Description:DESCRIPTION
Technical Field
[001] This disclosure relates generally to non-linear programming optimization, and more particularly to a method and a system for resource allocation optimization in a mine.
Background
[002] Excavations in mines are generally carried out by drilling and blasting big rocks into smaller pieces. Excavator trucks are then deployed in such blast zones to load dumper trucks with broken rocks. Such processes require efficient management of resources to provide increased production at minimum costs. Effective management of time and resources can be of critical importance to mining companies.
[003] With an increase in computational capacity, dynamic optimization of resource allocation at a large scale has now become possible. However, currently, a single objective approach is used for allocation of resources. Conventional techniques fail to address multiple objectives through a single model with high accuracy. Further, such techniques perform allocation of equipment (such as, excavator trucks and dumper trucks) but not of other resources (such as, operators of the equipment). Further, allocation of equipment is carried out without considering previous location and break hours of the equipment.
[004] As such, there is a need in the present state of art for techniques enabling multi-stage and multi-objective dynamic optimization of resource allocation.
SUMMARY
[005] In one embodiment, a method for resource allocation optimization in a mine is disclosed. The method may include receiving production-related input data and cost-related input data corresponding to each of a plurality of resources. The production-related input data may include at least one target production output, blast zone inventory details, and a set of production-related operating parameters. The cost-related input data may include at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources. The plurality of resources may include a plurality of equipment and a plurality of operators. The method may further include determining a production objective function to obtain an optimized production output respect to the target production output through a mixed integer non-linear programming model based on the production-related input data and a set of production constraints. It should be noted that the set of production constraints may include blending constraints at a radical level. The method may further include determining a cost objective function to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model based on the cost-related input data, the set of production constraints, and a set of cost-related constraints. The method may further include performing a discrete event simulation for the plurality of resources based on the production-related input data and the cost-related input data, in accordance with the optimized production output and the optimized cost output to obtain a task allocation for each of the plurality of resources. The method may further include. dynamically updating the task allocation of one or more of the plurality of resources in near real-time based on the set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources, in accordance with the optimized production output and the optimized cost output.
[006] In another embodiment, a system for resource allocation optimization in a mine is disclosed. The system may include a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which when executed by the processor, cause the processor to receive production-related input data and cost-related input data corresponding to each of a plurality of resources. The production-related input data may include at least one target production output, blast zone inventory details, and a set of production-related operating parameters. The cost-related input data may include at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources. The plurality of resources may include a plurality of equipment and a plurality of operators. The processor instructions further cause the processor to determine a production objective function to obtain an optimized production output respect to the target production output through a mixed integer non-linear programming model based on the production-related input data and a set of production constraints. It should be noted that the set of production constraints may include blending constraints at a radical level. The processor instructions further cause the processor to determine a cost objective function to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model based on the cost-related input data, the set of production constraints, and a set of cost-related constraints. The processor instructions further cause the processor to perform a discrete event simulation for the plurality of resources based on the production-related input data and the cost-related input data, in accordance with the optimized production output and the optimized cost output to obtain a task allocation for each of the plurality of resources. The processor instructions further cause the processor to dynamically update the task allocation of one or more of the plurality of resources in near real-time based on the set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources, in accordance with the optimized production output and the optimized cost output.
[007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
[009] FIG. 1 is a block diagram of an allocation device configured for resource allocation optimization in a mine, in accordance with some embodiments of the present disclosure.
[010] FIG. 2 is a flow diagram of an exemplary process for resource allocation optimization in a mine, in accordance with some embodiments of the present disclosure.
[011] FIG. 3 illustrates a control logic for resource allocation optimization in a mine, in accordance with some embodiments of the present disclosure.
[012] FIG. 4 is a flow diagram of a detailed exemplary process for resource allocation optimization in a mine, in accordance with some embodiments of the present disclosure.
[013] FIG. 5 is a flow diagram of an exemplary process for operator selection for dumpers and excavators, in accordance with some embodiments of the present disclosure.
