nodes 202 that represent higher-level, more abstract objectives. The bottom-up MA procedures disclosed herein may comprise leveraging the more precise metrics 205 determined for lower-level nodes 202 to accurately estimate metrics for higher-level nodes 202. In other words, the accurate estimates determined for the lower-level nodes 202 may flow-up to higher-level nodes 202.

[0117] FIG. 5C further illustrates an example of a bottom-up MA procedure as disclosed herein. In the FIG. 5C example, the metrics 205 of the “Manual Sample Collection” objective (represented by metrics UIC 505-1-1 of objective UIC 510-1-1) flow up to the “Sample Collection” objective (represented by objective UIC 510-1) and, according to the OR parent-child relationship, metrics 205 of the unselected “Automated Sample Collection” objective are ignored. Since the “Manual Sample Collection” is already implemented at the facility 20, the metrics 205 of the “Sample Collection” objective are updated to indicate an implementation time of 0 months, 0 cost, 0 cost savings, and 100% chance of success. According to the bottom-up MA procedure, the updated metrics 205 of the “Sample Collection” objective may cascade up, resulting in modifications to the metrics 205 determined for the “Automated RP” BA-1494-PCT Page 35 of 62objective (represented by metrics UIC 405-2), e.g., duration between 24 and 36 months, cost from 2000 to 3000 USD, savings of 400 to 500 USD, and PoS between 35% and 63%.

[0118] Referring back to FIG. 5A, the TDM procedure disclosed herein may comprise breaking down more abstract, higher-level objectives into less abstract, lower-level objectives. The TDM procedure may comprise expanding the optimization model 122 to successively lower and lower levels. For example, the TDM procedure may comprise a plurality of phases, each phase operating at a respective level L of the optimization model 122. Implementing the TDM procedure within a level L may comprise deconstructing the nodes 202 within level L into respective sets of child nodes 202 within level L+1, each set of child nodes 202 coupled to its parent node 202 by a respective control node 220; implementing the TDM procedure within level L+1 may comprise deconstructing the nodes 202 within level L+1 into respective sets of child nodes 202 within level L+2, each set of child nodes 202 coupled to its parent node 202 by a respective control node 220; implementing the TDM procedure within level L+2 may comprise deconstructing the nodes 202 within level L+3 into respective sets of child nodes 202 within level L+4, each set of child nodes 202 coupled to its parent node 202 by a respective control node 220; and so on.

[0119] The MC module 214 may perform TDM operations on objective nodes 202 until such objective nodes 202 are disassembled into a state that can be satisfied. In other words, objective nodes 202 of the optimization module 122 may be broken down to a level of granularity such that the child objectives thereof represent TS that can be implemented within the facility 20 (e.g., concrete, real-world TS as opposed to abstract objectives). As disclosed herein, a TS may comprise and / or refer to a technology (future or existing), solution, service, product, hardware, software, deployment, installation, and / or technical functionality capable of being deployed within a facility 20. In some implementations, the optimization model 122 may be configured to model a plurality of different TS for respective, higher-level objectives. For example, it may be possible to satisfy an objective using a plurality of different technological solutions, each of which may be modeled by a respective child node 202 and / or subset of the optimization model 122. BA-1494-PCT Page 36 of 62

[0120] FIGS. 6A-6C are a schematic block diagrams illustrating examples of optimization models 122 configured to represent relationships between objectives and TS capable of being deployed within the facility 20.

[0121] FIG.6A illustrates a portion of the optimization model 122 pertaining to the “Automated Sample Collection” objective (represented by the objective UIC 510-1-2 in FIGS. 5A-5D). As shown in FIG. 6A, the “Automated Sample Collection” objective node 202 may be disassembled into lower-level child nodes 202, including a “Remote Transmission” objective node 202-1 and a “Local Automated Measurement” objective node 202-2. The “Remote Transmission” objective node 202-1 may be configured to model the implementation of remote data transmission functionality within the facility 20. The “Remote Transmission” objective may be satisfied by one of a plurality of alternative technological solutions. As disclosed herein, alternative technological solutions of a higher-level objective node 202 may be modeled by respective child nodes 202 within the optimization model 122. In the FIG.6A example, the alternative technological solutions of the “Remote Transmission” objective are modeled by a “Wireless Communication” objective node 202-1-1 and a “Wired Communication” objective node 202-1-2. The “Wireless Communication” objective node 202-1-1 may be configured to model the implementation of wireless networking functionality within the facility 20 and the “Wired Communication” objective node 202-1-2 may be configured to model the implementation of wired network infrastructure. The optimization model 122 may further comprise a control node 220-1 configured implement OR control logic 225-1 such that selection of either child objective node 202-1-1 through 202-1-2 will satisfy the parent objective.

[0122] As further illustrated in the FIG.6A example, the “Wireless Communication” objective mode 202-1-1 may comprise multiple child nodes, including a “WiFi” child node 202-1-1-1 and an “LTE” child node 202-1-1-2, each configured to model a respective alternative technological solution for implementing the “Wireless Communication” objective within the facility 20. Accordingly, the child nodes 202-1- 1-1 and 202-1-1-2 may be linked to the “Wireless Communication” objective node 202-1-1 through a control node 220 configured to implement an OR logical relationship. Since the child nodes 202-1-1-1 through 202-1-1-2 represent specific, real-world technological solutions capable of being deployed within the facility 20, the child nodes 202-1-1-1 and 202-1-1-2 (and subsequent children thereof, if any) BA-1494-PCT Page 37 of 62may comprise technological solution layer(s) of the optimization model 122. By contrast, the higher-level, abstract “Wireless Communication” objective node 202-1- 1 (and parent(s) thereof) may comprise objective layer(s) of the optimization model 122.

[0123] Metrics 205-1-1 of the “Wireless Communication” objective node 202-1-1 may be derived from metrics 205 of determined for child nodes 202-1-1-1 and 202- 1-1-2 in accordance with an OR bottom-up MA, as disclosed herein. The metrics 205- 1-1 of the “Wireless Communication” objective node 202-1-1 may be configured to span metrics 205 of the child nodes 202-1-1-1 and 202-1-1-2, e.g., may have a duration between 30 and 49 months, cost between 1770 and 2500 USD, savings between 50 and 80 USD, PoS between 5% and 39%, and so on.

