Order commitment decision-making method based on operation planning optimization and related equipment

Abstract

The embodiment of the invention belongs to the technical field of production decision, and relates to an order commitment decision method based on operation planning optimization, which comprises the following steps: obtaining production configuration information corresponding to a business scene to be decided; constructing an order commitment model according to the production configuration information; and solving the order commitment model according to a preset solver to obtain a target order commitment plan of the business scene to be decided. The invention further provides an order commitment decision-making device based on operation planning optimization, computer equipment and a storage medium. Through the method based on operation planning optimization, the whole process from production configuration information acquisition, model construction to generation of the target order commitment plan by using the solver is realized, so that collaborative optimization of the production plan and customer delivery is ensured, the problem of relatively high uncertainty of the order commitment result is avoided, the resource utilization efficiency is improved, and the user experience is improved. And customer trust and satisfaction are enhanced.

Description

Technical Field

[0001] This application relates to the technical field of production decision-making, and particularly to an order commitment decision-making method and related equipment based on operational research optimization. Background Art

[0002] Different from make-to-stock production, make-to-order (MTO) is a production mode that produces according to customer customization requirements. When adopting this production mode, enterprises do not pre-distribute finished products. Instead, after the design is determined between both parties, they need to wait for the customer's order to start formal production.

[0003] It can be seen that this mode has characteristics such as small batches and multiple varieties. The formal order is the key to triggering material procurement and the start of the production process, and customers require a clear reply regarding the delivery date. In this process, the existence of order commitment is particularly important, and its main role is to confirm the requested date of the order.

[0004] Currently, for order commitment, the traditional method mainly uses ATP (Available-to-Promise based on inventory availability), CTP (Capable-to-Promise based on capacity availability), or a cross-application of both to make decisions. However, this method is difficult to specifically consider the enterprise operation and production execution situation, has poor executability, increases the uncertainty of order commitment results, and leads to a decline in customer satisfaction. Summary of the Invention

[0005] The purpose of the embodiments of this application is to propose an order commitment decision-making method and related equipment based on operational research optimization to solve the technical problem of high uncertainty in order commitment results.

[0006] To solve the above technical problem, the embodiments of this application provide an order commitment decision-making method based on operational research optimization, and adopt the following technical solutions:

[0007] An order commitment decision-making method based on operational research optimization includes the following steps:

[0008] Obtain the production configuration information corresponding to the business scenario to be decided;

[0009] Construct an order commitment model according to the production configuration information;

[0010] Solve the order commitment model according to a preset solver to obtain the target order commitment plan for the business scenario to be decided.

[0011] Further, the step of constructing an order commitment model according to the production configuration information specifically includes:

[0012] Configure the parameter information and decision variables of the order commitment model according to the production configuration information, and determine the objective function of the order commitment model;

[0013] Determine the constraint conditions of the order commitment model according to the objective function, and obtain the order commitment model.

[0014] Furthermore, the optimization objectives of the objective function include high-priority guarantee and maximization of profit margin. Among them, the high-priority guarantee represents giving priority to ensuring the order production of high-priority customers, and the maximization of profit margin represents maximizing the utilization of production capacity under the condition of high-priority guarantee.

[0015] Furthermore, the constraint conditions include production capacity constraint, material constraint, demand satisfaction constraint, and strict expected order constraint.

[0016] Furthermore, the production capacity constraint represents that the sum of the usage of each resource in each time period and the production capacity occupancy of the orders in process cannot exceed the maximum daily production capacity of the resource;

[0017] The material constraint represents that the supply of raw materials for each order does not exceed the maximum supply of materials;

[0018] The demand satisfaction constraint represents that the demand for each order needs to be equal to the quantity committed for the order in each time period;

[0019] The strict expected order constraint represents that the orders with strict expected date requirements need to be shipped before the expected delivery date of the order.

[0020] Furthermore, after the step of solving the order commitment model according to a preset solver to obtain the target order commitment plan for the business scenario to be decided, the following steps are further included:

[0021] When receiving the update information corresponding to the production configuration information, update the order commitment model according to the update information, and return to execute the step of solving the order commitment model according to a preset solver to obtain the target order commitment plan for the business scenario to be decided.