[014] FIG. 6 is a flow diagram of an exemplary process for allocating excavators by considering previous locations of the excavators, in accordance with some embodiments of the present disclosure.
[015] FIG. 7 is an exemplary discrete event simulation table, in accordance with some embodiments of the present disclosure.
[016] FIG. 8 is a flow diagram of an exemplary process for dynamic assignment of route-wise trip, in accordance with some embodiments of the present disclosure.
[017] FIG. 9 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
DETAILED DESCRIPTION
[018] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. Additional illustrative embodiments are listed below.
[019] Referring now to FIG. 1, a block diagram of an allocation device 100 configured for resource allocation optimization in a mine, is illustrated, in accordance with some embodiments of the present disclosure. By way of an example, the mine may be a coal mine, a limestone mine, a gold mine, a diamond mine, or the like. The allocation device 100 may allocate resources in mines based on various optimized techniques. In some embodiments, the allocation device 100 may receive production-related input data 102 and cost-related input data 104. The production-related input data 102 may include at least one target production output, blast zone inventory details, and a set of production-related operating parameters. The set of production-related operating parameters may include availability information and productivity information. The cost-related input data 104 may include at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources. The set of cost-related operating parameters may include fuel consumption information, payload information, and route details. The plurality of resources may include a plurality of equipment (for example, excavators and dumpers) and a plurality of operators (for example, an excavator operator or a dumper operator).
[020] Further, in order to optimally allocate resources, the allocation device 100 may include a production objective function determination module 106, a mixed integer non-linear programming model 108, a cost objective function determination module 110, a simulation module 112, and an allocation updating module 114. Moreover, the allocation device 100 may include a datastore or may be coupled to a storage for storing data, results of analysis or intermediate results generated by the modules 106-114.
[021] The production objective function determination module 106 may be configured to determine a production objective function for obtaining an optimized production output with respect to the target production output through the mixed integer non-linear programming model 108. In some embodiments, the target production output may be a maximum production output. The mixed integer non-linear programming model 108 may determine the production objective function based on the production-related input data 102 and a set of production constraints. The set of production constraints may include blending constraints at a radical level (for example, CaO, SiO2, Al2O3, Fe2O3, etc.). The production objective function determination module 106 may be communicatively coupled to the cost objective function determination module 110.
[022] Further, the cost objective function determination module 110 may be configured to determine a cost objective function to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model 108. The cost objective function may be determined based on the cost-related input data 104, the set of production constraints, and a set of cost-related constraints. Further, the cost objective function determination module 110 may be operatively coupled to the simulation module 112.
[023] The simulation module 112 may be configured to perform a discrete event simulation for the plurality of resources. The discrete event simulation may be performed based on the production-related input data 102 and the cost-related input data 104, in accordance with the optimized production output and the optimized cost output. Consequently, a task allocation for each of the plurality of resources may be obtained. In some embodiments, to perform the discrete event simulation, an expected start time, break hours, and an expected end time for each of one or more tasks in the task allocation of a resource, may be determined. The simulation module 112 may be communicatively coupled to the allocation updating module 112.
[024] The allocation updating module 112 may dynamically update the task allocation of one or more of the plurality of resources in near real-time in accordance with the optimized production output and the optimized cost output. To update the task allocation, the set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources may be considered. In some embodiments, a next task may be allocated in the task allocation of an unavailable resource to an available resource when the unavailable resource is performing a current task beyond the expected end time of the current task.
[025] Further, in some embodiments, a plurality of Key Performance Indicators (KPIs) corresponding to each of the plurality of resources may be determined by the allocation device 100. Further, each of the plurality of KPIs may be predicted in real-time. KPIs may be considered to confirm the output optimization. The KPIs may include productivity (TPH), production (MT), quality adherence, asset utilization (%), asset idle time, and fuel consumption.
