System and method for look-ahead optimization of mine operation

Abstract

A method and computing environment for mine productivity optimization is disclosed. Possible movements for mine vehicles over a predetermined timeframe are modeled, and a mine merit function is computed for each of the possible movements. A possible movement set is selected on the basis of the merit function, and a vehicle schedule is generated from the possible movement set and pushed out to the vehicles. Optimizations of future movement may be made continuously, on a rolling basis, and the schedules updated. Schedules may be revised on the basis of planned future events such as maintenance events.

Description

[0001] PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0002] Attorney Docket No. 122169.00339

[0003] SYSTEM AND METHOD FOR LOOK-AHEAD OPTIMIZATION OF MINE OPERATION

[0004] CROSS-REFERENCE TO RELATED APPLICATIONS

[0005] This application claims the benefit of priority from U.S. Provisional Application 63 / 701,541, filed under the same title on September 30, 2024, the entire contents of which is incorporated herein by reference.

[0006] FIELD OF THE INVENTION

[0007] This disclosure relates to systems and methods for managing the movement of equipment, material and personnel in a mine environment, and specifically, in an open pit mining environment.

[0008] BACKGROUND

[0009] Mining environments, particularly open pit surface mining environments rely on the safe and efficient extraction of material and the movement of mobile equipment for efficient operation.

[0010] Conventional mine operations may be described as follows. A pit mine includes one or more working faces (also called pit faces). These are areas where ore is being blasted or dug. One or more power shovels work in proximity to the faces. Shovels are typically mobile or semi-mobile and are typically diesel-electric or all-electric powered (e.g., dragline excavators). Haul trucks receive ore at the power shovels and transport the ore to one of one or more crushers. Crushed ore is then transported, ty pically by conveyor, for further processing and refining, e.g., by solvent extraction or the like. As an intermediate step, haul trucks may transport ore from faces to stockpile, where ore of various different types or grades is blended, and then from stockpiles to crushers. Thus, at any one time, a large number of vehicles may be working within a particular mine, and during operation, each vehicle will move to different locations within the mine to retrieve material, dump material, or to assist in a number of different operations.

[0011] The workhorse of a modem surface mine is a mine haul truck, which is a dump truck capable of hauling hundreds of tons of material. Conventional fleet management systems (FMS) are used to guide haul trucks as they move through the mine to conduct tasks such as those set forth above. An FMS will provide missions (i.e., next location in the mine and tasks

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[0014] Attorney Docket No. 122169.00339 such as “go to shovel 1 to receive loacTor “go from shovel 1 to stockpile 3 to deliver load”). An FMS system may also provide detailed routing instructions, including, for example, turn by turn instructions for navigation through the mine environment to the next destination along a particular route following one or more of the mine’s roadways or designated throughways. These instructions may be provided at a relatively high level of detail. For example, an FMS may provide guidance instructions that a driver may follow to a particular location on a particular side of a shovel. An exemplary FMS is described in co-owned United States Patent No. 9644978 entitled “Target destination selection for a mining vehicle,” the entirety of which is incorporated herein by reference for all purposes.

[0015] A conventional FMS seeks to route haul trucks through the mine with a high degree of efficiency. Efficient utilization of haul trucks is advantageous because haul truck transportation of ore tends to be one of several rate limiting processes for mine productivity. Moreover, haul trucks represent a high level of capital investment, and mine operators seek to maximize the time in which they are in productive use, and to minimize down time, unproductive time (e.g., travel that is not to a shovel or a crusher, travel while empty to a shovel, traveling to a waste dump with a load of non-ore material, etc.) and idling time (e.g., waiting in a queue).

[0016] Additionally, efficient routing through the mine is advantageous because of the operational nature of haul trucks themselves. Mine haul trucks are some of the largest land vehicles ever built. As such, they are characterized by limited maneuverability, relatively slow acceleration and deceleration and poor sight lines on every side of the vehicle. A mine is an inherently hazardous environment characterized by ad hoc road networks that may change day- to-day, steep slopes, unstable shoulders, and cross traffic. An accident or collision can be catastrophic, both for the operator, and in terms of lost productivity and repair costs.

[0017] Moreover, operating a haul truck is extremely expensive in terms of energy. A conventional mine haul truck is a diesel-electric vehicle, which is powered by diesel electric generators powering electric drive motors. While a diesel-electric drive is an inherently efficient mode of operation, particularly when combined with regenerative braking, haul trucks still consume enormous amounts of fuel simply because of their mass. With the advent of more efficient and cost-effective hub motors, electrical storage batteries, and management systems, new opportunities exist for battery-electric mine vehicles and equipment. Battery Electric Vehicles (BEVs) potentially offer increased energy efficiency for open-pit load & haul

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[0020] Attorney Docket No. 122169.00339 operations. However, BEVs batteries still do not have the energy density of diesel, and so they will require more frequent stops for battery swaps or charging, or will require supplementation with overhead electric trolly lines, which represent an additional expense. The cost of operating any haul truck, either BEV or diesel-electric, demands that vehicles be used in the most efficient way, i.e., with minimal idling, and with the shortest possible routes between missions.

[0021] In a conventional FMS, and as set forth above, a driver is assigned a mission, which may be thought of as a current task paired with a current destination. When an operator completes a mission (e.g., delivers a load of ore to a stockpile or crusher), the operator may request a new mission, or be automatically assigned a new mission by the FMS. The FMS will generally make next mission assignments by trucks according to a rule set intended to maximize mine productivity (i.e., tonnage of ore moved per unit time such as per shift). To take a simplified example, application of a rule set may cause the FMS to direct a truck to the closest shovel with an available loading position or the crusher with the shortest queue, or the like. Next assignments are generally generated and assigned in a manner intended to increase throughput and ensure that trucks, shovels and crushers are always being used.

