CA3220392A1 - Vehicle allocation system - Google Patents

Abstract

Mine sites include various different types and ages of vehicles, which makes the dispatcher's job of allocating the various vehicles very difficult in order to maximize production of material. In particular, different vehicles and different operators, handle route conditions, such as grade and curvature, differently. A swarm-based truck allocation system provided on each vehicle is able to independently provide suggestions for each vehicle's allocation based on the vehicle's operating capabilities and the operating capabilities and status of other vehicles passing thereby. The status and operating capabilities of the vehicles may be communicated to each other via an inexpensive communication network, e.g. Bluetooth, when the vehicles are in close proximity.

Description

VEHICLE ALLOCATION SYSTEM
TECHNICAL FIELD
[0001] The present disclosure relates to a vehicle allocation system, and in particular to a swarm-based vehicle allocation system, in which each vehicle receives information from other passing vehicles and determines which individual task to accept in order to achieve a global goal.
BACKGROUND

[0002] An open pit mining operation requires a plurality of shovels for digging up the raw material, a plurality of loaders for loading the raw material onto trucks, and a plurality of trucks for transporting spoils to a spoils pile and the raw material to a breaker for initial processing of the raw material.

[0003] A dispatcher is the quarterback of a mine site, responsible for impacting multiple key performance indicators (KPI's) throughout day-to-day operations. Typically, the dispatcher is trying to monitor several different computer screens and several different 3r1 party applications, while communicating with multiple phones and radios.

[0004] A pit supervisor is often out in the field managing the operation on the ground. For large mining cites there may be several pit supervisors managing different sections of the mining cite.
Each pit supervisor share some KPI' s with dispatchers but they are also accountable for things like safety and personnel issues in their section of the pit. Each pit supervisor may drive a vehicle around the mine cite, and is often responsible for geographically separate equipment. Typically, each pit supervisor has access to a mobile phone and sometimes a laptop in the vehicle, but needs to pay attention to their surroundings at all times.

[0005] Many mine sites include various different types and ages of vehicles, which makes the dispatcher's job of allocating the various vehicles, e.g. trucks, to maximize production very difficult. In particular, different vehicles and/or different operators, handle route conditions, e.g.
grade and curvature, differently. Moreover, some vehicles, e.g. trucks, handle certain route conditions, e.g. grade, incrementally different, while other vehicles handle other conditions, e.g.
curvature, substantially different.

Date Recue/Date Received 2023-11-17

[0006] Swann describes a self-organizing system that is observed in nature, most commonly in birds, ants, and bees, in which individual agents produce an "intelligent"
global behavior despite the absence of a centralized system that would guide individual behavior.

[0007] An object of the present disclosure is to provide a system for allocating vehicles in an industrial operation and providing vehicle allocation based on a vehicle allocating system, e.g. a swami-based system.
SUMMARY

[0008] Accordingly, a first exemplary method includes a method of determining vehicle allocation in a mining operation performed by a plurality of trucks servicing a plurality of shovels, the method comprising for each truck of the plurality of trucks:

[0009] a) receiving a plurality of individual tasks to be performed by the plurality of trucks relating to collecting material from the plurality of shovels and delivering the material to an endpoint location to achieve a global task;

[0010] b) receiving task information for each individual task relating to accomplishing the global task;

[0011] c) collecting truck information and updated task information from other trucks of the plurality of trucks when the other trucks are in close proximity; and

[0012] d) determining which of the plurality of individual tasks to perform, using a vehicle control processor executing an swarm algorithm stored on non-transitory memory, based on the truck information, the task information, the updated task information, and the global task.