[026] Additionally, in some embodiments, the allocation module 100 may also predict required sweetener quantity. The required sweetener quantity may be predicted based on the optimized production output and the optimized cost output through the mixed integer non-linear programming model 108. The sweetener is a high-grade material used to achieve target production quality. In other words, the allocation device 100 may predict required sweetener quantity to match the required quality. For example, a target is ‘102 Lime Saturation Factor (LSF)’ and present value is ‘100 LSF’. In this case, the allocation module 100 may predict the required sweetener quantity to be added to the material to meet the ‘102 LSF’ target. Moreover, in some embodiments, a waiting time for a resource may be calculated using a single channel queuing theory.
[027] Also, in some embodiments, operator details corresponding to each of the plurality of operators may be received. The operator details may include, but are not limited to, operator Identity Document (ID) and operator skill levels. An operator may be mapped to an available equipment based on the corresponding operator details.
[028] It should be noted that the operators may be allocated based on their availability and skills. For example, in a scenario, 10 excavators may be suggested. However, there may be 7 available excavator operators. In that case, all the 7 out of 10 excavators may be assigned because only 7 excavator operators are available. Also, there may be a scenario where 11 excavator operators may be present, but 10 excavators are suggested. In that case, 10 best operators out of 11 may be chosen for operating the excavators based on their skills. By way of an example, an operator may have a special skill for a particular type of excavator.
[029] In short, the allocation device 100 may allocate resources considering the production target quality as well as quantity. As will be appreciated to a person skilled in the art that the production should be accomplished every day in different shifts based on demand. Based on that fleet (i.e., different types of equipment and the operators) may be decided. By way of an example, between the blast zones and crushers, fleet (for example, dumpers and excavators) may be needed. For driving the dumpers and the excavators, the operators may be required. The allocation device 100 may consider various parameters such as “How many trips the fleet may cover”, “what capacity and type of dumper should be placed”, “where the excavator should be placed”, “what blast zone should be selected”, and the like. By the allocation device 100, a goal of increased production, decreased cost and fuel consumption at the same time may be achieved.
[030] With regards to the blast zones, a number of blast zones may be selected from a list of available blast zones considering the target production quantity and quality. In some embodiments, the excavators may be selected based on fuel consumption (i.e., lower fuel consumption). In between the blast zones and crusher routs may be there. Appropriate dumper requirement may be determined based on various factors such as trips covered by a dumper and required dumper capacity.
[031] In some embodiments, all dumper parameters such as dumper speed, loading time, loaded travel time, unloading time, unloaded travel time may be considered. Also, performance of the dumpers during a shift may be considered. It should be noted that other than mines domain, the allocation device 100 may be utilized in various other domains such as logistics optimization, airlines (for flight reservation), and the like.
[032] It should be noted that all such aforementioned modules 106 – 114 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 106 – 114 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 106 – 114 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 106 – 114 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 106 – 114 may be implemented in software for execution by various types of processors. An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module, and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
[033] As will be appreciated by one skilled in the art, a variety of processes may be employed for resource allocation optimization in a mine. For example, the exemplary allocation device 100 may dynamically provide resource allocation optimization in a mine. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the allocation device 100 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the processor in the allocation device 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some, or all the processes described herein may be included in the processor in the allocation device 100.
[034] Referring now to FIG. 2, an exemplary process 200 for resource allocation optimization in a mine is depicted via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 2 is explained in conjunction with FIG. 1. Each step of the process may be performed by the modules 106-114 of the allocation device 100.
[035] At step 202, production-related input data (same as the production-related input data 102) and cost-related input data (same as the cost-related input data 104) may be received. The production-related input data and cost-related input data may be corresponding to each of a plurality of resources. The production-related input data may include at least one target production output, blast zone inventory details, and a set of production-related operating parameters. The set of production-related operating parameters may further include availability information and productivity information. It may be noted that the availability information may include break hours. By way of an example, the productivity information may include, but may not be limited to maximum trip number, route-distance, shift hour, or the like. The cost-related input data may include at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources. The set of cost-related operating parameters may include fuel consumption information, payload information, and route details. The plurality of resources may include a plurality of equipment and a plurality of operators. The plurality of equipment may be, but are not limited to, excavators and dumpers. The plurality of operators may be, but may not be limited to, excavator operators and dumper operators.