[0022] Furthermore, after the step of obtaining the production configuration information corresponding to the business scenario to be decided, the following steps are further included:

[0023] Preprocess the production configuration information and convert the data format of the production configuration information into a preset format.

[0024] To solve the above technical problems, an order commitment decision device based on operational research optimization is further provided in an embodiment of the present application, and the following technical solutions are adopted:

[0025] An order commitment decision device based on operational research optimization includes:

[0026] An acquisition module, configured to acquire production configuration information corresponding to a business scenario to be decided;

[0027] A model construction module, configured to construct an order commitment model according to the production configuration information;

[0028] A model solving module, configured to solve the order commitment model according to a preset solver to obtain a target order commitment plan for the business scenario to be decided.

[0029] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

[0030] A computer device includes a memory and a processor. Computer-readable instructions are stored in the memory, and when the processor executes the computer-readable instructions, the steps of the above-mentioned order commitment decision method based on operations research optimization are implemented.

[0031] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

[0032] A computer-readable storage medium has computer-readable instructions stored thereon, and when the computer-readable instructions are executed by a processor, the steps of the above-mentioned order commitment decision method based on operations research optimization are implemented.

[0033] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0034] The order commitment decision method based on operations research optimization disclosed in the present application obtains production configuration information corresponding to a business scenario to be decided; constructs an order commitment model according to the production configuration information; and solves the order commitment model according to a preset solver to obtain a target order commitment plan for the business scenario to be decided. Through the method based on operations research optimization, the present application realizes the whole process from production configuration information collection, model construction to generating a target order commitment plan using a solver, thereby ensuring the collaborative optimization of production plans and customer deliveries, avoiding the problem of high uncertainty in order commitment results, improving resource utilization efficiency, and enhancing customer trust and satisfaction. Description of the Drawings

[0035] To more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1is an exemplary system architecture diagram to which the present application can be applied;

[0037] Figure 2 is a flowchart of an embodiment of an order commitment decision method based on operations research optimization according to the present application;

[0038] Figure 3 is a schematic structural diagram of an embodiment of an order commitment decision device based on operations research optimization according to the present application;

[0039] Figure 4 is a schematic structural diagram of an embodiment of a computer device according to the present application. Detailed implementation manners

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0041] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase does not necessarily refer to the same embodiment at every occurrence in the specification, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0042] To enable those skilled in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0043] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0044] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0045] Terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, and so on.

[0046] Server 105 can be a server providing various services, such as a background server supporting the pages displayed on terminal devices 101, 102, and 103.

[0047] It should be noted that the order commitment decision-making method based on operations research optimization provided in the embodiments of the present application is generally executed by the server. Correspondingly, the order commitment decision-making device based on operations research optimization is generally set in the server.

[0048] It should be understood that Figure 1 the numbers of the terminal devices, network, and server in

[0049] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, network, and server. Figure 2 Continuing to refer to

[0050] Step S201: Obtain the production configuration information corresponding to the business scenario to be decided.

[0051] In this embodiment, the electronic device on which the order commitment decision-making method based on operations research optimization runs (such as Figure 1The server shown can send or receive data through wired or wireless connection methods. It should be noted that the above wireless connection methods can include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

[0052] In this embodiment, first, all production configuration information required for the current business scenario is collected from the enterprise internal systems (such as ERP, MES, production scheduling systems, etc.), which usually includes sales order information, production capacity data, material supply capacity, in-process work orders and process routes, and other auxiliary data.

[0053] Step S202: Construct an order commitment model according to the production configuration information.

[0054] In this embodiment, a mathematical model can be established using the mixed integer programming method in operations research, which can include the following parts: decision variables, objective function, constraint conditions, and integration of dynamic factors.

[0055] Step S203: Solve the order commitment model according to a preset solver to obtain the target order commitment plan for the to-be-decided business scenario.