[0013] An exemplary system includes a system of determining vehicle allocation in a mining operation performed by a plurality of trucks servicing a plurality of shovels, the system comprising:

[0014] a vehicle control processor; and Date Recue/Date Received 2023-11-17

[0015] a non-transitory memory including computer instructions, which when executed by the vehicle control processor is configured to:

[0016] a) receive a plurality of individual tasks to be performed by the plurality of trucks relating to collecting material from the plurality of shovels and delivering the material to an endpoint location to achieve a global task;

[0017] b) receive task information for each individual task relating to accomplishing the global task;

[0018] c) collect truck information and updated task information from other trucks of the plurality of trucks when the other trucks are in close proximity; and

[0019] d) determine which of the plurality of individual tasks to perform, using the vehicle control processor executing an algorithm stored on non-transitory memory, based on the truck information, the task information, the updated task information, and the global task.
BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Some example embodiments will be described in greater detail with reference to the accompanying drawings, wherein:

[0021] FIG. 1 is a schematic diagram of a mining operation;

[0022] FIG. 2 is a schematic diagram of a truck cycle in the mining operation of FIG. 1;

[0023] FIG. 3 is a schematic diagram of an exemplary truck allocation system;

[0024] FIG. 4 illustrates a map of a mine site generated by the truck allocation system of FIG. 3;

[0025] FIG. 5 is an activity diagram detailing an exemplary method of the truck allocation system of FIG. 3; and

[0026] FIG. 6 illustrates a vehicle page generated by the truck allocation system.

Date Recue/Date Received 2023-11-17 DETAILED DESCRIPTION

[0027] While the present teachings are described in conjunction with various embodiments and examples, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives and equivalents, as will be appreciated by those of skill in the art.

[0028] With reference to FIGS. 1 and 2, an industrial operation, e.g. an open pit mining operation, utilizes a plurality of vehicles, for example a plurality of trucks 103 and a plurality of loading units, e.g. shovels 101 for digging up the raw material and loaders 102 for loading the raw material onto the trucks 103. The plurality of trucks 103 transport the raw material from an initial location, e.g.
the shovel 101, over a series of routes to one or more desired end-point locations 105, i.e. breakers for initial processing of the raw material and/or spoil piles. The following example relates to allocating trucks 103 in an open pit mining operation, but the allocation of any vehicle in any industrial operation travelling between initial locations to endpoint locations is within the scope of the invention.

[0029] With reference to FIG. 2, a typical cycle in a typical mining operation includes the shovels 101 piling the raw material in a remote mining location, while the loaders 102 load the raw material onto a plurality of material handling (dump) trucks 103. The trucks 103 travel routes between the loaders 102 and the end-point location 105, e.g. the breaker or the spoils pile, passing by each other and through common intersections. The trucks 103 may take time getting into position for loading, then they may bunch up during hauling, and end up queueing while waiting to dump their load at the end-point location 105. The trucks 103 may also take time getting into position for dumping.
After dumping their load, the trucks 103 then return via the route to the loading site, which again may result in bunching and queuing delays, thereby completing the cycle. All of these steps may cause a loss of production.

[0030] Swarm behavior can be applied to the vehicles, e.g. the trucks 103, to achieve a decentralized system in which a vehicle controller, e.g. processor, 20 in each of the trucks 103 may be given a global task to achieve and/or one or a plurality of individual tasks by a central truck allocation system 1. The vehicle controller 20, as part of a central, e.g.
swarm, truck allocation system 1, may execute computer software instructions relating to an individual truck allocation Date Recue/Date Received 2023-11-17 system algorithm stored on non-transitory memory 21 provide in each truck 103.
Each truck 103, i.e. the vehicle controller 20, may then optimize their behavior through vehicle-to-vehicle (V2V) communication with other trucks 103, e.g. passing by, and/or vehicle-to-everything (V2E) communication, e.g. stationary beacons 30 or a control center 120, without any new explicit directions from the central swarm truck allocation system 1. For the trucks 103 to be able to operate in a self-organized manner, they ideally require the global task that has to be achieved by the fleet of trucks 103 or simply operate on selecting from and completing the plurality of individual tasks as they arise. The global task may be, for example, a daily or weekly throughput that needs to be provided to the endpoint location 105, e.g. the mill, and the individual task may include travelling to a selected shovel 101 for loading and travelling to the endpoint location 105 for unloading.