[0022] In a conventional FMS, next assignment decisions are typically made on the basis of contemporaneous information about the state of the mine (e.g., the locations of vehicles and equipment and the current mine plan including the mine road network) at the time of the operator’s request for the next assignment. The next mission is also typically assigned on the basis of a master schedule which is generated to meet certain mine production targets. These conventional methods have certain disadvantages in that they do not incorporate longer timehorizon information, which may be useful in providing vehicles with their next assignment. Systems and methods described below address these shortcomings.

[0023] SUMMARY OF THE INVENTION

[0024] Embodiments of the invention are directed to systems and methods for optimizing vehicle movement and equipment utilization (e.g., shovel utilization) in a mine environment, and generating and adjusting schedules for vehicle movement and equipment utilization. In one embodiment, a number of functional software modules or processes are provided. One module is anFMS module, which sends data to and receives data from computing devices located at mobile agents (e.g., trucks, shovels and other equipment). The FMS may provide trucks with their next mission or assignment, for example.

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[0027] Attorney Docket No. 122169.00339

[0028] The system also includes a primary schedule module in communication with a data store. The data store contains information regarding the operation of the time such as its geographic layout and the road network. The primary schedule module is configured to optimize vehicle and equipment assignments in the mine over predetermined time horizons. The primary scheduler’s optimization process involves computing one or more merit functions as a function of feasible vehicle movement modified by a set of constraints, and then selecting a set of vehicle movements that maximize the value of the merit function (i.e., an optimal schedule). The merit functions may reflect parameters such as energy use, emissions or mine output. Variables to movement and other potential events may be injected into the model of feasible movements to determine optimal vehicle routing to maximize the merit function over a predetermined time frame. The result of this optimization process may be one or more vehicle movement schedules that designate the movement of vehicles or other equipment over a predetermined time period.

[0029] Embodying systems also include a mission manager module. The mission manager module mediates between the FMS and the primary schedule module to pass vehicle assignments (i.e., missions) to the FMS for forwarding to vehicles at the appropriate time, such as when a vehicle or the FMS requests a next mission.

[0030] In one aspect, the invention includes a method of directing the movement of vehicles or other equipment in a pit mine environment. The method includes the steps of building a long, time-horizon schedule specifying the destinations and routes taken by one or more mine vehicles over a first predetermined time period. The schedule is built by determining an optimal sequence of feasible vehicle movements over a time period, where feasibility of the movement is determined by applying a set of constraints, such as movement constraints. Optimization may occur by iteratively computing a merit function as a function of a various feasible movement sets, and identifying the set of movements under which the value of the merit function is maximized or exceeds some threshold. The system then transforms this series of movements into a long-term schedule, which may be a default schedule. When a vehicle requests a next assignment, the assignment may be provided to the vehicle in accordance with this default schedule. However, in preferred embodiments, the default (i.e., the long term) schedule is refined on a real time basis. In accordance with this refinement method, the system generates an optimal short-term series of vehicle movements on the basis of a current state of the mine. The current state of the mine may take into account contemporaneous events that

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[0033] Attorney Docket No. 122169.00339 were not part of the mine model when the optimization routine was initially run (e.g., at the beginning of the day or shift), such as vehicle break downs, personnel issues, accidents or road closures. This “snapshot” optimization may be run to determine optimal movements over a short time frame (e.g., the next hour or two hours), and a second short term schedule may be generated from the snapshot optimization. This optimization may be used to provide next assignments and to modify the long- term, default schedule.

[0034] In one embodiment, a computerized method of moving vehicles in an open pit mine is described. In accordance with the method, data is received regarding a contemporaneous state of the mine, including the positions of vehicles, shovels and crushers, a geographical model of a mine layout, the geographical model including data representing a road network, and a plurality of vehicle models each model reflecting performance characteristics of a vehicles. With this date, the method generates a set of feasible future movements of vehicles and materials in the mine based on the data regarding a contemporaneous state of the mine over a predetermined timeframe, wherein each of the feasible movements in the set is generated subject to a set of constraints. The method then calculates, for each feasible movement in the set, a merit function reflecting the performance of the mine, and selects one of the set of feasible future movements on the basis of the merit function calculation. Then, the method generates a vehicle schedule on the basis of the selected set of feasible future movements, transmits to one or more mine vehicles movement instructions based on the vehicle schedule, and moves the one or more vehicles in accordance with the movement instructions. The method may include the step of displaying movement instructions to a vehicle operator on a visual display.

[0035] In another embodiment, a method for directing and controlling the movement of vehicles in an open pit mine is provided. According to the method, a mine production targets specifying amounts of mined material to be provided to one or more targets over the course of a timeframe is received. Then the method determines amounts of material to be mined by one or more sources over the course of the timeframe in order to achieve the production target. Then the method computes a contemporaneous schedule specifying the movement of one or more vehicles over the course of a subset of the time frame between the sources and sufficient to transport material at a rate sufficient to achieve the production targets. The computation considers the positions sources and targets, a geographical model of a mine layout, the geographical model including data representing a road network, and a plurality of vehicle models each model reflecting performance characteristics of a vehicles. The method also

[0036] 5

[0037] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0038] Attorney Docket No. 122169.00339 involves transmiting to one or more mine vehicles movement instructions based on the contemporaneous schedule and moving one or more vehicles in accordance with the movement instructions. Additionally, the method includes receiving data specifying one or more scheduled events that impact vehicular movement during the subset of the time frame, revising the schedule on the basis of the one or more scheduled events and transmiting to the one or more mine vehicles revised movement instructions based on the revised schedule. One or more of the vehicles are moved in accordance with the revised movement instructions.

[0039] Inventive embodiments include a computing environment including one or more server and client computing devices in network communication with each other and in electronic data communication with non-volatile data storage, where the computing environment is used to carry out the method steps described above. In particular, the non-volatile data storage may include computer executable instructions encoded thereon sufficient to cause one or more programmable devices on the one or more computing devices to carry out the method steps.