[0056] In this embodiment, according to the scale and complexity of the model, a suitable mathematical programming solver (such as COPT, CPLEX, Gurobi, etc.) is selected, and the constructed mixed integer programming model is input into the solver for solution. The solver searches for the optimal or approximately optimal solution according to the objective function and constraint conditions. This process may require a certain amount of computing time, especially in large-scale problems. Therefore, reasonable solution accuracy and time limits can be set. The solver outputs the commitment plan for each order in each time period (including the specific delivery date, committed quantity, and resource occupancy situation). The output results need to be verified to ensure that all constraint conditions are met and it is executable in actual production scheduling. If necessary, adjustments or re-solutions are made in combination with the actual production situation.

[0057] This application realizes the whole process from production configuration information collection, model construction to generating the target order commitment plan using the solver through an operations research optimization-based method, thereby ensuring the collaborative optimization of production plans and customer deliveries, avoiding the problem of high uncertainty in order commitment results, improving resource utilization efficiency, and enhancing customer trust and satisfaction.

[0058] In some optional implementation manners of this embodiment, the step of constructing an order commitment model according to the production configuration information specifically includes:

[0059] Configure the parameter information and decision variables of the order commitment model according to the production configuration information, and determine the objective function of the order commitment model;

[0060] Determine the constraint conditions of the order commitment model according to the objective function to obtain the order commitment model.

[0061] In this embodiment, constructing an order commitment model is an overall process. Its core lies in using the collected production configuration information to configure the parameters of the model, determine the decision variables, and construct the objective function. Then, based on the objective function, the constraint conditions that meet the actual production requirements are deduced to form a complete mathematical model. This process is not just static data modeling, but organically integrates multi-dimensional data such as orders, production capacity, materials, in-process work orders, and process routes. The actual production problems are digitized through decision variables (such as binary variables indicating whether an order is shipped at each time period, resource usage status, and order commitment quantity), and driven by the objective function (usually reflected as maximizing the overall profit while minimizing resource idle or remaining production capacity under the premise of meeting customer priorities and strict delivery requirements), so as to guide subsequent production scheduling and order commitment decisions. In this process, by configuring the production configuration information, the model parameters can fully reflect the enterprise's internal business objectives and production capabilities. For example, factors such as customer priorities and order profits are used to weight the objective function, taking into account both the urgency of customer needs and the enterprise's profitability. Immediately after the objective function is determined, based on the optimization direction it reflects, various constraint conditions are introduced, such as resource production capacity constraints, material supply constraints, and order constraints with strict delivery dates, to ensure that the solution obtained by model solving is theoretically feasible and can be implemented in actual production.

[0062] Exemplarily, the order commitment model can be roughly as follows:

[0063] A) Input data

[0064] I: Order set, representing the set of all sales orders.

[0065] J: Process set, representing the set of all production processes.

[0066] R: Resource set, representing the set of all available resources (such as machines, workers, equipment, etc.).

[0067] T: Time set, representing the time periods in the production plan (such as days, weeks, etc.).

[0068] P i : Customer priority of order i, reflecting the importance and urgency of the customer.

[0069] C i:Profit of order i, reflecting the profitability of the order.

[0070] S i Expected delivery date of order i.

[0071] D i :Demand quantity (product quantity) of order i.

[0072] M i Whether order i requires strict on-time delivery (1 means strict, 0 means flexible).

[0073] a i,j : Quantity of process j required to be completed for order i.

[0074] m i,j : Capacity occupancy of resource r for process j (unit man-hour consumption).

[0075] c h,j : Capacity required to complete process j for work-in-progress order h.

[0076] U r : Maximum daily capacity of resource r.

[0077] Q r,t : Remaining capacity of resource r at time t.

[0078] b i,k : Quantity of material k required for order i.

[0079] N k : Supply upper limit of material k.

[0080] B) Decision variables

[0081] x i,t : Whether order i is shipped (committed) at time t (0 or 1), 1 if committed to ship at this moment, otherwise 0.

[0082] y r,t : Whether resource r is used at time t (0 or 1), 1 if the resource is used at this moment, otherwise 0.