[0031] With each of the vehicle controllers 20 of the entire fleet of trucks 103 having the global task, each individual vehicle controller 20 may also have access to vehicle information 25 including the truck capabilities, e.g. type of truck, size of truck, size of load, speed of truck, location of truck, loaded vs unloaded, fuel level and/or consumption, maintenance schedule, etc.
of their corresponding truck 103. The vehicle information 25, e.g. the truck capabilities for each truck 103, may be stored on the non-transitory memory 21 or on a separate non-transitory memory accessible by the corresponding vehicle controller 20. Furthermore, each vehicle controller 20 may receive from the central truck allocation system 1, for example from the V2V communication system or from the V2E communication system some sort of indicator of a stimulus that represents the intensity with which each shovel 101 needs trucks 103. The intensity with which each shovel 101 needs trucks 103 could be based on task information 35, e.g. factors such as the short-term prioritization of the ore-block being dug, loading times, length of truck queues etc. The hierarchal dispatch of a truck 103 to a shovel 101 would be decided by each individual truck allocation algorithm on each truck 103 that is configured to optimize and balance the intensity (task information 35) with which each shovel 101 requires trucking, and the capabilities (vehicle information 25) of the truck 103 that are most capable in the moment. For simplicity, this can be described as a bidding system, in which each vehicle controller 20 calculates a bid for each task, e.g. each shovel 101, based on the intensity of the task, and the capabilities of the truck 101 to determine how efficiently the truck 103 can perform each task, e.g. each shovel's needs. The vehicle controller 20 then determines which task to perform, e.g. which shovel to proceed to, by the highest bid.
Date Recue/Date Received 2023-11-17

[0032] Each truck 103 may have receivers and transmitters configured to connect to the V2V
communication system which may be embedded in level 5 autonomy. A collision avoidance system provided on each truck 103 will ensure that the trucks 103 have a spatial awareness of other trucks 103. This is an indirect form of communication that will rely on sensing the position and speed of other trucks 103. A more direct form of communication could occur during a mine delay, such as a roadblock, pause in operations for blast, etc. where the trucks 103 are able to communicate with one another and optimally re-route to their destination.
Whenever trucks 103 are in close proximity, e.g. up to 100 m, preferably up to 50 m, and even more preferably up to 10 m, such as when two trucks pass by each other or are behind one another, the trucks 103 may exchange vehicle information 25 and updated task information 35 via the V2V
communication system. The vehicle information 25 may include some or all of the capabilities, i.e. threshold, of each truck 103, the current individual task that each truck 103 is engaged in (if any), and the capabilities (vehicle information 25) and individual tasks (task information 35) of any or all of the trucks 103 that either truck has exchanged information with within a given period of time, e.g.
hour, day etc. Beacons 30, each with a receiver and a transmitter connected to the V2V
communication system, may also be positioned in selected locations, e.g.
intersections, in the mine for exchanging vehicle information 25 with trucks 103 that pass within a certain proximity, e.g. up to 100 m, preferably up to 50 m, and even more preferably up to 10 m.

[0033] The vehicle information 25 may also include historical operation data, such as one or more of Bluetooth, GPS, speed, tire temperature, fuel consumption, load weights, strut pressure, weather, and production data of material, e.g. bank cubic meters (BCM), for each vehicle, e.g.
shovels 101, loaders 102 and trucks 103, and each route over a predetermined period of time, e.g.
days to 60 days, preferably 10-30 days, and more preferably about 14-15 days, from the data collected and stored in non-transitory memory 3 and/or interim storage 6.

[0034] Each vehicle controller 20 or the swarm truck allocation system 1 may determine load and spot times for one or more of the shovels 101, the loaders 102 and the endpoint locations 105 from the historical operation data. Each of the load and spot times may be an average value based on the last predetermined number of days, e.g. adjusted to a percentage of the 365 day average based on the last 2-5 shift average actuals. The dispatcher 111, e.g. located in the control center 120, may Date Recue/Date Received 2023-11-17 adjust the load and spot time, if something has significantly changed in the digging conditions from the last 2-5 shifts.