[0124] In some implementations, a child node 202 may be capable of satisfying the requirements and / or dependencies of multiple parent nodes 202. For example, a TS modeled by a child node 202 may implement functionality required by multiple higher-level objective nodes 202. FIG.6B illustrates an example of a child node 202 that satisfies requirements of multiple parent nodes 202.

[0125] In the FIG.6B example, the “Wireless Communication” objective may satisfy the “Remote Transmission” objective as well as dependencies of one or more other higher-level objectives, such as the “Automated Security” objective node 202 (represented by objective UIC 410-1 in FIG. 4), which in turn may satisfy aspects of the top-level “Automated Plant” objective (represented by objective UIC 310 in FIGS. 3 and 4).

[0126] In some implementations, the optimization model 122 may be configured to represent child nodes 202 that satisfy requirements of a plurality of parent nodes 202, e.g., may be configured to model N to 1 parent-child relationships, wherein a child node 202 may satisfy dependencies of each of N different parent nodes 202 within the optimization model 122. In the FIG 6B example, the optimization model 122 may couple the child “Wireless Communication” objective node 202-1-1 to the parent “Remote Transmission” objective node 202-1 via link 202-1-1A (through OR control node 220-1) and the parent “Automated Security” objective node 202 via link 204-1- 1B (through one or more intervening nodes 202 and / or links 204, not shown in FIG. 6B to avoid obscuring details of the illustrated examples). BA-1494-PCT Page 38 of 62

[0127] The MA module 216 may be configured to adjust the calculation of metrics 205 for upper-level nodes 202 based, at least in part, on multi-parent relationships of the optimization model 122. Prior to selection of a particular child technical solution, the metrics 205-1-1 of the “Wireless Communication” objective node 202-1-1 may be configured to span the metrics 205 assigned to child nodes 202-1-1-1 through 202-1- 1-E, e.g., per the OR metrics relationship, as disclosed herein. The metrics 205-1-1 may flow to parent nodes 202 per the disclosed bottom-up MA procedure, e.g., may be incorporated into the metrics 205-1 of the “Remote Transmission” objective node 202-1 as well as the metrics 205 of the “Automated Security” objective node 202.

[0128] The metrics 205-1-1 of the “Wireless Communication” objective node 202- 1-1 may be updated in response to selection or activation of a candidate TS (e.g., selection or activation of one of the child nodes 202-1-1-1 through 202-1-1-E). Additional utilizations of child nodes 202 may reduce the overhead of higher-level objectives (and / or the task as a whole). For example, the cost metrics 205 attributed to implementation of the “Wireless Communication” objective may remain constant regardless of the number of parent nodes 202 satisfied thereby. Other metrics 205 such as duration, cost savings, and PoS may flow up to multiple parents, as disclosed herein, e.g., cost savings metrics 205 may be realized by each parent node 202 whereas capital costs 205 may be attributed to a single parent node 202 (or spread across multiple parent nodes 202).

[0129] In the FIG.6C example, the “LTE” node 202-1-1-2 may be selected instead of the “WiFi” node 202-1-1-1. In response to the selection, the MA module 216 may update the metrics 205-1-1 of the “Wireless Communication” node 202-1-1 in accordance with the OR bottom-up MA procedure, e.g., incorporate the “LTE” metrics 205-1-1-2 (as opposed to the range spanned by the metrics 205-1-1-1 and 205-1-1- 2). The MA module 216 may be further configured to adapt metrics 205 of the parent nodes 202 in accordance with a multi-parent metrics relationship.

[0130] The multi-parent metrics relationship may be configured to accurately attribute metrics 205 of child nodes 202 having multiple parents 202. For example, multi-parent metrics relationships may be configured to attribute duration, cost, cost savings, PoS, safety risk, and / or other metrics 205 between multiple parent nodes 202. By way of non-limiting example, multi-parent metrics relationships may derive BA-1494-PCT Page 39 of 62metrics 205 for respective parent nodes (n) of a child node 202 having N parents per Eq.14-18 of Table 3 below: Multi-Parent Metrics Relationships Metric Metrics Relationship 4567 8, p p hild node 202 may incorporate the duration, PoS, and safety risk metrics 205 of the child node 202, e.g., per Eq.14, 17, and 18. Other metrics 205, such as cost and cost savings, however, may be distributed between parent nodes 202 to avoid duplication of such metrics 205 within the optimization model 122. For example, economic metrics 205 such as cost and cost savings may be distributed evenly between parent nodes 202 per Eq. 15 and 16. Alternatively, economic metrics 205 may be distributed in other ways, e.g., may be assigned to a single parent node 202 (and may be ignored within other parent nodes 202). In the FIG.6C example, the metrics 205-1-1-1A attributed to the “Remote Transmission” objective due to selection of the “LTE” TS by the “Wireless Communication” objective node 202-1-1 (as well as the metrics 205-1-1B attributed to the “Automated Security” objective) may reflect the full duration and PoS metrics 205 of the “LTE” TS per Eq.14 and 17, but may only incorporate 1 / N of the economic metrics 205 per Eq.15 and 16 (where N represents the number of parent nodes 202 that share the “LTE” TS, which is two in the FIG.6C example).

[0132] In some implementations, the FM module 110 may be configured to identify redundancies within the optimization model 122. For example, the “Automated Security” objective node 202 may comprise a separate, redundant child node 202 configured to represent “Wireless Communication” functionality (not shown in FIGS. 6A-6C to avoid obscuring details of the illustrated examples). For example, the “Wireless Communication” objective node 202-1-1 illustrated in FIGS.6A-6C may be created through implementation of a TDM procedure on and / or under the BA-1494-PCT Page 40 of 62“Automated Sample Collection” objective node 202 (and / or higher-level “Automated RP” or “Automated Support” objective nodes 202) and a separate, redundant “Wireless Communication” objective node 202 may be created in a TDM procedure implemented on (or under) the “Automated Security” objective node 202.

[0133] In some implementations, the FM module 110 may be configured to identify potential redundancies during construction of the optimization model 122. For example, the FM module 110 may search the optimization model 122 for nodes 202 capable of satisfying respective requirements of an objective before instantiating nodes 202 corresponding to the respective requirements. For example, the TDM procedure implemented on or under the “Automated Security” objective node 202 may comprise identifying a “Wireless Communication” requirement. Rather than instantiating an additional “Wireless Communication” node 202, the FM module 110 may search the optimization model 122 for an equivalent objective node 202 and, in response, reference the equivalent objective node 202 rather than instantiating another “Wireless Communication” node 202. As used herein, an “equivalent” node 202 may comprise and / or refer to a node 202 configured to represent functionality that is the same, similar, and / or compatible with the functionality of another node 202. In other words, equivalent nodes 202 may refer to nodes 202 capable of satisfying the same or similar objectives, requirements, and / or dependencies.