[0083] Z i Demand commitment quantity of order i (quantity satisfying the commitment of order i).

[0084] C) Constraint conditions

[0085] Capacity constraint: Considering that the usage of each resource r at each time period t at the process level + the capacity occupancy of work-in-progress orders cannot exceed the maximum daily capacity Ur of the resource, and Ur will be calculated recursively according to the production capacity reservation table.

[0086]

[0087] Material constraint: Ensure that the supply quantity of raw materials for each order does not exceed the maximum supply quantity of materials.

[0088]

[0089] Demand fulfillment constraint: Ensure that the demand quantity Z of each order i is equal to the quantity committed for order i in each time period (fully committed when there is remaining order capacity).

[0090]

[0091] Strict expected order constraint: For orders with strict requirements for the expected date, they must be shipped on the expected delivery date S of the order i of the order.

[0092]

[0093] Objective function: Maximize the profit considering customer priorities and make the production capacity utilization as large as possible.

[0094]

[0095] λ: Penalty coefficient for production capacity utilization. Try to keep the remaining production capacity low. By weighted profit and priority, give priority to ensuring orders from high-priority customers and maximize the profit.

[0096] It should be noted that the model can not only adopt traditional mixed integer programming, but also introduce a multi-objective optimization framework to balance profit, customer satisfaction and resource utilization simultaneously, so as to better cope with the complex and changeable situations on the production site. Secondly, in order to meet the demand of continuous updating of real-time data in actual production, the model parameters and constraint conditions can be designed as a dynamic update mechanism, so that the order commitment plan can be adaptively adjusted according to the latest production status. In addition, auxiliary solution technologies such as heuristic algorithms and genetic algorithms can be combined. When the model scale is large or the problem is complex, an approximate optimal solution can be quickly obtained and the flexibility and robustness of the decision-making can be ensured. Finally, through the integration with the enterprise ERP and MES systems and the graphical decision support interface, the application of the model can achieve closed-loop feedback and human-machine collaboration, enabling the planner to perform manual intervention on the parameters and constraints when necessary, and further improving the executability and flexibility of the overall decision-making.

[0097] This application constructs a rigorous and flexibly extensible order commitment model by configuring production configuration information, setting model parameters and decision variables, and determining the objective function and constraint conditions, so that the model can not only reflect the actual production requirements but also have the ability of adaptive optimization.

[0098] In some alternative implementation manners of this embodiment, the optimization objectives of the above objective function include high-priority guarantee and maximization of profit margin. Among them, the high-priority guarantee represents giving priority to ensuring the production of orders of high-priority customers, and the maximization of profit margin represents maximizing the utilization of production capacity under the condition of high-priority guarantee.

[0099] In this embodiment, overall, when constructing the objective function, the model will introduce the weights of high-priority customers, so that under limited production resources, the orders of high-priority customers are preferentially guaranteed during the optimization process. Specifically, the customer priority parameter can be set for each order, and in the objective function, a larger weight is multiplied by the profit index and the committed quantity related to the order, so that the high-priority orders can preferentially meet their delivery requirements during the solution process. In this way, even in the case of resource tension or production capacity limitation, it can ensure that the orders of important customers are preferentially arranged, thereby improving customer satisfaction and market competitiveness. On the premise of high-priority guarantee, the objective function also considers the problem of maximizing profit margin. This objective is usually reflected by optimizing the production capacity utilization rate, that is, under the premise of meeting high-priority orders, minimizing the idle resources as much as possible (for example, reflected by punishing the remaining production capacity), so that the entire production plan can obtain higher economic benefits when using the existing resources. The profit term and the penalty term for resource utilization in the objective function work together, so that when the solver generates the order commitment plan, it not only ensures the timely production of high-priority customer orders, but also achieves the maximization of the overall profit under the condition of minimizing the idle production capacity.

[0100] This application ensures the priority production of critical customer orders and optimally utilizes the production capacity with limited resources by simultaneously introducing the optimization objectives of high-priority guarantee and profit maximization in the objective function, thereby improving the overall economic benefits.