[0035] The vehicle controllers 20 or the central truck allocation system 1 may estimate a cycle time on each potential route in the entire mine site for each truck 103 (and operator) based on the historical operation data, even though each truck 103 may not have ever travelled each route. The cycle time may also be included in the vehicle information to facilitate the task selection operation.
The vehicle controllers 20 or may include a machine learning model 7 that characterizes, e.g.
length, grade & curvature, each route that each truck 103 has travelled, and separates each route into different sections by characterization. Then the machine learning model 7 may evaluates how each truck 103 has performed on each different type of section. For example, the machine learning model 7 may aggregate each route into route segments, e.g. 50 m to 500 m, based on route features or complexity, such as one or more of: flat, uphill (10 < grade <20 ), extreme uphill (grade >
20 ), downhill (-10 < grade <-20 ), extreme downhill (grade <-20 ), straight, curved, extreme curved. The machine learning model 7 may define the length of each route segment until the route feature changes. Alternatively, the machine learning model 7 may separate each route into equal segments, e.g. 50 m, and provide each segment a segment feature based on the most significant of the route features. Then the machine learning model 7 may generate a travel time for each route segment, e.g. each 50 m segment, of the route based on corresponding segment features and then aggregates the segment times to get total travel time.

[0036] Then the machine learning model 7 may estimate how each truck 113 would perform on all of the routes in the mine, not just the routes that each truck 103 has travelled. The output from the machine learning model 7 may be a cycle time for each truck 103 (and operator) on each route.
The inputs to the machine learning model 7 may include one or more of the following: a) meters traveled in different grade categories, e.g. extreme uphill, uphill, flat, downhill, extreme downhill;
b) meters traveled in different curve categories, e.g. sharp curve, gentle curve, straight); c) road complexity, e.g. combination of route features; d) truck fleet, e.g. make, model and year; and e) horsepower of each truck 103. By using this methodology, the machine learning model 7 can estimate the average speed of each truck 103 and/or operator within 5% of the actual speed over different types of routes, e.g. uphill, downhill, shallow curve and sharp curve. For example, for a Date Recue/Date Received 2023-11-17 truck 103 travelling 30 km/h, the model prediction may be within 5% of actuals (from 28.3 to 31.7 km/h).

[0037] The shovels 101 and the endpoint locations 105, e.g. the mill or scrap pile, may also include a beacon 30 and/or an end-point controller 40 executing computer software instructions stored in non-transitory memory 41 along with a transmitter and/or receiver for communicating with the vehicle controllers 20 via the V2V communication system. The beacons 30 and/or the end-point controllers 40 may communicate vehicle information 25 and/or task information 35 including details relating to the current status of the individual tasks, e.g. one or more of the geometry of the ore body, the amount of mineral dug and/or loaded onto trucks 103, the amount of mineral left to dig and/or load, the current queue time for the trucks 103, the ore body priority in short term, and shovel loading time, etc. The beacons 30 and/or the end-point controllers 40 may also communicate details relating to the current status of the global task, e.g. up to date production amounts and/or time limits for individual tasks and global task.

[0038] In some exemplary embodiments, the fleet of trucks 103, including some or all remote control trucks 103, may be fitted with an inexpensive communication system, e.g. a Bluetooth' Low Energy (BLE) capabilities through which vehicle tracking will be conducted. The method of BLE localization does not require any GPS and is instead focused on the "beacons" 30 or Bluetooth signal emitters which may be placed on the trucks 103 and around the mine. The Bluetooth signals will be detected by the trucks 103, and a radius distance, e.g. up to 100 m, preferably up to 50 m, and even more preferably up to 10 m, will be established to the beacons 30.
Once the trucks 103 can detect multiple beacons 30, e.g. minimum 3 simultaneously, the radii will be tri-angulated to estimate the co-ordinates of each truck 103.