[036] In some embodiments, operator details corresponding to each of the plurality of operators may be received. The operator details may include, but are not limited to, operator Identity Document (ID) and operator skill levels. An operator may be mapped to an available equipment based on the corresponding operator details.
[037] It should be noted that the operators may be allocated based on their availability and skills. For example, in a scenario, suggested excavators are more than present excavator operators, in that case, all the excavators may not be assigned because a smaller number of excavator operators are available.
[038] At step 204, a production objective function may be determined. The production objective function may be determined by the production objective function generation module 106. The production objective function may be determined to obtain an optimized production output with respect to the target production output through the mixed integer non-linear programming model 108. The target production output may correspond to a maximum production output. The production-related input data and a set of production constraints may be considered for production objective function determination. It should be noted that the set of production constraints may include blending constraints at a radical level.
[039] At step 206, a cost objective function may be determined using the cost objective function determination module 110. The cost objective function may be determined to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model 108. The cost-related input data, the set of production constraints, and a set of cost-related constraints may be taken into consideration while determining the cost objective function.
[040] In some embodiments, required sweetener quantity may be predicted. The required sweetener quantity may be predicted based on the optimized production output and the optimized cost output through the mixed integer non-linear programming model 108. The sweetener is a high-grade material used to achieve target production quality. In some embodiments, a waiting time for a resource may be calculated using a single channel queuing theory.
[041] A single-channel queuing problem deals with random interarrival time and service time at a single service station. Both the arrivals and service rates are independent of the customer number in queue. Arrivals are managed on first come first serve basis. Also, the service rate ‘µ’ is greater than the arrival rate ‘?’. It should be noted that ‘?’ is mean arrival rate of arrivals, ‘µ’ is mean service rate, and ‘n’ is number of customers in the system.
[042] Average service rate of excavator loader (‘µ’) in dumpers/hour is excavator average productivity/average dumper capacity. Arrival rate of dumpers (‘?’) at excavator (dumpers/hour) is excavator-dumper arrival rate equivalent to number of trips excavator/shift hour. For example, an average waiting time of a customer in the queue/ an average waiting time of a dumper in the queue in front of an excavator.is given by equation (1), given below:
Wq=2/ µ (µ -?) equation (1)
[043] At step 208, a discrete event simulation for the plurality of resources may be performed. A simulation module 112 may be employed to perform this step. The discrete event simulation may be based on the production-related input data and the cost-related input data, in accordance with the optimized production output and the optimized cost output, resulting into a task allocation for each of the plurality of resources. To perform the discrete event simulation, an expected start time, break hours, and an expected end time for each of one or more tasks in the task allocation of a resource, may be determined.
[044] Additionally, the discrete event simulation may display a complete schedule of a dumper dispatch to determine the start-time, break time, and end-time of each individual truck. This may be provided as an input stream for dynamic dispatch of dumpers.
[045] At step 210, the task allocation of one or more of the plurality of resources may be updated dynamically in near real-time, in accordance with the optimized production output and the optimized cost output. To perform this step, allocation updating module 114 may be utilized. The set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources may be considered for updating the task allocation. Further, in some embodiments, a next task may be allocated in the task allocation of an unavailable resource to an available resource when the unavailable resource is performing a current task beyond the expected end time of the current task.
[046] In some embodiments, the dumpers may be dynamically re-allocated to the excavators during the operation to take care of increasing production of the current hauling fleet or to achieve the production target using a smaller number of dumpers. This way higher fleet utilization may be enabled which may lead to higher productivity. The dynamic dispatch handles the stochastic elements of the mines and identifies excavators that are running behind the hourly targets and redirect dumpers from other routes to the needy excavators.