[0134] In some embodiments, the FM module 110 may be configured to implement a redundancy analysis procedure configured to, inter alia, identify redundancies within the optimization model 122. Aspects of the redundancy analysis procedure may be implemented after construction of the optimization model 122, e.g., as opposed to attempting to identify redundancies during construction of the data structure 201. The redundancy analysis procedure may comprise identifying redundant nodes 202. Redundant nodes 202 may comprise and / or refer to equivalent nodes 202, as disclosed herein (e.g., nodes 202 configured to represent equivalent objectives and / or technological solutions, such as “Wireless Communication”). The redundancy analysis procedure may further comprise replacing redundant nodes 202 with multi-parent connections 204 and / or revised metrics 205, as illustrated in FIGS.6B and 6C.

[0135] As further illustrated in FIGS. 6A-6C, the “Local Automated Measurement” objective may be satisfied by one of a plurality of child objective nodes 202, including BA-1494-PCT Page 41 of 62a “Sensors” objective node 202-2-1, a “Drones with Sensors” objective node 202-2-2, and “Handheld Detectors” objective node 202-2-3, which may be coupled to the “Local Automated Measurement” objective node 202-2 by a control node 220-2 configured to implement OR control logic 225-2.

[0136] In some implementations, an objective node 202 may represent an objective that is complex and / or has a large scope. It may not be efficient (or even feasible) to implement such objectives as a whole, e.g., in one project or milestone. The optimization model 122 may be configured to disassemble selected objectives into objectives (or solutions) having multiple phases. The phases may be configured to discretize completion states of the parent objective (instead of tracking percent completion of the parent objective itself).

[0137] FIG. 7 is a schematic block diagram illustrating an example of an optimization model 122 configured to break down an objective (or TS) into a plurality of child nodes 202, each child node 202 representing a respective milestone or phase of the parent objective. In the FIG. 7 example, the “Sensors” objective node 202-2-1 may require deployment of sensor devices throughout the facility 20. The sensor deployment may involve procurement, installation, and / or configuration many sensor devices and / or different types of sensor devices. Accordingly, it may not be efficient (or feasible) to implement the “Sensors” objective within the scope of single project. As illustrated in the FIG. 7 example, the “Sensor” objective may be broken down into objective nodes 202 configured to model respective implementation phases, including a “Sensors Phase 1” objective node 202-2-1-1 and a “Sensors Phase 2” objective node 202-2-1-2 having respective metrics 205-2-1-1 and 205-2-1-2. In the FIG. 7 example, both phases can be deployed in parallel. The disclosure is not limited in this regard, however. For example, if implementation of “Sensors Phase 2” objective were required to begin after completion of the “Sensors Phase 1” objective, the duration of the “Sensors Phase 2” metrics 205-2-1-2 could be offset accordingly, e.g., set to 12-20 rather than 6-12.

[0138] As disclosed herein, the optimization model 122 may be configured to analyze the impact of more specific TS, such as alternative implementations of the “Wireless Communication” objective node 202-1-1 illustrated in FIGS.6A-6C. In the FIG.8 example, the “Wireless Communication” objective node 202-1-1 may be further broken down into specific TS for satisfying the “Wireless Communication” objective, BA-1494-PCT Page 42 of 62including a “WiFi” objective node 202-1-1-1 representing a “WiFi” technological solution and a Long-Term Evolution “LTE” objective node 202-1-1-2 representing a wireless cellular network technological solution. The alternative TS may have respective metrics 205-1-1-1 and 205-1-1-2, which may be combined according to OR control logic 225-1-1 of control node 220-1-1. Although FIG.8 illustrates particular examples of alternative TS for satisfying the “Wireless Communication” objective, the disclosure is not limited in this regard. The optimization model 122 could be configured to include any number of alternative “Wireless Communication” implementations based on any suitable wireless communication technology.

[0139] The “WiFi” and “LTE” objective nodes 202-1-1-1 and 202-1-1-2 may represent deployable TS, e.g., may correspond to a technological solution portion of the optimization model 122. The objective model 122 may be further configured to model deployment of TS within the facility 20. In other words, the MC module 214 may be configured to implement TDM operations on TS nodes 202, the TDM operations configured to break down the TDM into lower-level deployment models 822. As used herein, a deployment model 822 may comprise and / or refer to data configured to model the real-world deployment and / or implementation of a TS within a target environment, e.g., the facility 20. A deployment model 822 may, for example, comprise and / or represent aspects of an implementation plan or scheme. A deployment model 822 may be embodied by the optimization model 122, e.g., may comprise subsection or subtree within the optimization model 122. Alternatively, or in addition, aspects of a deployment model 822 may be embodied by and / or within a separate data structure.

[0140] In the FIG. 8 example, the optimization model 122 may comprise and / or reference deployment models 822 for the “WiFi” and “LTE” technological solutions, e.g., may comprise a “WiFi” deployment model 822-1 and “LTE” deployment model 822-2.

[0141] FIG.9 is a schematic block diagram illustrating an example of a deployment model 822. The deployment model 822 of the FIG. 9 example may be configured to model implementation of the “WiFi” TS node 202-1-1-1 of FIG.8 within the facility 20 and, as such, may be referred to as a “WiFi” deployment model 822-1.

[0142] Deployment models 822 may comprise a collection of interconnected nodes 202, as disclosed herein. The nodes 202 may be arraigned in hierarchical parent-child BA-1494-PCT Page 43 of 62relationships, e.g., higher-level parent nodes 202 may be coupled lower-level child nodes 202. A deployment model 822 may further comprise control nodes 220, which may comprise control logic 225 configured to define and / or control parent-child relationships, including logical relationships, metrics relationships, and so on, as disclosed herein.