[0040] Systems and methods according to inventive embodiments have certain advantages, such as increases in mine productivity overtime, decreases in overall energy use, and decreases in emissions.

[0041] Inventive systems have additional advantages over conventional systems. Current systems for optimizing haulage vehicle assignments do not consider emission reduction as part of their target objective. Current systems do their optimization based on limited data regarding the current state of the mine without considering all scheduled activities. Legacy systems were designed for conventional vehicles powered by diesel fuel and do not optimize re-energizing for vehicles using alternative energy sources. Prior systems optimized based on snapshots of the mine state taken at specific times, with incomplete information, and without continually updating in real-time with each change to the mine. Previous systems focused on maximizing production without specifically following a pre-determined plan. While some previous systems may address one or more of the issues mentioned above, the look-ahead optimization (LAO) system described herein is the first to provide a comprehensive solution for each of these problems.

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[0044] Attorney Docket No. 122169.00339

[0045] BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein constitute part of this specification and include example embodiments of the present invention which may be embodied in various forms.

[0047] FIG. 1 depicts a computing environment in which methods according to the invention may be practiced.

[0048] FIG. 2 conceptually depicts computer processes usable for carrying out a method according to the invention.

[0049] FIG. 3 is a diagram showing inputs to a look-ahead optimization system according to the invention, constraints that may be considered by the system, and exemplary outputs.

[0050] FIG. 4 is a flow chart showing how the system assigns missions on the basis of schedule optimization.

[0051] FIGs 5-6 are flow charts showing the logic used by the mission manager system to determine whether to send a mission to the FMS.

[0052] FIG. 7 is a conceptual block diagram showing operation of the primary scheduler and the mission manager subsystems at a higher level of detail than FIG. 2.

[0053] FIG. 8 is a flow diagram showing the steps of a method of scheduling and rescheduling vehicle movement in a mine environment.

[0054] DETAILED DESCRIPTION

[0055] The present inventions will now be discussed in detail with regard to the attached drawing figures that were briefly described above. In the following description, numerous specific details are set forth illustrating the Applicant’s best mode for practicing the invention and enabling one of ordinary skill in the art to make and use the invention. It will be obvious, however, to one skilled in the art that the present invention may be practiced without many of these specific details. In other instances, well-known machines, structures, and method steps have not been described in particular detail in order to avoid unnecessarily obscuring the present invention. Unless otherwise indicated, like parts and method steps are referred to with like reference numerals.

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[0059] The methods described herein may be carried out in an actual or simulated mine environment in conjunction with processes being executed in computing environments, such as the one 100 illustrated in FIG. 1. The computing devices (e.g., 110) that may carry out the inventive methods described below will generally include the features of computing devices such as one or more programmable microprocessors (112), volatile and / or non-volatile memory (120), input devices (116) such as keyboards, microphones and pointing devices, and output devices (e.g., 114) such as visual displays and speakers. The method steps described below may be implemented on such computing devices, and specifically, the computing devices may have programmable processors in electronic communication with non-volatile storage, which may have computer readable instructions encoded thereon that are executable by the processor to carry out the described method steps.

[0060] The computing devices on which the described systems are implemented preferably have wired and / or wireless network interface circuits configured to allow the device to receive data from and send data to other computing devices (e.g., 130, 135) over a communications network 140. Communications network 140 may be wired or wireless or a mix of the two. Information accessed by computing device 110 over the communications network 140 may include data objects including data about vehicles, vehicle configurations, mine layouts, and the like. In certain cases, the mine simulations described below take data on parameters like vehicle condition (e.g., vehicle speed, location, destination, weight), vehicle type, geographical data regarding mine layouts, vehicle activity such as travel between waypoints, and mine productivity from sensors and / or other computing devices within a mine environment. For example, a computing device hosting a mine model and running optimization routines as described may receive data (133) from computing devices located at vehicles (130) or directly from sensors at the vehicles, which data may include information about the speed of the vehicle, its weight, time varying location, fuel level and battery data such as voltage, temperature and charge state and tire temperature.

[0061] Computing devices may receive data geographically representing a mine environment, including data representing a road network, the locations of charging / refuel stations, shovels, crushers and senice bays. Such data may originate with a mine management computing device 135, tasked with real-time scheduling of the movement of vehicles and other equipment in the mining environment. The road network data may comprise data reflecting individual road segments, each of which may have certain properties encoded in the road network data.

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[0065] For example, the road network data may be a data structure that includes sub-structures, each corresponding to a road segment. Each road segment structure may have a data record with data representing endpoints of the segment, the identities of intersecting road segments, segment length, speed limits or other rules governing speed, rules including or disallowing certain vehicle types, slope, segment condition, hazard conditions and the presence of a trolley line. Geographic mine data may be received from a mine vehicle scheduling and management system including its own computing device. Such a system serves to supply task assignments to mine haul trucks, and otherwise control or direct the movement of vehicles and material within the mine environment.

[0066] Computing devices implementing the methods described below may also receive data from sensors or other computing devices, which data is reflective of mine output or energy inputs to the mine. For example, computing devices implementing methods according to the invention may receive data from crushers, indicating the amount of material being processed on a real-time basis.

[0067] Computing devices implementing the methods described below may also receive data from internal and external sources regarding electricity usage. For example, computing devices may receive data from sensors or from an electric utility supplying a mine environment regarding instantaneous and time-averaged electricity usage by the mine and / or rate information. Similar data may be received from internal electrical power generation or storage facilities, like generators or solar installations. Additionally, computing devices implementing the methods described below may receive data from charging stations indicating the presence or absence of vehicles, electricity output, and battery charge state and voltage.