[0101] In some alternative implementation manners of this embodiment, the above constraint conditions include production capacity constraint, material constraint, demand satisfaction constraint, and strict expected order constraint.

[0102] In this embodiment, the production capacity constraint ensures that the total usage of each production resource in each time period (including the capacity occupied by newly arranged orders and in-process orders) does not exceed the maximum daily production capacity of the resource; the material constraint guarantees that all orders do not exceed the maximum supply of materials in terms of raw material consumption, thus preventing over-commitment of resources; the demand fulfillment constraint requires that the cumulative committed quantity of each order over each time period should meet the order demand, achieving the full delivery of orders; the strict expected order constraint ensures that orders with strict delivery date requirements must be shipped before the customer's expected delivery date, thereby improving customer satisfaction. These constraint conditions together constitute a multi-dimensional decision-making model that can comprehensively reflect production capacity, material supply, and customer requirements, providing practical and feasible limiting conditions for subsequent solution seeking.

[0103] By setting multiple constraint conditions such as production capacity, materials, demand fulfillment, and strict expected orders, this application ensures that the generated order commitment plan has high practical executability and risk prevention and control capabilities in terms of resources, materials, and delivery requirements.

[0104] In some alternative implementation manners of this embodiment, the above production capacity constraint means that the sum of the usage of each resource in each time period and the capacity occupied by in-process orders cannot exceed the maximum daily production capacity of the resource;

[0105] The material constraint means that the raw material supply quantity of each order does not exceed the maximum supply quantity of materials;

[0106] The demand fulfillment constraint means that the demand quantity of each order needs to be equal to the quantity committed by the order over each time period;

[0107] The strict expected order constraint means that orders with strict expected date requirements need to be shipped before the expected delivery date of the order.

[0108] In this embodiment, the production capacity constraint specifically requires that in each time period, the total usage of each resource (including the consumption of the resource by new orders and in-process orders) must be lower than or equal to the maximum daily production capacity of the resource. This constraint ensures that the production plan does not exceed the actual capabilities of equipment, personnel, or other resources, avoiding production bottlenecks or delays caused by overloading. The material constraint requires that the raw material supply quantity of each order does not exceed the maximum supply quantity of materials, thus preventing the production progress from being affected due to insufficient raw materials during the order execution process. The demand fulfillment constraint stipulates that the demand quantity of each order must be equal to the quantity committed by the order over each time period. This can not only ensure the complete delivery of orders but also facilitate the subsequent tracking and verification of order execution situations. The strict expected order constraint emphasizes that for orders with strict delivery date requirements, it is necessary to ensure that they are shipped before the customer's expected delivery date, thus meeting the requirements of contracts or customer service agreements.

[0109] It should be noted that in practical applications, the above constraints can be further extended to dynamic constraints with a certain fault tolerance or buffer mechanism. For example, for capacity constraints, production capacity reservation can be introduced to cope with unexpected situations that may occur during the production process; for material constraints, a certain safety inventory can be set; and for the constraints of demand satisfaction and strict expected orders, appropriate flexible adjustments can also be made according to the actual situation on the premise of ensuring basic satisfaction, forming a more intelligent order commitment decision-making system.

[0110] This application realizes the precise matching of production resources and order demands by defining each constraint in detail (such as ensuring that the usage amount of each resource does not exceed the maximum daily production capacity, the material supply amount does not exceed the upper limit, the order demand is consistent with the committed quantity, and strictly requiring the key orders to be delivered on schedule), thereby improving the reliability of the production plan and the customer trust.

[0111] In some alternative implementation manners of this embodiment, after the step of solving the order commitment model according to the preset solver to obtain the target order commitment plan of the to-be-decided business scenario, the following steps are further included:

[0112] When receiving the update information corresponding to the production configuration information, update the order commitment model according to the update information, and return to execute the step of solving the order commitment model according to the preset solver to obtain the target order commitment plan of the to-be-decided business scenario.