[0039] Accordingly, the methodology that may be applied to the trucks 103 where each shovel 101 becomes one of the individual tasks, and the stimuli, i.e. the intensity with which each individual task needs trucks 103 dispatched thereto, may depend on the vehicle information 25 and the task information 35 collected from other trucks 103, the beacons 30, the shovel 10, and the endpoint locations 105. The threshold may be defined for each truck 103 from the vehicle information 25, e.g. by the capabilities of each truck 103, such as the location of each truck 103, each truck's capacity, each truck's status (loaded vs unloaded), fuel schedule and maintenance Date Recue/Date Received 2023-11-17 schedule. Both the intensity and the threshold may further be defined by mathematical equations as these variables must be dynamic to reflect the changing environment and targets. The vehicle controller 20 for each truck 103 individually and independently determines a response or a rating for each individual task, and the task with the highest response or rating, i.e. highest probability of completion, to the stimuli based on their threshold will be the task, e.g. the shovel 101, to which the truck 103 that will be sent to complete the task.

[0040] With reference to FIG 3, the swarm truck allocation system 1 includes a main controller processor 2 executing computer instructions stored relating to a swarm allocation algorithm on non-transitory memory 3, e.g. local or cloud based. The swarm truck allocation system 1 may be configured to transmit the initial global and individual task information 35 to one or more of the vehicles and/or location, e.g. the trucks 103, the shovels 101, loaders 102 and the dump sites 105.
The swarm truck allocation system 1 may also be configured to receive the vehicle information 25 and the updated task information 35 that is transmitted from each vehicle and/or location, e.g.
beacons 30, shovels 101, loaders 102 and trucks 103, and dumping cites 105, via a suitable communication network for storage on non-transitory memory 3 or interim non-transitory memory storage 6. The truck information may include vehicle operation data, such as Bluetooth or GPS
location, speed, tire temperature, fuel consumption, load weights, strut pressure, and even weather, and production data of material, e.g. bank cubic meters (BCM), processed from one or more breakers or the trucks 103. The swarm truck allocation system 1 may include the machine learning model 7 executing a machine learning (Al) algorithm, e.g. XGBoost, incorporated herein by reference, for estimating the travel time on each potential route for each truck 103, and an optimizer system 8 executing an optimizer algorithm for determining truck allocation schemes to increase total BCM/hr for the mine site. The machine learning model 7 and the optimizer system 8 may be included in the same internal network or accessed from another network via a suitable communication network. The optimizer system 8 may comprise the Mixed-Integer Linear Programming (MILP) solver or some other suitable optimizer system. A swarm truck allocation web application 9 may also be provided to enable remote access and a graphic user interface (GUI) to the swarm truck allocation system 1 by external users, e.g. the dispatcher 111 and the supervisors 112, via a suitable network 10, e.g. the world wide web.

Date Recue/Date Received 2023-11-17

[0041] With reference to FIG. 4, a map 150 of the mine site may be generated by the swarm truck allocation system 1, with input from a user, with known mapping tools, e.g.
Google maps, and/or utilizing the operation data 4, e.g. Bluetooth or GPS positioning of the beacons 30, the shovels 101, the loaders 102, the trucks 103 and the endpoint locations 105. The map 150 may be a dynamic map, and include constantly updated locations D or dump sites 105, locations S of shovels 101, locations L of loaders 102, along with updated locations of trucks 103 identified by a number in a box. The routes used by the trucks 103 may also be indicated, e.g. with dark lines. The different pits, e.g. Lake, Eagle and Swift, may be generally identified with a geometric figure, e.g.
oval or circle.

[0042] With reference to FIG. 5, in an initial step 201, a) includes receiving a global task to be performed by the trucks 103, and/or receiving individual tasks, e.g. relating to collecting material from each shovel 101 from the swarm truck allocation system 1. The global task may be simply a production amount for a period of time, e.g. day, week or month, or the global task may have a more detailed task, such as production of a certain quality of ore or within certain environmental standards. The global task may not be necessary or may simply be the completion of one or more of the individual tasks.

[0043] The next step 202, relates to assigning an intensity to each shovel 103 relating to accomplishing the global task. The intensity with which the task needs trucks 103 dispatched thereto, may depend on the task information 35, such as one or more of the geometry of the ore body, quality of the ore, the ore body priority in short term, the shovel loading time, the truck queue time, etc.