[0068] It is contemplated that the data used for modeling that is described throughout this disclosure may be collected from a mine environment in real time, a model constructed, predictions made on the basis of the model, and then adjustments can be made to mine operation in real time to optimize on figures on merit such as production, energy cost, emissions targets, or combinations of the above. The adjustments may include generating and altering schedules of vehicle movement and tasks for vehicles and equipment to engage in in the mine.

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[0071] Attorney Docket No. 122169.00339

[0072] Inventive embodiments provide mines with the ability to optimize their load and haul operations to satisfy mine plan targets and emissions reduction targets. This is the first system to provide the following features all in one system that includes the following features:

[0073] • Adherence to mine plan targets as well as emissions reduction goals;

[0074] • Consideration of scheduled activities such as maintenance, breaks, road closures, blasts;

[0075] • Common data model which handles haulage vehicles and re-energizing infrastructure regardless of power technology' being used;

[0076] • Continuous re-optimization based on real-time events.

[0077] It is helpful to describe, at a high level, the steps taken by the various systems that will be described to govern the movement of vehicles, equipment and material in a mine environment. This discussion will occur in reference to the flow diagram of FIG. 8, which depicts various software processes making vehicle and equipment scheduling determinations upon receiving various inputs provided by sensors and / or data stores, as shown.

[0078] The fundamental figure of merit that mine management seeks to maximize is productivity, which is defined by material throughput, that is, the amount of material that is extracted and processed at the mine, or more specifically, the rate of material extraction and processing of material from which suitable finished metal may be extracted. This requires consideration of the amount of and rate at which material is extracted and moved, and the type or grade of the material. Thus, mine management begins with the establishment of a mine plan, or a mine production plan. The time horizon for the mine production plan is typically one shift, so such a plan may be referred to as a shift plan. The shift plan specifies the amount of material, of various grades, that ideally will be moved to target locations (e.g., crushers, stockpiles, etc.) over the course of one shift. The shift plan specifies the total mine production target, which is the most fundamental measure of mine productivity, as well as the processing target locations that are participating in mine production (e.g., the active crushers, etc.). In establishing the shift plan, account is taken of the throughput of the processing equipment, and the minimum total material grade that must be supplied to that equipment. Thus, as shown in FIG. 8, the first step in mine management is to set per shift mine plan targets, which are selected with

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[0081] Attorney Docket No. 122169.00339 knowledge of the material targets, the rate at which those targets can process material, the necessary grade of the material, and the total desired processing rate / amount. The result of this process is a list of material targets and the total amount of material and grade to be delivered to each target over the course of the shift.

[0082] Once the system has determined how much material of what type must be delivered to each material target, a mine management method according to the inventive embodiment next must determine where this material will come from. The sources of mined materials are shovels that are working at faces and / or stockpiles of previously staged materials. Each of these sources will have material of a different grade, and indeed, a given face being dug by a shovel may have different grades of material accessible by the shovel throughout the shift. As seen in FIG. 8, a second mine management step is to assign to each source of material the amounts and grades of material that will be supplied by each source during the course of the shift to fill the material needs of the various targets during the shift. The result of this process is a requirement, for each source (e.g., for each shovel), of the amount of material the shovel must supply, and the grade of that material, during the course of a shift.

[0083] In the inventive embodiments, a software optimization engine referred to herein as the shift plan optimizer or “SPO” generates an optimal schedule to supply the amounts and grades of material to the various target locations from the various material sources. This optimization process has, as constraints, a total material throughput (i.e., the target total mine productivity), the material to be extracted by the various sources, as well as a geographic database including a road network model. One component of this optimization is to preferentially source material from sources that are closest to the material targets. The result of the SPO process is a schedule for each of the sources during the shift, e. g. , “load this much material during this time window”, “load this much material during this subsequent time window” etc. Movements of the equipment may also be incorporated into the optimum schedule. This may occur when a shovel must move to a different area on a working face or a different area in a stockpile to access material of a different grade.

[0084] The source equipment schedule is a data structure that may be divided into one or more source equipment missions. Each mission data structure will include an estimated start time and an estimated end time. The SPO may also add a safety factor (i.e., a scale factor) to the

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[0087] Attorney Docket No. 122169.00339 estimated end time to account for unforeseen events, such as unexpected equipment down time or operator breaks.

[0088] At this point in the process flow, the system has determined scheduling for the equipment to be sourcing the material. It must now schedule transport equipment to move the material being shoveled at the material sources to the targets. This task is handled by a primary schedule software process, which executes a second optimization routine to build a schedule of missions for haul trucks over a predetermined time frame. In one embodiment the primary schedule builds an optimum schedule of the next n missions (e.g., five) for each vehicle, where a mission is a next task or destination (e.g., go to source 3 and receive load of material, and then go to target 1 and dump). The primary schedule takes as its inputs the source equipment schedule, the road network, the target schedule, and contemporaneous information about the state of the mine and vehicles therein that is supplied in real time over a sensor network. For example, a vehicle equipped with a GPS sensor may report that it has stopped moving, or an equipment operator may report a flat tire or accident, in which case the primary scheduler can generate a new optimized schedule that does not rely on the disabled or inoperable vehicle. The result of this schedule revision is a new set of missions that are pushed out to vehicle operators over the fleet management system process, which handles interface with the haul trucks.

[0089] Embodiments of mine management systems according to the invention enhance this two-stage optimization process with additional data, permitting additional schedule revisions. As noted above, the Primary Scheduler process takes as its input static targets, but also, contemporaneous mine status information. There is additional data that may be relevant to mine management that is accounted for by an LAO system”). The LAO system takes into account scheduled events occurring in the future that may impact mine productivity. These scheduled events may include planned refueling / recharge stops for vehicles, planned operator breaks, areas of the mine that are scheduled to be closed due to road maintenance, blasting etc. The LAO system receives updates regarding these predictable future events, and then works with the Primary Schedule to generate schedule revisions that account for the future operation of the time over one or more time horizons (e.g., the next hour, the next two hours, etc.). In practice, this process may involve receiving the default vehicle movement schedule from the primary scheduler, performing one or more simulations of material equipment over the course of the given timeframe using the default schedule and accounting for one or more planned

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[0092] Attorney Docket No. 122169.00339 events, and then suggesting a revised optimum schedule on the basis of the simulations. Here the term “simulations” is not intended to be limiting and be something as simple as altering the schedule in whatever way is required to maintain scheduled production targets in view of the planned event. More complex, monte carlo type simulations are described below.