[0143] The deployment model 822 of a technological solution may comprise, inter alia, activity nodes 902 and activity objective nodes 910, which may be configured to model and / or represent activities and activity objectives of the technological solution. As used herein, an activity objective of a deployment model 822 may comprise and / or refer to any suitable objective pertaining to the deployment and / or implementation of the technological solution of the deployment model 822 including, but not limited to: planning, design (e.g., design review, evaluation, and / or modification), logistics, resource management, resource procurement (e.g., procuring hardware resources, software resources, or the like), resource installation, resource deployment, resource configuration, training, and / or the like. As used herein, an activity may comprise and / or refer to any suitable action pertaining to implementation of an activity objective.

[0144] Activity nodes 902 may be broken down into lower-level nodes 202, such as lower-level activity nodes 902, resource nodes 1002 (disclosed in further detail in conjunction with FIG.10), and so on. Activity nodes 902 may be further configured to define metrics 205 associated with execution of the activities modeled thereby, e.g., may comprise metrics operators. The metrics 205 of an activity node 902 may comprise estimates for duration, cost, PoS, and so on. The metrics 205 of activity nodes 902 may, however, omit cost savings estimates because cost-savings (if any) may not be realized until the corresponding technological solution is deployed. The metrics 205 determined for lower-level nodes 202 of a deployment model 822 may cascade to higher-level nodes 202, as disclosed herein.

[0145] The “WiFi” deployment model 822-1 of the optimization model 122 illustrated in the FIG.9 example may comprise activity nodes 902 configured to model implementation of any suitable activity pertaining to the deployment of WiFi communication functionality within the facility 20 including, but not limited to: the development of design requirements ( “Design Requirement” activity node 902), creation of a project plan for the WiFi deployment (“Create Project Plan” activity node BA-1494-PCT Page 44 of 62902), review of electromagnetic interference (EMI) and / or radio frequency interference (RFI) (“Review EMI / RFI” activity node 902), cyber security planning (“Create Cybersecurity Plan”) activity node 902), evaluate and modify the deployment design (“Design Modification” activity node 902), procure resources required for the WiFi deployment (“Procurement” activity node 902), install the procured resources (“Install” activity node 902), and so on. Logical and metrics relationships between the activity nodes 902 (and / or corresponding activity objective nodes 910) may be defined by parent-child connections 204 between nodes 202 and / or control nodes 220, as disclosed herein.

[0146] Activity nodes 902 may be broken down into lower-level child activities and / or resources. As used herein, a “resource” may comprise and / or refer to any suitable means for satisfying an objective and / or requirement thereof including, but not limited to hardware resources, software resources, human resources, and / or the like.

[0147] FIG, 10 is a schematic block diagram illustrating a portion of a deployment model 822 generated in response to implementation of a TDM operation on the “Design Modification” activity node 902 of the FIG. 9 example. The “Design Modification” activity may be broken down into the procurement of human resources and engineering resources (e.g., engineering tools and / or data). The human resource requirements of the “Design Modification” activity may be modeled by a “People Ready” activity objective node 910. The “People Ready” activity objective may be satisfied by either a “Projects Staff” staff resource (e.g., internal personnel) or “Engineering Contractor” resource (e.g., third-party personnel), which may be represented by respective resource nodes 1002 having respective metrics 205. The OR parent-child relationship between the “People Ready” activity objective and the “Projects Staff” and “Engineering Contractor” resource nodes 1002 may be defined by an OR control node 220, as disclosed herein.

[0148] In the FIG.10 example, the engineering resources required by the “Design Modification” activity may be modeled by a “Tools and Data Ready” activity objective node 910. Satisfying the “Tools and Data Ready” activity objective may require acquisition of “Engineering Tools” and “Engineering Data” resources, which may be represented by respective resource nodes 1002 having respective metrics 205. The AND parent-child relationship between the “Tools and Data Ready” activity objective BA-1494-PCT Page 45 of 62and the corresponding child resource nodes 1002 may be defined by an AND control node 220, as disclosed herein.

[0149] Metrics 205 determined for the resources required by the “Design Modification” activity may cascade up from lower-level nodes 202, e.g., resource nodes 1002 to higher-level nodes, such as activity objective nodes 910 and / or activity nodes 902. The metrics 205 may flow up to higher levels of the optimization model 122 in accordance with control logic 225 of the control nodes 220.

[0150] Referring back to FIG. 1, the FM module 110 may further comprise an optimization module 116. The optimization module 116 may be configured to determine an “optimal” implementation scheme 150 for an optimization model 122. As used herein, an “optimal” implementation scheme 150 may refer to an implementation scheme 150 selected according to a determined optimization criterion and / or constraints, such as cost, cost savings, implementation time, PoS, and / or the like. For example, a user 15 may specify a cost constraint indicating an available budget for the implementation scheme 150, a time constraint indicating a maximum implementation time for the implementation scheme 150 and / or the like. Alternatively, or in addition, the user 15 may specify optimization criteria and / or weights, e.g., indicate relative weights to be assigned to respective metrics 205, e.g., assign weights to duration, cost, cost savings, probability success, safety risk, and / or other metrics 205. Aspects of the optimization criteria and / or constraints may be specified the user 15, e.g., may be included in input data 111, specified through the user interface 115 and / or the like.

[0151] The optimization module 116 may be configured to leverage the data structure 201 of the optimization model 122. The optimization module 116 may utilize the optimization model 122 to, inter alia, evaluate potential implementation schemes 150. An implementation scheme 150 may comprise a subset of the optimization model 122, the subset corresponding to selection of respective OR branches thereof (e.g., selection of respective technological solutions from sets of alternative technological solutions for respective objectives). In other words, an implementation scheme 150 may comprise a selection of activation of a set of nodes 202 within the optimization model 122 that satisfy requirements of respective high- level objective nodes 202. An implementation scheme 150 may, for example, comprise an optimization model 122 in which a selection is determined for each BA-1494-PCT Page 46 of 62possible alternative, e.g., each control node 220 configured to implement OR control logic 225 (or other, similar control logic 225). An implementation scheme 150 may further comprise deployment models 120 for selected TS. An implementation scheme 150 may, therefore, comprise an FMS as disclosed herein, e.g., may comprise a facility modification strategy for achieving a specified set of objectives at the facility 20.

[0152] The optimization module 116 may be configured to evaluate potential implementation schemes 150. Evaluating an implementation scheme 150 may comprise determining metrics 205 for respective objectives (objective nodes 202) of the implementation scheme 150 through implementation of a bottom-up MA procedure, as disclosed herein. Metrics 205 of the top-level objective node 202 of the optimization model 122 may be derived from metrics determined for lower-level objectives and, as such, may objectively quantify economic, technological, and / or risk characteristics for implementation of the specified objectives according to a selected set of TS.