[047] A plurality of Key Performance Indicators (KPIs) corresponding to each of the plurality of resources may be determined, in accordance with some embodiments of present invention. Further, each of the plurality of KPIs may be predicted in real-time. KPIs may be considered to confirm the output optimization. The KPIs may include productivity (TPH), production (MT), quality adherence, asset utilization (%), asset idle time, and fuel consumption. The KPIs may be for every utilized equipment such as for equipment-operator allocation.
[048] Referring now to FIG. 3, a control logic 300 for resource allocation optimization in a mine is illustrated, in accordance with some embodiments of the present disclosure. FIG. 3 is explained in conjunction with FIGS. 1-2. In some embodiments, the control logic 300 may be implemented in a fist stage 302 and a second stage 304.
[049] At the first stage 302, production inputs 306 may be received. Examples of the production inputs 306 may include a production target, a quality target, available equipment, equipment productivity, equipment capacity, shift hours, and route information. After receiving the production inputs 306, the first objective function 308 may be determined. The first objective function 308 may correspond to the production objective function. The first objective function 308 may be determined to calculate total possible production based on available resources and a set of production constraints. The set of production constraints may include blending constraints at a radical level. As a result, optimized production output 310 may be obtained which may be used as an input for the second stage 304.
[050] In one embodiment, a production constraint may be truck availability. Total number of available trucks (for example, dumpers) may be checked based on their types in such a way that assignment should not exceed available number of trucks at the start of the shift. In one embodiment the constraint may be maximum number of trips on a route. For example, maximum number of trips possible in one route may be calculated based on route distance, cycle time generated dynamically. In one embodiment the constraint may be minimum number of trips on a route. For example, minimum number of trips may be assigned for one route.
[051] In one embodiment, the constraint may be target quality (including overall Quality/LSF) of crusher production. For example, target quality of production for every available crusher may be checked individually. In one embodiment, the constraint may be crusher-dumper load balancing. For example, crusher output (individually for each crusher) does not exceed dumper output for the respective number of routes (routes which are connected to that crusher).
[052] Further the production constrains include limitation on blast zone target. For example, the production hauled from the blast zone should not exceed the available quantity in the blast zone. The production constraints include production target. For example, to restrict overall production target crusher-wise. The production constraints also include excavator availability in order to check total number of available excavators based on their types (assignment should not exceed available number of excavators).
[053] Moreover, the constraint may be dumper-excavator load balancing to check whether an excavators’ output is similar to the output of the dumpers connected to it. This may ensure excavator utilization exceeding 100%. The constraint may be target quality of individual element of crusher production to check target quality of production for every available crusher individually for a specific set of elements.
[054] At the second stage 304, in some embodiments, the cost inputs 312 may be received. Further, at the second stage 304, in some embodiments, the first objective 308 may also be received. The cost inputs 312 may include average payload per trip, equipment utilization and mileage, fuel consumption in various states of an equipment, a cycle time, an ideal time and time required in various states of the equipment, (for example, loading time, unloading time, travel time, and the like), and equipment speed, location and route. In some embodiments, the equipment may be a dumper. After receiving the cost inputs 312 and the first objective function 308, the second objective function 314 may be determined. The second objective function 314 may correspond to the cost objective function. The second objective function 314 may be determined to calculate required cost to produce optimized production output 310 based on the first objective function 308, the cost-related input data, the set of production constraints, and a set of cost-related constraints. As a result, the optimized cost output 316 along with optimized production output 310 may be obtained.
[055] The cost related constrains include all the production constraints. to check total production quantity is more than or equal to achieved one from the first objective function. Further, in one embodiment, the cost related constrains include blending constraints at radical level. Usually, quality constraints are formed based on the LSF. However, here the constraints are applied in the radical level CaO, SiO2, Al2O3, and Fe2O3, to achieve the target quality more accurately.
[056] Referring now to FIG. 4, a flow diagram of a detailed exemplary process 400 for resource allocation optimization in a mine, in accordance with some embodiments of the present disclosure. FIG. 4 is explained in conjunction with FIGS. 1-3.