[0093] Thus, the LAO engine takes as its inputs the current schedule being generated by the Primary Scheduler, which will generally include next vehicle destinations over a predetermined timeframe (e.g., next hour), or the next n missions (e.g., the next five destinations and activities), as well as information regarding future planned events that may impact vehicle availability. The LAO engine may also receive as input data regarding contemporaneous mine conditions (e.g., vehicle location, speed, heading, etc.). With this input, the LAO engine may generate schedule revisions that are passed to the FMS for distribution to vehicles as revised missions (a process illustrated in the flow diagram of FIG. 7).

[0094] In some cases, the LAO may use the same optimization routine as the primary scheduler, but may include additional parameters such as the scheduled events discussed above. In other cases, the LAO may use a different optimization routine that optimizes vehicle missions on the basis of parameters other than or in addition to meeting mine plan production targets. For example, the LAO optimization engine may conduct its own optimization on the basis of nonproduction constrained parameters, such as environmental parameters. For example, the LAO engine may optimize vehicle missions to minimize carbon emissions, or preferably, include some total carbon emission target in a combined merit function for mine performance that also includes mine throughput or productivity. This process may involve assigning BEV vehicles to certain routes, even if such assignment causes a decrease in productivity.

[0095] FIG. 2 illustrates a conceptual arrangement for computational tasks and the flow of data objects in accordance with a first embodiment. In the arrangement of FIG. 2, the illustrated blocks are software processes or modules (as described below) running on one or more computing devices and / or data objects stored in one or more databases. The system is agnostic as to the physical location of processors executing the processes to be described, but in a preferred embodiment, modules 210, 205 and 225 are located in a central mine management server and module 215 is a process running on a remote client device located in a vehicle or other mine equipment. One or more of the modules depicted in FIG. 2 may perform the steps set forth in the process flow diagram of FIG. 8.

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[0098] Attorney Docket No. 122169.00339

[0099] The system of FIG. 2 includes an FMS module 210, which handles communication with agents or clients (e.g., 215), such as computing devices and audio or visual displays operating in associated with vehicles such as haul trucks, other equipment (e.g., shovels or crushers) and other facilities within an open pit mine such as tire shops and refueling stations. The FMS module 210 may receive time varying information from agents such as their location (as measured by on-board GPS receivers located on vehicles), fuel level, load weight, speed, tire temperature and pressure, operator information (such as time since last break, time since coming on shift, time until shift end, and biometric data collected by operator monitoring systems). This information may be received as wired or wireless signals (like the other communication described herein) over wired or wireless networks. This information received from vehicles may be passed to the primary schedule module 205 for use in the optimization and scheduling tasks to be described. The FMS module 210 may also send navigation instructions and other information to a visual or auditory information output system (e.g., a visual display), with which an operator of the vehicle may interface.

[0100] The system also includes a primary schedule module 205 in signal communication with a data store 220. The data store may include data regarding the operation of the mine, including a mine model which contains geographic information about the mine layout such as the location of faces and shovels, the road network, topographical information (e.g., roadway slopes), the location of refueling / recharging stations and maintenance facilities, the locations of dumps, stockpiles and crushers, known navigation hazards, etc. The data store 220 may also include scheduling information, such as a predetermined schedule under which vehicles or other equipment are to engage in certain tasks like pre-scheduled maintenance. This schedule may have been generated by the primary scheduling module 205 as will be described. The schedule may also include information regarding operator breaks or shift changes. The data store 220 may also include vehicle models, which are model representations of vehicle operational parameters such as the fuel / energy use of vehicles and their carbon emissions as a function of work. Different vehicle models may be provided for different types of vehicles, such as for both conventional diesel-electric vehicles and for battery electric vehicles (BEVs). Vehicle models may be used to predict fuel or battery energy use (and recovery through regenerative braking) as a vehicle goes about various tasks in the model mine such as by moving from point to point in the mine under various load conditions along road routes specified in the mine model.

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[0102] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0103] Attorney Docket No. 122169.00339

[0104] The primary scheduling module 205 is a software module that is capable of modeling the movement of vehicles, other equipment, and material in a modeled mine environment (i.e., a modeled twin of an actual mine site). Vehicle and material movement may be modeled over different future time periods as a predictive model. The primary schedule module 205 anticipates events such as refueling / recharging stops (determined by predicting fuel or battery state using the vehicle models and predicted movements of a vehicle) and scheduled downtime (i.e., for vehicle maintenance events), as well as events such as road closures, operator breaks and shift changes for vehicles in the predictive model. The primary schedule module 205 may compute certain output parameters over specified timeframes based on the predictive model, for example, mine output (measured in amount of material processed), value (which incorporates real-time data regarding finished material price), energy used, energy cost over the specified time frame, and emissions (e.g., carbon emissions) generated by mine operation. One or more of these factors may be used as a merit function to optimize vehicle and material movement by performing iterative computations of the value of a merit function as different hypothetical cases of vehicle movement are iteratively modeled. This optimization routine may be used to generate or alter preexisting vehicle movement schedules, to assign missions to particular vehicles, or to change preexisting vehicle assignments. In certain embodiments, a schedule, or one or more schedules, is selected on the basis of satisfying a shift mine plan that specifies an amount and type of material to be supplied to a plurality of material target destinations over the course of a shift. One output of the primary schedule module 205 is a confirmed schedule, which is a default schedule for vehicle movement over a predetermined time frame, where the confirmed schedule has been built according to optimization performed on a mine model over a predetermined time frame.