[0044] In a third step 203, each truck 103 collects vehicle information 25 from one or more of:
other trucks 103, the beacons 30; and collects updated task information 35 from one or more of:
the endpoint locations 105, the other trucks 103, and the other vehicles, e.g.
shovels 101. This step may be continuously executed at all times as the trucks 103 travel around the mine site.

[0045] In final step 204, the individual vehicle controller 20 performs a task selection process to determine which individual task, e.g. which shovel 101 to travel to, the truck 103 will perform based on the task information 35 and the vehicle information 25. The entire process including the task selection step 204 may be repeated after the completion of the previous individual task or Date Recue/Date Received 2023-11-17 prior to completion of the individual task, e.g. during the unloading of the truck 103, during the arrival of the truck 103 at the endpoint location 105 or while travelling from the shovel 101 to the endpoint location 105, since the first three steps 201, 202 and 203 may be executed continuously, i.e. whenever, wherever and whatever, as the truck 103 performs each task.

[0046] An optional maintenance check step 205 may be included to ensure each truck 103 is within operating conditions, such as status of oil, brakes, fuel etc.

[0047] The swarm truck allocation system 1 allocates trucks 103 to shovel/dump combinations to increase productivity, e.g. maximize material moved. To do this, the truck allocation system 1, may use task information 35 from the loaders 101, the shovels 102 and the endpoint locations 105.
For example: how much the shovels 102 dig material, how fast each shovel 101 load trucks 103, and how much material the site is trying to move per shovel 102. The truck allocation system 1 may also use how much material each dump 105 is targeted to get per shift.
Limitations may be included as well if for example a shovel 102 only has a certain amount of material available to move and will run out during a shift. In this case, less trucks may be allocated by the truck allocation system 1, so that the target material is moved, but the extra trucks 103 can be used more efficiently elsewhere.

[0048] In step 204, the truck processor 20 executing software stored in the memory 21 in each truck 103 determines a truck rating for each of the plurality of trucks 103, using a machine learning system executing a machine learning algorithm, based on each truck's capabilities, the vehicle information 25 from other trucks 103 and the task information 35. The vehicle controller 20 provided on each truck 103 determines the truck rating for the task, whereby the truck 103 or trucks 103 with the highest ratings, i.e. highest probability of completing the task, to the stimuli based on their threshold, may be the truck 103 that proceeds to complete the task, e.g.
travels to receive a load at the shovel 101 and travel to dump the load at the endpoint location 105.

[0049] The individual tasks and the global task can be updated automatically at a given time period, e.g. each shift or day or week, or manually, as desired.

[0050] With reference to FIG. 6, during step 203, the swarm truck allocation system 1 may generate a vehicle page 301, which may illustrate the status of the vehicle, e.g. the trucks 103, the shovels Date Recue/Date Received 2023-11-17 101, the loaders 102 and the endpoint locations 105 etc., either all at once or via individual pages for each type of vehicle, e.g. truck page, accessed by tabs in the header. The swarm truck allocation system 1 may receive vehicle status inputs, e.g. from one or more of the following: the vehicle information 25, the task information 35, the operators of the vehicles 101, 102 and 103, and the dispatcher 111, so that the swarm truck allocation system 1 and/or the dispatcher 111 can observe which vehicles are available for the next truck allocation recommendation. The vehicle page 301 can includes a plurality of vehicle cards 302, each vehicle card 302 may provide the following one or more of the following data:

[0051] Operational status may include one or a plurality of status, e.g.
available, needs attention, and not available. The status may be color coded with the following values:
Green (Available) ¨
the Truck has no issues and is available for work; Yellow (Needs Attention) ¨
the Truck is not down, but has reasons for not being available; and Red (Not Available) ¨ the Truck is currently down with issues. The dispatcher 111 and/or the swarm truck allocation system 1 m ay need to get additional information.