[0113] In this embodiment, a dynamic feedback and update mechanism is introduced. Its core idea is that after obtaining the preliminary target order commitment plan through the preset solver, if the production configuration information is updated, the order commitment model is updated in a timely manner and re-solved to obtain the latest order commitment plan. This design takes into account the uncertainty and dynamic changes of information in the actual production environment, such as production capacity changes, material supply fluctuations, order changes, or customer priority adjustments. The update mechanism makes the entire decision-making system have high flexibility and adaptability, and can continuously optimize the order commitment result on the basis of real-time monitoring and feedback to ensure that the generated production plan always conforms to the current actual situation. To further improve the robustness of the system, the update step can be combined with a real-time data acquisition system and a prediction model to warn and quickly respond to abnormal situations in the production environment (such as equipment failures, sudden orders, etc.), so as to re-adjust the model parameters in a short time and quickly obtain a new optimal or approximate optimal solution through the solver. This closed-loop feedback mechanism helps to continuously improve the execution efficiency of production scheduling and customer satisfaction.

[0114] By introducing a dynamic update mechanism, this application timely updates the model and re-solves it according to the changes in production configuration information, obtaining an order commitment plan that can respond to the changes in the production site in real time, thereby enhancing the flexibility and robustness of the system.

[0115] In some alternative implementation manners of this embodiment, after the step of obtaining the production configuration information corresponding to the business scenario to be decided, the following is further included:

[0116] Preprocess the production configuration information and convert the data format of the production configuration information into a preset format.

[0117] In this embodiment, on the basis of obtaining the production configuration information, a preprocessing step is added, that is, the original production configuration information is converted into a preset data format. This step mainly solves the problem of inconsistent data formats between various systems and data sources within the enterprise, ensuring that the data input to the model can be standardized and structured, thereby guaranteeing the accuracy and efficiency of model construction and solution. Data preprocessing usually includes data cleaning, format conversion, missing value filling, outlier handling, and data standardization, etc., which all provide a solid data foundation for subsequent parameter configuration and model solution. With the continuous improvement of the enterprise's informatization level, the data preprocessing module can further integrate automated tools and artificial intelligence technologies to achieve rapid processing and real-time update of massive production data. At the same time, by establishing data interface standards, seamless docking with systems such as ERP and MES can also be achieved, thereby constructing an end-to-end intelligent order commitment decision-making platform.

[0118] This application realizes the accuracy and consistency of data through preprocessing, format standardization, and data cleaning of production configuration information, ensures the high efficiency of subsequent model construction and solution, and promotes seamless integration with other enterprise information systems.

[0119] The embodiments of this application can obtain and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0120] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0122] It should be understood that although the steps in the flowchart of the accompanying drawings are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least some of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. Their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0123] Further reference Figure 3 to Figure 2 As an implementation of the method shown above, an embodiment of an order commitment decision-making device based on operations research optimization is provided in this application. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0124] As Figure 3 shown, the order commitment decision-making device 300 based on operations research optimization described in this embodiment includes: an acquisition module 301, a model construction module 302, and a model solution module 303. Among them:

[0125] The acquisition module 301 is used to acquire production configuration information corresponding to the business scenario to be decided;

[0126] The model construction module 302 is used to construct an order commitment model according to the production configuration information;

[0127] The model solution module 303 is used to solve the order commitment model according to a preset solver to obtain the target order commitment plan for the business scenario to be decided.

[0128] The order commitment decision-making device based on operations research optimization provided by this application realizes the whole process from production configuration information collection, model construction to generating a target order commitment plan using a solver through an operations research optimization method, thereby ensuring the collaborative optimization of production plans and customer deliveries, avoiding the problem of high uncertainty in order commitment results, improving resource utilization efficiency, and enhancing customer trust and satisfaction.

[0129] In some alternative implementation manners of this embodiment, the model construction module 302 is further configured to:

[0130] Configure the parameter information and decision variables of the order commitment model according to the production configuration information, and determine the objective function of the order commitment model;

[0131] Determine the constraint conditions of the order commitment model according to the objective function to obtain the order commitment model.