[057] At step 402, user inputs may be received. In one embodiment a user input may be blast zone inventory quantity and quality details. In one embodiment, the user input crusher target quality, quantity, productivity, and sweetener details. In one embodiment, the user input may be dumper fleet and Fuel Consumption (FC) details. In one embodiment, the user input may be a start time and break hours of a dumper. In one embodiment, the user input may be excavator fleet and FC details. In one embodiment, the user input may be operator availability and skill details.
[058] Thereafter, at step 404, an optimization model may be initialized based on the user input, various production and cost related parameters, and decision variables. The optimization model may correspond to the mixed integer non-linear programming model (such as, the non-linear programming model 108). The production and cost related parameters may include production-related operating parameters and cost related operating parameters.
[059] At step 406, a first objective function (same as the first objective function 308) may be determined so that optimized production output (for example, maximum production output) is obtained. Available resources and a set of production constraints may be considered to determine the first objective function. The set of production constraints may include blending constraints at a radical level.
[060] At step 408, the optimization model may be executed at a first stage (same as the first stage 302). The optimization model may allocate the resources in such a way that optimized/maximum production adhering the quality may be obtained as a first stage output 408a of the first stage.
[061] At step 410, a second objective function (same as the second objective function 314) may be determined. The second objective function may be determined to calculate required cost to produce optimized production output based on the first objective function, the user input, the set of production constraints, and a set of cost-related constraints.
[062] At step 412, the optimization model may be executed at a second stage so that optimized cost output (for example, by minimum fuel consumption) and optimized production output (for example, maximum production output) adhering the quality is obtained as a second stage output 412a. Further, at step 414, initial equipment allocation, resource allocation, equipment wise KPI, sweetener quantity, overall KPI table, truck dispatch schedule may be generated.
[063] At step 416, dumpers (trucks) may be dispatched dynamically based on the truck dispatch schedule. At step 418, dispatch allocation table may be generated based on dynamic dispatch of the dumpers.
[064] Referring now to FIG. 5, a process 500 for operator selection for dumpers and excavators is illustrated, in accordance with an exemplary embodiment. Each step of the process 500 may be executed through an optimization model (for example, such as the non-linear programming model 108). FIG. 5 is explained in conjunction with FIGS. 1-4.
[065] At step 502, an optimization model may be initialized for equipment allocation. In one embodiment the equipment allocation may correspond to dumper allocation. In another embodiment, the equipment allocation may correspond to the excavator allocation. At step 504, equipment details (i.e., excavator and dumper details) may be obtained from the optimization model.
[066] Thereafter, at step 506, details of available operators (for example, drivers available for excavators and dumpers) may be received for a shift. The details may include, but are not limited to, operator Identity (ID), operator skill type, and operator proficiency details. At step 508, number of available operators may be matched with number of available equipment. At step 510, operators’ skills may be matched with equipment types. For example, operator ‘A’ and operator ‘B’ have a specific type of skill ‘S’ which may be essential for operating an equipment ‘E’ (i.e., ‘S’ (of operator ‘A’ and operator ‘B’) may be a match for ‘E’).
[067] At step 512, operators’ skills level may be checked, and efficient (expert/ efficient/ competent) operators based on the skill level may be retrieved. At step 514, equipment-operator mapping may be generated for equipment allocation.
[068] Referring now to FIG. 6, a process 600 for allocating excavators by considering previous locations of the excavators is illustrated, in accordance with an exemplary embodiment. Each step of the process 600 may be executed through an optimization model (for example, such as the non-linear programming model 108). FIG. 6 is explained in conjunction with FIGS. 1-5. At step 602, the optimisation model may be initialized. Thereafter, at step 604, excavators’ may be allocated to the blast zones through the optimisation model. At step 606, existing excavators’ locations may be considered.
[069] It may be checked whether any excavator is present within the selected blast zones, at step 608. At step 610, if the excavator is present within the selected blast zones, then allocate that existing excavator. If the excavator is absent within the selected blast zones, then the excavator(s) allocated by the model may be considered.