[0153] The optimization module 116 may be configured to implement any suitable optimization algorithm or technique including, but not limited to: a genetic algorithm, a Monte Carlo approach (e.g., activate various combinations of different nodes 202 to evaluate the impact of such activation on local and / or global objectives), and so on. The optimization algorithm may be guided by any suitable optimization means, such as gradient descent, stochastic gradient descent (SGD), and / or the like. Alternatively, or in addition, the FM module 110 may comprise and / or be coupled to an AI / ML platform configured to implement machine-learned optimization techniques.

[0154] The implementation scheme 150 determined by the optimization module 116 may comprise an optimal FMS. The implementation scheme 150 (and optimal FMS) may be provided to the user 15 as computer-readable data or one another suitable form, e.g., as an implementation plan, human-readable data such as a document, and / or the like. Alternatively, or in addition, the implementation scheme 150 may be provided and / or maintained as an optimization model 122 (e.g., data structure 201) configured to be displayed and / or manipulated by use of user interfaces 115 of the FM module 110, such as the OM UI 315 disclosed herein.

[0155] In some implementations, the FM module 110 may be further configured to implement other type of analysis on the optimization model 122 and / or implementation scheme 150. For example, the FM module 110 may be configured to BA-1494-PCT Page 47 of 62implement a sensitivity analysis configured to, inter alia, quantify sensitivity of the resulting metrics 205 to specified factors, such as the cost and / or availability of specified technological solutions, resources, activities, and / or the like.

[0156] FIG. 11 is a flow diagram illustrating an example of a method 1100 for managing implementation of a set of one or more interrelated objectives pertaining to a facility 20, as disclosed herein. Step 1110 may comprise constructing an optimization model 122. The optimization model 122 may be constructed to include high-level objective nodes 202 configured to represent respective objectives of the set of one or more interrelated objectives, as disclosed herein. Step 1110 may further comprise implementation of TDM procedure(s) to break down higher-level objective nodes 202 into lower-level objective nodes 202. The TDM procedure may continue to a TS level of the optimization model 122, e.g., may continue until child nodes 202 represent TS as opposed to abstract objectives. In some implementations, aspects of the TDM procedure may be implemented by use of a library 130 maintained on DSR resources of the computing device 102. The library 130 may comprise generalized information that can be customized for import into the optimization model 122, including but not limited to: an objective library 230 comprising generalized information pertaining to objectives suitable for implementation within the facility 20, a TS library 232 comprising generalized information pertaining to TS for respective objectives, a deployment library 234 comprise generalized information pertaining to deployment models 822 for respective TS, and so on.

[0157] Constructing the optimization model 122 may further comprise defining TS for respective objectives. Aspects of the TS may be retrieved from the library 130 as disclosed herein. Step 1110 may further include implementing TDM procedures on higher-level TS (e.g., generic technological solutions), which may comprise creating nodes 202 configured to represent alternative technological solutions and / or specific technological solutions, e.g., specific technological solutions for a higher-level “Wireless Communication” objective, such as a “WiFi” technological solution, “LTE” technological solution, and / or the like, as disclosed herein.

[0158] Step 1110 may further comprise identifying redundancies within the optimization model 122 and / or replacing redundant nodes 202 with multi-parent relationships, as disclosed herein. BA-1494-PCT Page 48 of 62

[0159] In some implementations, step 1110 may further comprise implementing TDM procedures to, inter alia, generate deployment models 822 for respective TS nodes 202, as disclosed herein.

[0160] Step 1120 may comprise determining an optimal implementation scheme 150. Step 1120 may comprise receiving optimization criteria and / or constraints, formulating an optimization problem, and determining a solution to the optimization problem in accordance with an optimization algorithm, as disclosed herein. The implementation scheme 150 determined at 1120 may comprise a subset of the optimization model 120, e.g., may comprise selection or activation of specified TS. The implementation scheme 150 may further comprise deployment models 822 for selected TS. The implementation scheme 150 may, therefore, comprise an FMS suitable to manage implementation of the set of one or more interrelated objectives within the facility 20.

[0161] Step 1130 may comprise providing the implementation scheme 150 and / or data corresponding to the optimal implementation scheme 150 to a user 15, as disclosed herein.

[0162] FIG. 12 is a flow diagram illustrating an example of a method 1200 for constructing an optimization model 122, as disclosed herein. Step 1210 may comprise generating high-level objective nodes 202, step 1220 may comprise execution of TDM procedure(s) on respective objective nodes 202, step 1230 may comprise executing TDM operations on lower-level objective nodes 202 (e.g., TS nodes 202 configured to model respective technological solutions, as disclosed herein), and step 1240 may comprise implementation of bottom-up analysis procedure(s) on the resulting optimization model 122. The bottom-up analysis procedure(s) may comprise determining metrics 205 for lower-level nodes 202 and propagating the lower-level metrics 205 to higher-level nodes 202, as disclosed herein, e.g., per control logic 225 defining respective parent-child relationships within the optimization model 122. The bottom-up analysis procedure may be implemented at the deployment level (within respective deployment models 822) and proceed to higher-levels of the data structure 201, as disclosed herein.

[0163] FIG. 13 is a flow diagram illustrating an example of a method 1300 for implementing a TDM procedure on a node 202 of an optimization model 122. Step 1310 may comprise disassembling the objective node 202 in to one or more child BA-1494-PCT Page 49 of 62nodes 202, each child node 202 configured to represent a respective dependency and / or requirement of the objective node 202. Step 1320 may comprise determining whether the disassembly results in one or more child objective nodes. If so, the flow may continue at 1330; otherwise, the flow may continue at 1360.

[0164] Step 1350 may comprise instantiating the child objective nodes 202 within the optimization model 122. Step 1340 may comprise determining a parent-child relationship, e.g., an AND, OR, or other type of control logic 225 and instantiating a corresponding control node 220. Step 1350 may comprise executing TDM procedures on respective child objective nodes 202 of the child objective nodes 202 instantiated at 1330.

[0165] Step 1360 may comprise instantiating a node 202 configured to represent a TS of the parent objective, as disclosed herein. Step 1370 may comprise implementing TDM procedures on the resulting TS nodes 202, as disclosed herein.