[0132] The order commitment decision-making device based on operations research optimization provided by this application constructs a rigorous and flexibly extensible order commitment model by configuring production configuration information, setting model parameters and decision variables, and determining the objective function and constraint conditions, so that the model can not only reflect actual production requirements but also have the ability of adaptive optimization.

[0133] In some alternative implementation manners of this embodiment, in the model construction module 302, the optimization objectives of the objective function include high-priority guarantee and maximization of profit margin. Among them, the high-priority guarantee represents giving priority to ensuring the order production of high-priority customers, and the maximization of profit margin represents maximizing the utilization of production capacity under the condition of high-priority guarantee.

[0134] The order commitment decision-making device based on operations research optimization provided by this application ensures the priority production of key customer orders while achieving the optimal utilization of production capacity with limited resources by introducing the optimization objectives of high-priority guarantee and profit maximization into the objective function at the same time, thereby enhancing the overall economic benefits.

[0135] In some alternative implementation manners of this embodiment, in the model construction module 302, the above-mentioned constraint conditions include production capacity constraint, material constraint, demand satisfaction constraint, and strict expected order constraint.

[0136] The order commitment decision-making device based on operations research optimization provided by this application ensures that the generated order commitment plan has high practical executability and risk prevention and control capabilities in terms of resources, materials, and delivery requirements by setting multiple constraint conditions such as production capacity, materials, demand satisfaction, and strict expected orders.

[0137] In some alternative implementation manners of this embodiment, in the model construction module 302,

[0138] The above production capacity constraint means that the sum of the usage of each resource in each time period and the occupied single production capacity in the production order cannot exceed the maximum daily production capacity of the resource.

[0139] The above material constraint means that the supply quantity of raw materials for each order does not exceed the maximum supply quantity of the material.

[0140] The above demand satisfaction constraint means that the demand quantity of each order needs to be equal to the quantity committed in each time period of the order.

[0141] The above strict expected order constraint means that an order with a strict expected date requirement needs to be shipped before the expected delivery date of the order.

[0142] The order commitment decision device based on operational research optimization provided by this application realizes the precise matching of production resources and order demands by defining each constraint in detail (such as ensuring that the usage of each resource does not exceed the maximum daily production capacity, the material supply quantity does not exceed the upper limit, the order demand is consistent with the committed quantity, and strictly requiring key orders to be delivered on schedule), thereby improving the reliability of the production plan and the customer trust.

[0143] In some optional implementation manners of this embodiment, the model construction module 302 is further configured to:

[0144] When receiving the update information corresponding to the production configuration information, update the order commitment model according to the update information, and return to execute the step of solving the order commitment model by a preset solver to obtain the target order commitment plan of the to-be-decided business scenario.

[0145] The order commitment decision device based on operational research optimization provided by this application, by introducing a dynamic update mechanism, updates the model and re-solves in a timely manner according to the changes in the production configuration information, and obtains an order commitment plan that can respond to the changes in the production site in real time, thereby enhancing the flexibility and robustness of the system.

[0146] In some optional implementation manners of this embodiment, the model solving module 303 is further configured to:

[0147] Preprocess the production configuration information and convert the data format of the production configuration information into a preset format.

[0148] The order commitment decision device based on operational research optimization provided by this application realizes the accurate consistency of data through preprocessing, format standardization and data cleaning of the production configuration information, ensures the high efficiency of subsequent model construction and solving, and promotes the seamless integration with other enterprise information systems.

[0149] To solve the above technical problems, an embodiment of this application further provides a computer device. For details, please refer to Figure 4, Figure 4 This is the basic structural block diagram of the computer device in this embodiment.

[0150] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of this technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0151] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device, etc.

[0152] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 41 can also include both the internal storage unit and the external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store the operating system installed on the computer device 4 and various application software, such as computer-readable instructions for the order commitment decision method based on operations research optimization. In addition, the memory 41 can also be used to temporarily store various data that have been output or will be output.

[0153] In some embodiments, the processor 42 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the order commitment decision method based on operations research optimization.