[0105] Embodying systems also include a mission manager module 225. The mission manager module mediates between the FMS 210 and the primary schedule module 205 to pass vehicle assignments (i.e., missions) to the FMS for forwarding to vehicles at the appropriate time, such as when a vehicle or the FMS requests a next mission. The mission manager module may also receive mission requests from external sources (e.g., a third-party control process, or a vehicle), and query the primary schedule module to determine whether the requests should be implemented. In these cases, the primary schedule module 205 may compute the impact of the requested mission on the operative merit function, apply a threshold, and determine whether to alter the confirmed schedule to implement the requested mission.

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[0107] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0108] Attorney Docket No. 122169.00339

[0109] In certain cases, the system of FIG. 2 is used to generate a default schedule of vehicle movement in a mine over a long timeframe, preferably several hours, a work shift, 12 hours, etc. This is referred to below as “snapshot” mode, and the future mine behavior is scheduled on the basis of that state of the mine when the optimization is started. This may be the beginning of a shift. In building the optimized schedule, the system not only models feasible movements of vehicles and material, but it also takes into account other known or potential events that are likely to occur over the course of the predetermined time frame for which the schedule is being built. These known events may include events like road closures, scheduled vehicle maintenance, shift changes, operator breaks or other down-town events. Additionally, the system may accept input from other scheduling systems in building the optimized schedule. A replenish engine may be queried and respond with needed recharge / refuel missions that are accounted for in building the schedule. The refuel / replenish engine may use the vehicle models in the datastore and feasible movement patterns to predict fuel / energy usage for the vehicles over time and predict the points in time and the positions of the vehicles at and near the time when they should be refueled / recharge. The system thus predicts the timing for these refuel / recharge events and determines optimal routing for the vehicles to refuel / charge stations (including trolley lines) and builds these events into the default schedule.

[0110] In certain inventive embodiments, optimization is alternatively or additionally performed on a short time horizon. Rather than building an optimized schedule based on long term predictions starting from a start-of-shift condition, in this “real time”, the current realtime state of the mine is examined, and optimal short-term activity is computed by optimizing on permutations from the current state of the mine. This enables the consideration of real-time events as they occur, and real time optimization may run on a rolling or high frequency periodic basis. This process results in a set of short-term assignments, i.e., a second schedule (as contrasted with the long-term default schedule) which is a short-term schedule for operating vehicles. This schedule can be defined over a very short time horizon, i.e., containing as little as the optimal next mission for every vehicle operating at the time the snapshot optimization is performed. In some cases, the real-time optimization is run over a 2-hour future window, which results in about 5 assignments per truck. This process may be repeatedly performed on an ongoing basis, such that the system continuously is reoptimizing a short-term schedule based on current mine conditions, which may include unscheduled events such as road closures or vehicle breakdowns.

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[0112] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0113] Attorney Docket No. 122169.00339

[0114] The real time optimization process may interact with the long-term predictive schedules in a number of ways. For example, when the next assignments are based on the snapshot conflict with the scheduled next assignments in the default, long-term schedule, the long-term schedule may be adjusted to include the next assignments, which are being based on more up- to-date real time information. Additionally, real-time optimization may be triggered by triggering events such as vehicle break downs, other equipment failures, power interruptions, accidents and road closures. When a triggering event occurs, the system may perform a realtime optimization to generate a new list of next assignments, pushed out to the operating vehicles. A new long-term schedule may then be built that accounts for the changed condition of the mine. Additional details regarding how new mission assignments is pushed out to vehicles is provided in connection with the Figures, below.

[0115] In certain embodiments, the mission manager may alter a schedule set by the primary schedule module by performing an additional optimization on the basis of predicted events. That is to say, the primary schedule module may analyze the schedule produced by the primary schedule module, and model the ideal vehicle movements indicated by that schedule in view of known future events. In certain embodiments, this analysis may be triggered when the current time is within a predetermined amount of time of a known future events. Known future events are known events that may impact vehicle movement such as refueling / recharge missions, closures of portions of the road network, operator breaks, scheduled maintenance events (e.g., tire changes), and the like. By modeling the delay caused by these events, the mission manager may determine that the current schedule should be changed in some way, e.g., by rerouting vehicles to balance material flow from a set of shovels when it appears that one or more trucks are going to be inoperable at a current timeframe.

[0116] Additionally, the mission manager 225 may perform a re-optimization of vehicle movement on the basis of figures of merits other than mine production. In certain of the methods described above, the mine plan (the target productivity for the shift) is taken by the primary scheduler 205 as a hard constraint - the goal of the optimization routine is to generate a schedule that satisfies the mine production targets. In other embodiments, an optimum schedule is constructed under which this constraint is relaxed in favor of other figures of merit, such as energy consumption or carbon emission. This will typically be done by imposing another constraint on the optimization routine of a carbon ceiling, carbon emission rate ceiling or an energy cost, and then mine productivity will be maximized under that constraint. Thus,

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[0118] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0119] Attorney Docket No. 122169.00339 in these embodiments, the mission manager 225 may set a flag and feed these additional constraints to the primary scheduler 205 for generation of a new optimal schedule, which is then pushed out to the vehicles via the FMS.