[0166] FIG.14 is a flow diagram illustrating an example of a method 1400 for a SAM procedure on a node 202 configured to represent a technological solution of an objective (e.g., a solution node 202), as disclosed herein. Step 1410 may comprise expanding the solution node 202 into a set of specific technological solutions, as disclosed herein. Step 1420 may comprise instantiating child solution nodes 202 configured to represent respective specific technological solutions. Step 1430 may comprise coupling the child solution nodes 202 to the parent solution node 202 by a control node 220 configured to implement OR control logic 225. Step 1450 may comprise identifying dependencies of respective solutions and step 1450 may comprise executing the SAM procedure on respective child solution nodes 202. Execution of the SAM procedure on a node 202 configured to represent a specific technological solution may comprise generating a deployment model 822 for the specific technological solution, as disclosed herein. The specific technological solutions and / or corresponding deployment models 822 may be retrieved from a library 130, as disclosed herein.

[0167] FIG. 15 is a flow diagram illustrating an example of a method 1500 for modeling deployment of a specific technological solution. Step 1510 may comprise determining activities and / or resources required to implement the specific technological solution. Step 1510 may comprise retrieving aspects of a deployment model 822 from a library 130 maintained on and / or within a data store 106 of the FM BA-1494-PCT Page 50 of 62module 110. Step 1510 may comprise identifying the activities, activity objectives, and / or resources required to deploy the specific technological solution.

[0168] Step 1520 may comprise instantiating nodes 202 configured to represent the activities, activity objectives, and / or resources identified at 1520 within the optimization model 122 as disclosed herein. Step 1520 may further comprise specifying corresponding metrics 205 for the deployment and / or propagating the metrics 205 to higher levels of the optimization model 122, as disclosed herein.

[0169] FIG. 16 is a flow diagram illustrating an example of a method 1600 for determining an optimal implementation scheme for one or more target objectives. Step 1610 may comprise formulating an optimization problem for the objectives. Step 1610 may comprise constructing an optimization model 122 comprising high-level nodes 202 configured to represent the target objectives, expanding the optimization model 122 through implementation of one or more TDM procedures, modeling technological solutions for lower-level objectives, and defining alternative, specific technological solutions for higher-level generic technological solutions (e.g., deployment models 822), as disclosed herein. Step 1610 may further comprise assigning metrics 205 to lower-level nodes 202 and propagating the lower-level metrics 205 to higher-level nodes 202 in a bottom-up MA procedure(ss), as disclosed herein.

[0170] Step 1610 may also include determining optimization criteria and / or constraints for the optimization problem, as disclosed herein.

[0171] Step 1620 may comprise determining potential implementation scheme(s) 150, each potential implementation scheme 150 corresponding to activation of a respective subset of the optimization model 122. Step 1620 may comprise iteratively refining and / or guiding potential implementation scheme(s) 150 in accordance with an optimization algorithm, as disclosed herein.

[0172] Step 1630 may comprise evaluating the potential implementation schemes. The evaluating may comprise propagating metrics 205 from lower-levels of the optimization model 122 to upper-levels in accordance with selection and / or activation of respective nodes 202, as disclosed herein.

[0173] Step 1640 may comprise determining whether to terminate the iterative evaluation of potential implementation schemes 150. The determination of 1640 may BA-1494-PCT Page 51 of 62be based on the optimization algorithm and / or optimization criteria of the optimization problem formulated at 1610.

[0174] Step 1650 may comprise outputting the optimal implementation scheme 150 (and / or data defining the optimal implementation scheme 150). The optimal implementation scheme 150 may be selected from the potential implementation schemes 150 evaluated at 1620-1640.

[0175] FIG. 17 is a flow diagram illustrating an example of a method 1700 for objective management. Step 1710 may comprise constructing an optimization model 122 for a set of one or more interrelated objectives pertaining to a facility 20, as disclosed herein. Step 1710 may comprise initializing the optimization model 122, which may comprise constructing top-level objective nodes 202 configured to represent respective top-level objectives. Step 1710 may further comprise performing TDM operations within objective layers of the optimization model 122, each TDM operation configured to break down a higher-level objective node 202 into a respective set of lower-level objective nodes 202. Step 1710 may further comprise constructing TS layer(s) of the optimization model 122, which may comprise breaking down upper-level objective nodes 202 into TS nodes 202 configured to represent technological solutions for the upper-level objectives. The TS layer(s) may model a plurality of alternative TS for respective objectives, as disclosed herein. Step 1710 may further comprise identifying redundancies within the optimization model 122 and / or replacing identified redundancies with multi-parent relationships, as disclosed herein.

[0176] Aspects of the optimization model 122 may be constructed based on OM data 220, as disclosed herein. Step 1710 may comprise acquiring OM data 220 one or more users 15 (e.g., may comprise acquiring OM data 220 through user interaction(s) with a UI 115). Alternatively, or in addition, step 1710 may comprise acquiring OM data 220 from a library 130. Step 1710 may comprise retrieving generalized information pertaining to objectives suitable for implementation at the facility 20 from an objective library 230, retrieving generalized information pertaining to TS for respective objectives from a TS library 232, retrieving generalized information pertaining to deployment models 822 for respective TS from a deployment library 234, and so on. BA-1494-PCT Page 52 of 62

[0177] Step 1710 may further comprise assigning metrics 205 to leaf nodes of the optimization model 205. As disclosed herein, a leaf node 202 may refer to a node 202 having no children. In some implementations, metrics 205 may be assigned to leaf nodes 202 by users 15 (e.g., through a UI 115, such as an OM UI 315 or the like). Alternatively, metrics 205 for leaf nodes 202 may be based on generalized information retrieved from the library 130 and / or information pertaining to the facility 20. For example, duration metrics 205 may be based on historical information maintained within the library 130 and / or capabilities of the facility 20 (as indicated by the facility DS 120). Cost metrics 205 for respective TS (and / or deployment models 822 of such TS) may be retrieved from the library 130 and / or refined by users 15. Cost savings metrics 205 may be based on cost estimates determined for candidate FMS and / or costs associated with existing processes of the facility 20 (e.g., may comprise a difference between current operating costs incurred by the facility 20 and projected operating costs utilizing selected TS). Risks associated with deployment of respective TS may be based on an organizational readiness level (ORL) of the facility 20 and / or human readiness level (HRL) of facility personnel recorded in the facility 20.