[0154] The network interface 43 may include a wireless network interface or a wired network interface, which is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0155] The computer device provided in this application realizes the whole process from production configuration information collection, model construction to generating a target order commitment plan using a solver through an operations research optimization-based method, thereby ensuring the collaborative optimization of production plans and customer deliveries, avoiding the problem of high uncertainty in order commitment results, improving resource utilization efficiency, and enhancing customer trust and satisfaction.

[0156] This application also provides another implementation manner, that is, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to execute the steps of the order commitment decision method based on operations research optimization as described above.

[0157] The computer-readable storage medium provided in this application realizes the whole process from production configuration information collection, model construction to generating a target order commitment plan using a solver through an operations research optimization-based method, thereby ensuring the collaborative optimization of production plans and customer deliveries, avoiding the problem of high uncertainty in order commitment results, improving resource utilization efficiency, and enhancing customer trust and satisfaction.

[0158] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions to enable a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of this application.

[0159] Obviously, the embodiments described above are only a part of the embodiments of this application, rather than all embodiments. The preferred embodiments of this application are given in the accompanying drawings, but they do not limit the patent scope of this application. This application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of this application more thorough and comprehensive. Although this application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure made by using the content of this application's specification and drawings, directly or indirectly applied in other related technical fields, is equally within the scope of patent protection of this application.

Claims

1. An order commitment decision method based on operations optimization, characterized in that: The steps include: Obtain production configuration information corresponding to the business scenario to be decided; constructing an order commitment model according to the production configuration information; The order commitment model is solved according to a preset solver to obtain a target order commitment plan for the business scenario to be decided.

2. The order commitment decision method based on operations optimization according to claim 1 is characterized in that: The step of constructing an order commitment model according to the production configuration information specifically includes: According to the production configuration information, parameter information and decision variables of the order commitment model are configured, and an objective function of the order commitment model is determined; The constraint conditions of the order commitment model are determined according to the objective function to obtain the order commitment model.

3. The order commitment decision method based on operations optimization according to claim 2 is characterized in that: The optimization goals of the objective function include high priority guarantee and maximum profit margin, wherein the high priority guarantee represents giving priority to the production of orders from high priority customers, and the maximum profit margin represents maximizing the utilization of production capacity under the condition of high priority guarantee.

4. The order commitment decision method based on operations optimization according to claim 2 is characterized in that: The constraints include capacity constraints, material constraints, demand fulfillment constraints, and strict expected order constraints.

5. The order commitment decision method based on operations optimization according to claim 4 is characterized in that: The capacity constraint means that the sum of the usage of each resource in each time period and the capacity occupied by the orders in process cannot exceed the maximum daily capacity of the resource; The material constraint means that the raw material supply quantity of each order shall not exceed the maximum supply quantity of the material; The demand fulfillment constraint means that the demand quantity of each order must be equal to the quantity promised by the order in each time period; The strict expected order constraint means that an order with strict expected date requirement needs to be shipped before the expected delivery date of the order.

6. The order commitment decision method based on operations optimization according to claim 1 is characterized in that: After the step of solving the order commitment model according to a preset solver to obtain a target order commitment plan for the business scenario to be decided, the method further includes: When update information corresponding to the production configuration information is received, the order commitment model is updated according to the update information, and the step of solving the order commitment model according to a preset solver to obtain a target order commitment plan for the business scenario to be decided is returned to be executed.

7. The order commitment decision method based on operations optimization according to any one of claims 1 to 6, characterized in that: After the step of obtaining the production configuration information corresponding to the business scenario to be decided, the method further includes: The production configuration information is preprocessed to convert the data format of the production configuration information into a preset format.

8. An order commitment decision-making device based on operations optimization, characterized in that: include: The acquisition module is used to obtain the production configuration information corresponding to the business scenario to be decided; A model building module, used to build an order commitment model according to the production configuration information; The model solving module is used to solve the order commitment model according to a preset solver to obtain a target order commitment plan for the business scenario to be decided.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the order commitment decision method based on operations optimization as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the order commitment decision method based on operations optimization as described in any one of claims 1 to 7.

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