[0120] FIG. 3 illustrates exemplary data inputs to, constraints used in the look-ahead optimization process, and the outputs from the primary scheduler process 205 discussed above with respect to FIG. 2. Exemplary inputs include Master Data (stored in and retrieved from, for example, Data Store 220), which may include the vehicle models discussed above, geographic mine information including the road network, location of fixed facilities, and topographical data, and specifications for refueling / recharge stations. The optimization routine may also take as inputs short term mine targets, which may be evaluated against outputs generated by the look- ahead prediction process. These targets may include figures of merit like production tonnage, or other factors such as CO2 emissions or energy use. Other data regarding defined scheduling events may be considered, such as scheduled temporary movement restrictions (e.g., which might be necessitated by blasting work or road maintenance in an area of the time), speed limits, road closures, etc., and operator breaks, shift changes, and scheduled vehicle / equipment maintenance events. This semi-static data may be supplemented by realtime data, collected from the FMS and from individual agents (vehicles) such as current and projected road state / conditions, current and projected vehicle state or conditions, and current or projected locked vehicle paths (e.g., paths which a vehicle must or must not travel such as a path associated with a trolley).

[0121] Referring still to FIG. 3, objections and constraints used by the LAO process (i.e., usable elements of the merit function used for optimization) include mine objections, constraints and the prediction time horizon. Objectives may include maximizing overall production, minimizing deviation from the mine plan, minimizing non-productive vehicle stops or idling, or minimizing CO2 emissions. Combinations of these factors may also be considered. Constraints (which are used to limit the optimization variable search space) may include material blending targets, blast sequences, service requirements, spatial limits like speed limits, and operational rules as shown.

[0122] The optimization routine may be run over any predetermine time period, but in preferred arrangements, it is contemplated that an optimization will be run and a schedule built on the basis of a snap shot of the time (time t=0) on the basis of predictions over a long-term

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[0124] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0125] Attorney Docket No. 122169.00339 period (e.g., 8-12 hours), and another optimization run and another schedule built on the basis of predictions made over a shorter time period (e.g., the next 2 hours).

[0126] FIG. 3 shows various outputs from the optimization process. These may include truck assignments and schedules showing future truck assignments, paths for vehicles to travel, and ETAs to various waypoints.

[0127] As shown in FIG. 3, the various sources of the input data may be consolidated into a common data model representation of a snapshot of the condition of the mine at a given point in time. There are at least two modes of operation for the LAO system: snapshot mode and real- time mode. In snapshot mode, a static optimization over the planning horizon (typically to the end of the current shift, up to 12 hours into the future) is performed. In real-tie mode, the system is continually listening to real-time events and re-optimizing based on the latest data. In this mode, optimal assignments are generated for the next two hours, which typically results in five missions per truck.

[0128] Referring now to FIG. 4, there is shown a process flow diagram that describes the lifecycle of each truck assignment / mission when the primary scheduler is operating in realtime mode. The primary scheduler subsystem considers the objections and constraints to create a “confirmed” mission schedule. As stated before, this can be a long-term or a short-term schedule that covers a predetermine time period into the future, for example, the next two hours. The mission manager subsystem maintains a database of the scheduled missions and sends them to the FMS for passing to the vehicles at the appropriate time (e.g. , when the FMS receives an indication from the vehicle / agent that the last mission is complete). Missions created by the primary scheduler start in a “confirmed” status (i.e., are the default missions that will be implemented unless real-time re-optimization indicates a change). Missions may also be submitted by external engines (e.g., an operator may request a specific mission). When this occurs, these missions are given a “submitted” status, and are only considered for scheduling if the primary scheduler determines they are feasible. This may be the case when predictive simulations including the requested mission do not change the merit function of the optimization time frame by more than a predetermined threshold amount.

[0129] Referring now to FIGs. 5a-5b, there is shown a divided flow diagram (with the method illustrated in FIG. 5a carrying over to FIG. 5b) illustrating the logic used by the mission

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[0131] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0132] Attorney Docket No. 122169.00339 manager of FIG. 2 to decide when to send a mission to the FMS for routing to a truck. The method of FIGs. 5a-5b shows the general flow for updating the schedules for all of the agents (i.e., the haul trucks). A method for updating the schedule for a single agent is shown in the flow diagram of FIGs. 6a-6c.

[0133] Referring now to FIG. 7, there is shown other components of an optimization ecosystem that interact with the LAO service engine running in the primary scheduler module and the mission manager when the system is running in real-time mode.

[0134] Some of the functional units described in this specification have been labeled as modules in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like.

[0135] Modules may also be implemented in software for execution by various types of processors. An identified module of executable code may, for example, comprise one or more physical or logical blocks of computer instructions which may, for example, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module.

[0136] Indeed, a module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.

[0137] Reference to a signal bearing medium may take any form capable of generating a signal, causing a signal to be generated, or causing execution of a program of machine-readable

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[0139] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0140] Attorney Docket No. 122169.00339 instructions on a digital processing apparatus. A signal bearing medium may be embodied by a transmission line, a compact disk, digital-video disk, a magnetic tape, a Bernoulli drive, a magnetic disk, punch card, flash memory, integrated circuits, or other digital processing apparatus memory device.

[0141] The schematic flow chart diagrams included are generally set forth as logical flow chart diagrams. As such, the depicted order and labeled steps are indicative of one embodiment of the presented method. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more steps, or portions thereof, of the illustrated method. Additionally, the format and symbols employed are provided to explain the logical steps of the method and are understood not to limit the scope of the method. Although various arrow types and line types may be employed in the flow chart diagrams, they are understood not to limit the scope of the corresponding method. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the method. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted method. Additionally, the order in which a particular method occurs may or may not strictly adhere to the order of the corresponding steps shown.

[0142] Furthermore, the described features, structures, or characteristics of the invention may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention.

[0143] This invention is described in preferred embodiments in the following description with reference to the Figures, in which like numbers represent the same or similar elements.