[0052] Current status description: the dispatchers 111 and/or the swarm truck allocation system 1 can use this information to decide if the vehicle, e.g. truck 103, will be available soon or will not be available for an extended period of time. For example, Scheduled ¨ PM
means that the Truck is in the shop for Preventative Maintenance and is likely unavailable for the next shit, while Unscheduled Mechanical ¨ No Start is often a starting issue that can be fixed quickly by the field maintenance team.

[0053] The Vehicle page 301 may include an Available container 311 and Not Available container 312. The statuses of the vehicle cards 302 may be updated every five minutes. When the dispatcher 111 and/or the swarm truck allocation system 1 activates a Reset Trucks tab or button, the status of the vehicle cards 302 may be updated and may be sorted into the following containers according to their status: Available container 311, e . g . green and yellow vehicle cards 302; and Not Available container 312, e.g. red vehicle cards 302.

[0054] For each vehicle, e.g. truck 103, the swarm truck allocation system 1, via the web application 9, can display for the dispatcher 111 or any other interested party to view, the following data:

Date Recue/Date Received 2023-11-17

[0055] Status: the operational status of the truck 103. The status may be color-coded and corresponds to the color and status of the truck card 312. The short status description helps the dispatcher 111 and/or the swarm truck allocation system 1 identify the current condition.

[0056] Last pit: the last pit to which the vehicle, e.g. truck 103, was assigned. The assignment information helps to provide high-level context of where the vehicle, e.g.
truck 103, is currently.

[0057] Performance: the value that the machine learning model 7 applies to the vehicle, e.g.
truck 103, based on its historical performance when compared to similar vehicles, e.g. trucks 103.
The trucks 103 can be labeled as Above, Average, and Below Performers with the corresponding color coding.

[0058] Uptime: the expected amount of time during which the vehicle, e.g.
truck 103, will be operating in a shift, e.g. excluding things like lunch and coffee breaks. This metric is used to predict the expected number of cycles that the truck 103 can contribute to producing BCM.

[0059] Rated HP: the actual power of the engine in the truck 103. On rare occasions, the maintenance team may lower or raise the horsepower of a truck 103 for different reasons, for example, to reduce wear on engine parts.

[0060] Nominal BCM: the size of the truck box for each truck 103 for hauling material.

[0061] OP Cost: the operating cost to run the truck 103. The operating cost may be the sum price of everything that it takes for the truck 103 to operate, including fuel costs, driver wages, and wear on tires and parts. The machine learning model 7 may convert the dollar value to BCM, thus the value on the truck page 302 is 650 BCM/Hr. On the Settings page, the admins can adjust this value for every truck 103.

[0062] Available: the availability of the vehicle, e.g. truck 103, for the next recommendation.
The vehicle card 302 may be updated by toggling the available tab on or off.

[0063] Each vehicle card 302, i.e. each truck103, may be assigned by the truck allocation system 1 to one or a plurality of fleets, based on at least one of the make of truck 103, the model of truck 103, the age of truck 103, the horsepower of the truck 103, and the average cycle time of the truck 103.
The dispatcher 111 can sort Truck cards 103 by fleet or view all trucks 103.
Fleet mixing occurs Date Recue/Date Received 2023-11-17 when trucks 103 with different fleet assignments, e.g. cycle times, are placed on the same route.
Fleet mixing may be defined as not only trucks from a different fleet, but also as a pair of Trucks with different cycle times traveling the same routes to the same shovel 101.
Fleet mixing is usually avoided to ensure slow trucks 103 are not working the same routes as fast trucks 103, and thereby reducing productivity.

[0064] For example: the trucks 103 may be divided into three categories (fleets) based on their engine and rated horsepower, 1) Fast, e.g. Caterpillar (CAT) 797, CAT 794, and Komatsu (KOM) 930 Detroit 3000 HP; 2) Slow, e.g. KOM 980, HITACHI, and KOM 930 2700 HP; and 3) Regular, e.g. KOM 930E 3000 HP Cummins.

[0065] The foregoing description of one or more example embodiments has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the disclosure be limited not by this detailed description.