[0178] Constructing the optimization model 122 at 1710 may further comprise determining metrics for non-leaf nodes based on, inter alia, metrics 205 determined for the child nodes 202 thereof. The metrics 205 of non-leaf nodes of the optimization model 122 may be determined through bottom-up metrics analysis operations, as disclosed herein. Metrics 205 of parent nodes 205 may be derived from the metrics 205 determined for the child nodes 202 thereof based on the logical relationship between the parent node 202 and child nodes 202 (e.g., the MA module 216 may be configured to derive metrics relationships from logical relationships recorded within the optimization model 122).

[0179] The optimization model 122 constructed at 1710 may be configured to model a plurality of different candidate FMS, each FMS comprising selection of a respective set of candidate TS and / or deployment models 822 for implementing the set of one or more interrelated objectives within the facility 20. In other words, each candidate FMS may comprise selection and / or activation of a respective subset of the optimization model 122. Each candidate FMS may, therefore, comprise respective metrics 205 based on the subset of nodes 202 selected therein. BA-1494-PCT Page 53 of 62

[0180] In some implementations, step 1710 may further comprise recording the optimization model 122 within the facility DS 120 and / or other NTS resources 104- 3.

[0181] Step 1720 may comprise providing graphical user interface(s) configured to display information pertaining to respective candidate FMS of the optimization model 122. Step 1720 may comprise providing graphical user interface(s) configured to enable users 15 to select and / or activate different alternative TS nodes 202. In response to selection of a particular set of TS nodes 202, the MA module 216 may update the metrics 205 determined for respective nodes 202 (per bottom-up metrics analysis operations) and display the updated metrics 205 within the graphical user interface. Users 15 may, therefore, visualize the impact of different TS (and / or different combinations of TS) on duration, cost, cost savings, PoS, safety risk, and / or other metrics 205.

[0182] Step 1730 may comprise outputting an implementation scheme 150 in response to user interaction with the graphical user interface(s) of step 1720. Step 1730 may comprise determining an optimal implementation scheme 150 (e.g., an optimal FMS) as disclosed herein. Alternatively, or in addition, step 1730 may comprise outputting the implementation scheme 150 in response to user selection of an FMS of the optimization model 122 (e.g., through selection or activation of respective TS and / or deployment models 822).

[0183] FIG.18 is a flow diagram of another example of a method 1800 for objective management, as disclosed herein. Steps 1810 through 1830 may comprise constructing an optimization model 122 for a set of one or more interrelated objectives, determining an optimal implementation scheme 150 (e.g., optimal FMS) based on the optimization model 122, and generating implementation scheme data, as disclosed herein.

[0184] Step 1840 may comprise updating the optimization model 122. The updating may comprise updating information pertaining to one or more TS nodes 202 of the optimization model 122. For example, the optimization model 122 may be updated to reflect changes to one or more technological solutions and / or corresponding deployment models 822, which may include, but are not limited to changes to: duration metrics 205 of the TS and / or deployment model 822 (e.g., changes due to improvements to the TS, improved competence and / or capabilities of BA-1494-PCT Page 54 of 62the facility 20 and so on), cost metrics 205 of the TS and / or deployment model 822 (e.g., changes in costs associated with resources and / or activities involved in the deployment of the TS), cost savings metrics 205, PoS metrics 205 (e.g., due to changes in the ORL and / or HRL of the facility 20, readiness of the TS, and / or the like), risk metrics (e.g., due to changes in ORL, HRL, technology readiness, and / or the like), and so on.

[0185] Step 1840 may further comprise updating metrics 205 of upper-level nodes 202 (and / or candidate FMS) in response to updating metrics 205 of one or more lower-level TS nodes 202 and / or deployment models 822. As disclosed herein, metrics 205 determined for lower-level TS nodes 202 and / or deployment models 822 may flow up to higher-level objective nodes 202 through, inter alia, bottom-up metrics analysis operations. Accordingly, updating metrics 205 assigned to TS nodes 202 and / or deployment models 822 may result in changes to upper-level objective nodes 202 and / or candidate FMS.

[0186] Step 1850 may comprise determining an optimal implementation scheme 150 (and / or an optimal FMS) based on the updated optimization model 122 of step 1840. Step 1850 may comprise reevaluating candidate FMS based on the updated metrics 205 determined at 1840. Accordingly, step 1850 may comprise selecting an optimal FMS different from the optimal FMS selected at 1820.

[0187] Step 1860 may comprise outputting updated implementation scheme data. Step 1860 may comprise identifying the optimal implementation scheme 150 (and / or optimal FMS) determined at 1850. In some implementations, step 1860 may comprise modifying the optimization scheme data output at 1830 in accordance with the updated implementation scheme data of 1860, e.g., may comprise replacing one or more TS of the implementation scheme data output at 1830 with TS of the updated implementation scheme data.

[0188] In some implementations, portions of the implementation scheme output at 1830 may be partially implemented at the facility 20. Information pertaining to deployment of the implementation scheme at the facility 20 may be recorded within an implementation model 132 maintained within the facility DS 120. In these circumstances, step 1860 may comprise modifying portions of the implementation model 132 that have not yet been completed. In other words, step 1860 may comprise modifying the implementation scheme 150 during deployment thereof within the BA-1494-PCT Page 55 of 62facility 20, e.g., may comprise modifying the implementation scheme 150 in response to changing conditions as determined at steps 1840 and 1850.

[0189] This disclosure has been made with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope of the present disclosure. For example, various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system, e.g., one or more of the steps may be deleted, modified, or combined with other steps.

[0190] Additionally, as will be appreciated by one of ordinary skill in the art, principles of the present disclosure may be reflected in a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium. Any tangible, non-transitory computer- readable storage medium may be utilized, including magnetic storage devices (hard disks, floppy disks, and the like), optical storage devices (CD-ROMs, DVDs, Blu-Ray discs, and the like), flash memory, and / or the like. These computer program instructions may be loaded onto a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer- readable memory produce an article of manufacture, including implementing means that implement the function specified. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified.

[0191] While the principles of this disclosure have been shown in various embodiments, many modifications of structure, arrangements, proportions, elements, BA-1494-PCT Page 56 of 62materials, and components, which are particularly adapted for a specific environment and operating requirements, may be used without departing from the principles and scope of this disclosure. These and other changes or modifications are intended to be included within the scope of the present disclosure.