[0144] Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present

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[0146] QB'\98765271.1 PCT / US25 / 48751 30 September 2025 (30.09.2025)

[0147] Attorney Docket No. 122169.00339 invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0148] Where, “data storage media,” or “computer readable media” is used, Applicants mean an information storage medium in combination with the hardware, firmware, and / or software, needed to write information to, and read information from, that information storage medium. In certain embodiments, the information storage medium comprises a magnetic information storage medium, such as and without limitation, a magnetic disk, magnetic tape, and the like. In certain embodiments, the information storage medium comprises an optical information storage medium, such as and without limitation, a CD, DVD (Digital Versatile Disk), HD- DVD (High-Definition DVD), BD (Blu-Ray Disk) and the like. In certain embodiments, the information storage medium comprises an electronic information storage medium, such as and without limitation, a PROM, EPROM, EEPROM, Flash PROM, compact flash, smart media, and the like. In certain embodiments, the information storage medium comprises a holographic information storage medium.

[0149] Reference is made throughout this specification to “signals.” Signals can be any time varying electromagnetic waveforms, whether or not encoded with recoverable information. Signals, within the scope of this specification, can be modulated, or not, according to any modulation or encoding scheme. Additionally, any Fourier component of a signal, or combination of Fourier components, should be considered itself a signal as that term is used throughout this specification.

[0150] While one or more embodiments of the present invention have been illustrated in detail, the skilled artisan will appreciate that modifications and adaptations to those embodiments may be made without departing from the scope of the present invention as set forth in the following claims.

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[0152] QB'\98765271.1

Claims

PCT / US25 / 48751 30 September 2025 (30.09.2025)Attorney Docket No. 122169.00339CLAIMSThe invention claimed is:

1. A method for directing and controlling the movement of vehicles in an open pit mine, comprising: receiving data regarding a contemporaneous state of the mine, including the positions of vehicles, shovels and crushers, a geographical model of a mine layout, the geographical model including data representing a road network, and a plurality of vehicle models each model reflecting performance characteristics of a vehicles, generating a set of feasible future movements of vehicles and materials in the mine based on the data regarding a contemporaneous state of the mine over a predetermined timeframe, wherein each of the feasible movements in the set is generated subject to a set of constraints; calculating, for each feasible movement in the set, a merit function reflecting the performance of the mine; selecting one of the set of feasible future movements on the basis of the merit function calculation; generating a vehicle schedule on the basis of the selected set of feasible future movements; transmitting to one or more mine vehicles movement instructions based on the vehicle schedule, and moving the one or more vehicles in accordance with the movement instructions.

2. The method of claim 1 , wherein the merit function reflecting the performance of the mine comprises a shift mine plan comprising a one or more material production targets.

3. The method of claim 2, wherein the one or more material production targets comprise amounts and grades of material to be delivered to one or more crushers or stockpiles.23QB'\98765271.1PCT / US25 / 48751 30 September 2025 (30.09.2025)Attorney Docket No. 122169.003394. The method of claim 1, wherein the merit function reflecting the performance of the mine comprises one or more environmental targets.

5. The method of claim 4, wherein the one or more environmental targets comprise carbon emissions targets.

6. The method of claim 5, wherein the merit function comprises a weighted average of environmental targets and material production targets.

7. A method for directing and controlling the movement of vehicles in an open pit mine, comprising: receiving a production target specifying amounts of mined material to be provided to one or more targets over the course of a timeframe; determining amounts of material to be mined by one or more sources over the course of the timeframe in order to achieve the production target; computing a contemporaneous schedule specifying the movement of one or more vehicles over the course of a subset of the time frame between the sources and sufficient to transport material at a rate sufficient to achieve the production targets, wherein the computation considers the positions sources and targets, a geographical model of a mine layout, the geographical model including data representing a road network, and a plurality of vehicle models each model reflecting performance characteristics of a vehicles; transmitting to one or more mine vehicles movement instructions based on the contemporaneous schedule; moving one or more vehicles in accordance with the movement instructions; receiving data specifying one or more scheduled events that impact vehicular movement during the subset of the time frame; revising the schedule on the basis of the one or more scheduled events; transmitting to the one or more mine vehicles revised movement instructions based on the revised schedule, and24QB'\98765271.1PCT / US25 / 48751 30 September 2025 (30.09.2025)Attorney Docket No. 122169.00339 moving one or more vehicles in accordance with the revised movement instructions.

8. The method of claim 7, wherein computing a contemporaneous schedule specifying the movement of one or more vehicles over the course of a subset of the time frame comprises computing the next n movements for each vehicle, wherein n represents an expected number of movements a vehicle may make during the subset of the time frame.

9. The method of claim 7, wherein computing a contemporaneous schedule specifying the movement of one or more vehicles over the course of a subset of the time frame comprises generating a set of feasible future movements of vehicles and materials in the mine over the subset of the time frame based on received data regarding a contemporaneous state of the mine over a predetermined timeframe, wherein each of the feasible movements in the set is generated subject to a set of constraints; calculating, for each feasible movement in the set, a merit function reflecting the performance of the mine; selecting one of the set of feasible future movements on the basis of the merit function calculation and generating a contemporaneous schedule on the basis of the selected set of feasible future movements.

10. The method of claim 9, wherein the merit function reflecting the performance of the mine comprises a shift mine plan comprising a one or more material production targets.

11. The method of claim 10, wherein the one or more material production targets comprise amounts and grades of material to be delivered to one or more crushers or stockpiles.25QB'\98765271.1PCT / US25 / 48751 30 September 2025 (30.09.2025)Attorney Docket No. 122169.0033912. The method of claim 9, wherein the merit function reflecting the performance of the mine comprises one or more environmental targets.

13. The method of claim 12, wherein the one or more environmental targets comprise carbon emissions targets.

14. The method of claim 10, wherein the merit function comprises a weighted average of environmental targets and material production targets.

15. The method of claim 7, wherein the one or more scheduled events comprise one or more of vehicle refuel or recharge stops.

16. The method of claim 7, wherein the one or more scheduled events comprise closure of a portion of the road network.

17. The method of claim 7, wherein the one or more scheduled events comprise vehicle operator breaks.

18. The method of claim 7, wherein the one or more scheduled events comprise scheduled vehicle mechanical maintenance.26QB'\98765271.1

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