Date Recue/Date Received 2023-11-17

Claims (20)

WE CLAIM:

1. A method of determining vehicle allocation in an industrial operation perfomied by a plurality of trucks servicing a plurality of shovels, the method comprising for each truck of the plurality of trucks:
a) receiving a plurality of individual tasks to be performed by the plurality of trucks relating to collecting material from the plurality of shovels and delivering the material to an endpoint location to achieve a global task;
b) receiving task information for each individual task relating to accomplishing the global task;
c) collecting vehicle information and updated task information from other trucks of the plurality of trucks when the other trucks are in close proximity; and d) determining which of the plurality of individual tasks to perform, using a vehicle control processor executing an algorithm stored on non-transitory memory, based on the vehicle information, the task information, the updated task information, and the global task.

2. The method according to claim 1, wherein step c) includes collecting wireless communication signals including the vehicle information and/or the task information from wireless signal emitters positioned on the other trucks.

3. The method according to claim 1, wherein step c) includes collecting vehicle information and task information from beacons positioned in different locations in the mining operation.

4. The method according to claim 1, wherein step c) includes collecting task information from beacons at the endpoint locations.

5. The method according to claim 1, wherein the vehicle information comprises one or more of size of truck, speed of truck, location of truck, type of truck, load weight, fuel level, maintenance schedule, and current individual task.
Date Recue/Date Received 2023-11-17

6. The method according to claim 1, wherein the task information comprises one or more of the short-term prioritization of ore-block being dug, truck loading times, truck unloading times, length of loading truck queues, length of unloading truck queues, geometry of ore body, amount of the material dug, amount of the material loaded onto trucks, amount of the material left to dig, and amount of the material left to load.

7. The method according to claim 1, wherein the close proximity is within 50 m.

8. The method according to claim 1, further e) repeating steps c) to d).

9. The method according to claim 8, further comprising determining a maintenance status prior to step e).

10. The method according to claim 9, wherein the maintenance status includes fuel status.

11. A system of determining vehicle allocation in a mining operation performed by a plurality of trucks servicing a plurality of shovels, the system comprising:
a vehicle control processor; and a non-transitory memory including computer instructions, which when executed by the vehicle control processor is configured to:
a) receive a plurality of individual tasks to be performed by the plurality of trucks relating to collecting material from the plurality of shovels and delivering the material to an endpoint location to achieve a global task;
b) receive task information for each individual task relating to accomplishing the global task;
c) collect truck information and updated task information from other trucks of the plurality of trucks when the other trucks are in close proximity; and d) determine which of the plurality of individual tasks to perform, using the vehicle control processor executing an algorithm stored on non-transitory memory, based on the vehicle information, the task information, the updated task information, and the global task.

Date Recue/Date Received 2023-11-17

12. The system according to claim 11, further comprising a wireless communication signal emitter disposed on each of the plurality of trucks; wherein step c) includes collecting wireless signals including the vehicle information and/or the updated task information from the Bluetooth signal emitters positioned on the other trucks.

13. The system according to claim 11, further comprising beacons positioned in different locations in the mining operation configured to receive and transmit vehicle information and task information from the plurality of trucks passing thereby; wherein step c) includes collecting vehicle information and task information from the beacons.

14. The system according to claim 13, wherein step c) includes collecting task information from beacons at the endpoint locations.

15. The system according to claim 11, wherein the vehicle information comprises one or more of size of truck, speed of truck, location of truck, type of truck, load weight, fuel level, maintenance schedule, and current individual task.

16. The system according to claim 11, wherein the task information comprises one or more of the short-tenn prioritization of ore-block being dug, truck loading times, truck unloading times, length of loading truck queues, length of unloading truck queues, geometry of ore body, amount of the material dug, amount of the material loaded onto trucks, amount of the material left to dig, and amount of the material left to load.

17. The system according to claim 11, wherein the close proximity is within 50 m.

18. The system according to claim 11, further e) repeating steps c) to d).

19. The system according to claim 18, further comprising determining a maintenance status prior to step e).

Date Recue/Date Received 2023-11-17

20.
The system according to claim 19, wherein the maintenance status includes fuel status.

Date Recue/Date Received 2023-11-17