[0192] The foregoing specification has been described with reference to various embodiments. However, one of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the scope of the present disclosure. Accordingly, this disclosure is to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope thereof. Likewise, benefits, other advantages, and solutions to problems have been described above with regard to various embodiments. However, benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, a required, or an essential feature or element. As used herein, the terms “comprises,” “comprising,” and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, a method, an article, or an apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, system, article, or apparatus. Also, as used herein, the terms “coupled,” “coupling,” and any other variation thereof are intended to cover a physical connection, an electrical connection, a magnetic connection, an optical connection, a communicative connection, a functional connection, and / or any other connection.

[0193] Those having skill in the art will appreciate that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the claims.

[0194] We claim: BA-1494-PCT Page 57 of 62

Claims

CLAIMS 1. A method for managing implementation of a modernization task to implement a high-level objective at a facility, comprising: initializing a hierarchical optimization model configured to represent the modernization task, the optimization model comprising a top-level node configured to represent the high-level objective; constructing lower levels of the optimization model through top-down modeling operations involving higher-level nodes of the optimization model, wherein a top-down modeling operation involving a higher-level node comprises: generating child nodes of the higher-level node within a lower level of the optimization model, the child nodes configured to represent respective requirements of the higher-level objective node, and linking the child nodes to the higher-level node with a control node of the optimization model, the control node defining a logical relationship between the higher-level node and the child nodes; determining metrics for respective nodes of the optimization model through one or more bottom-up metrics analysis procedures, wherein metrics of higher-level nodes of the optimization model are derived from metrics determined for lower-level child nodes, and wherein a bottom-up metrics analysis procedure to determine metrics for a higher-level node having lower-level child nodes comprises: determining a metrics relationship between the child nodes and the higher-level node, and deriving metrics of the higher-level node from metrics determined for the child nodes based on the determined metrics relationship; and selecting a technological solution configured to satisfy one or more requirements of the high-level objective based, at least in part, on the metrics determined for the respective nodes of the optimization model.

2. The method of claim 1, wherein the logical relationship defined between the higher-level node and the child objective nodes comprises a Boolean logical relationship. BA-1494-PCT Page 58 of 623. The method of claim 1, wherein the logical relationship defined between the higher-level objective node and the child node comprises one or more of an AND logical relationship, an OR logical relationship, an XOR logical relationship, and a NOR logical relationship.

4. The method of claim 1, wherein the bottom-up metrics analysis procedure to determine metrics for the higher-level node having lower-level child nodes further comprises determining the metrics relationship between the child nodes and the higher-level node based on the logical relationship defined between the child nodes and higher-level node within the optimization model.

5. The method of claim 4, wherein the logical relationship comprises an OR logical relationship, and wherein the metrics relationship determined between the child nodes and the higher-level node is configured to assign range values to respective metrics of the higher-level objective node, the range values configured to span metrics determined for each child node of the higher-level node.

6. The method of claim 4, wherein the logical relationship comprises an OR logical relationship having a selected child node, and wherein the metrics relationship determined between the child nodes and the higher-level node is configured to assign metrics of the selected child node to the higher-level node.

7. The method of claim 4, wherein the logical relationship comprises an AND logical relationship, and wherein the metrics relationship determined between the child nodes and the higher-level node is configured to assign a duration metric of the higher-level node comprising a maximum duration of the child nodes.

8. The method of claim 4, wherein the logical relationship comprises an AND logical relationship, and wherein the metrics relationship determined between the child nodes and the higher-level node is configured to assign a cost metric of the higher-level node comprising a sum of cost metrics of the child nodes.

9. The method of claim 4, wherein the logical relationship comprises an AND logical relationship, and wherein the metrics relationship determined between the BA-1494-PCT Page 59 of 62child nodes and the higher-level node is configured to assign a probability of success metric of the higher-level node comprising a product of probability of success metrics of the child nodes.

10. The method of claim 1, further comprising constructing a technological solution layer of the optimization model, wherein constructing the technological solution layer comprises: identifying a higher-level node having a requirement that can be satisfied by a technological solution; generating first child nodes for the higher-level node within the technological solution layer, the first child nodes configured to represent respective technological solutions that satisfy the requirement of the identified higher-level node; and generating a control node to link the generated child nodes to the identified higher-level node within the optimization model.

11. The method of claim 10, further comprising determining metrics for the first child nodes based on characteristics of the respective technological solutions represented by the first child nodes.

12. The method of claim 1, further comprising identifying one or more redundant nodes within the optimization model, the redundant nodes comprising nodes having requirements satisfied by other nodes within the optimization model.

13. The method of claim 12, defining multi-parent relationships for the redundant nodes identified within the optimization model, the multi-parent relationships configured to associate two or more higher-level nodes with a same lower-level node.

14. The method of claim 1, further comprising assigning metrics to leaf nodes of the optimization model.

15. The method of claim 14, further comprising assigning metrics to a leaf node of the optimization model in response to user interaction with a graphical user interface. BA-1494-PCT Page 60 of 6216. The method of claim 14, further comprising assigning metrics to a leaf node based on information pertaining to a technological solution represented by the leaf node retrieved from a datastore.

17. An apparatus, comprising: a processor coupled to a memory; and a facility management module configured for operation on the processor, the facility management module configured to: construct an optimization model for a set of one or more objectives pertaining to a facility by constructing top-level nodes configured to represent respective objectives of the one or more objectives, performing top-down modeling operations to break down nodes within upper levels of the optimization model into child nodes within lower levels of the optimization model, including nodes configured to represent alternative technological solutions for respective objectives, assign metrics to leaf nodes of the optimization model, determine metrics for non-leaf nodes of the optimization model through bottom-up metrics analysis operations, and determine an implementation scheme to achieve the one or more objectives pertaining to the facility based on the determined metrics.

18. The apparatus of claim 17, further comprising a user interface module configured to generate a graphical user interface configured to provide a visual representation of the optimization module to a user 19. The apparatus of claim 18, wherein the facility management module is configured to assign metrics to one or more of the leaf nodes in response to user interaction with the graphical user interface.

20. The apparatus of claim 17, wherein the facility management module is configured to assign metrics to one or more of the leaf nodes based on generalized information pertaining to technological solutions represented by the leaf nodes retrieved from a datastore. BA-1494-PCT Page 61 of 62

Images