[070] Referring now to FIG. 7, an exemplary discrete event simulation table 700, in accordance with an embodiment. FIG. 7 is explained in conjunction with FIGS. 1-6. The discrete event simulation table 700 may include truck number 702, start time 704, load completion time 706, and next arrival time 708. The truck number 702 may correspond to dumper number. For example, in one embodiment, for the truck number ‘60012015’, the start time may be ‘12:00:12 AM’, the load completion time may be ‘12:01:42 AM’, and the next arrival time may be ‘12:16:29 AM’. As illustrated in FIG. 7, for the same truck number ‘60012015’ with start time ’12:16: 29 AM’, the load completion time and the next arrival time may be ’12:17:59 AM’ and ’12:32:46 AM’. Similarly, for the same truck number with different start time, load completion time and next arrival time are represented in the discrete event simulation table 700.
[071] Further, based on the discrete event simulation table 700, firstly, a dispatch flag may be evaluated based on a predefined threshold value. The dispatch flag may determine whether a dispatch of a truck is required or not. If dispatch is not required, then the dispatch flag will be disabled and the dispatch will not run (for example, the dispatch flag will be disabled if running hours is close to end time of a truck). Secondly, dumpers may be identified for reallocation. If the dispatch flag is enabled, the dumpers which can be reallocated (i.e., available dumpers) may be identified. It may be noted that the available dumpers are dumpers which completed their allocated trips before a predefined threshold time.
[072] Thirdly, prospective routes may be identified. The prospective routes may be identified based on a percentage of pending trips and number of existing dumpers present in the route. Fourthly, the identified available dumpers may be assigned on the prospective routes. Fifthly, a final dispatch table may be generated. The final dispatch table may include details of the available dumpers to be reallocated along with source and destination of the prospective routes.
[073] Referring now to FIG. 8, a process 800 for dynamic assignment of route-wise trip for dumpers is illustrated via block diagram 800, in accordance with an embodiment. FIG. 8 is explained in conjunction with FIGS. 1-7. At step 802 inputs may be received. The inputs may include dumper loaded speed, dumper unloaded speed, route distance, and shift hour. At step 804, dumper travel time may be calculated when loaded as well as when unloaded. At step 806, dumper cycle time may be calculated. At step 808, route-wise trip number, cycle time, and shift hour may be calculated. Thereafter, at step 810, maximum possible trip number may be applied in the constraint. At step 812, optimization model may be run. Further, route-wise maximum trip number may be generated, at step 814.
[074] The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer. Referring now to FIG. 9, an exemplary computing system 900 that may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, or the like) is illustrated. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 900 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, personal entertainment device, DVR, and so on, or any other type of special or general-purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 900 may include one or more processors, such as a processor 902 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, the processor 902 is connected to a bus 904 or other communication medium. In some embodiments, the processor 902 may be an Artificial Intelligence (AI) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).
[075] The computing system 900 may also include a memory 906 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 902. The memory 906 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 902. The computing system 900 may likewise include a read only memory (“ROM”) or other static storage device coupled to bus 904 for storing static information and instructions for the processor 902.
[076] The computing system 900 may also include a storage device 908, which may include, for example, a media drives 910 and a removable storage interface. The media drive 910 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 906 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 920. As these examples illustrate, the storage media 912 may include a computer-readable storage medium having stored there in particular computer software or data.
[077] In alternative embodiments, the storage devices 908 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 900. Such instrumentalities may include, for example, a removable storage unit 914 and a storage unit interface 916, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 914 to the computing system 900.
[078] The computing system 900 may also include a communications interface 918. The communications interface 918 may be used to allow software and data to be transferred between the computing system 900 and external devices. Examples of the communications interface 918 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 918 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 918. These signals are provided to the communications interface 918 via a channel 920. The channel 920 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 920 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.
[079] The computing system 900 may further include Input/Output (I/O) devices 922. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I/O devices 922 may receive input from a user and also display an output of the computation performed by the processor 902. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 906, the storage devices 908, the removable storage unit 914, or signal(s) on the channel 920. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 902 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 900 to perform features or functions of embodiments of the present invention.
[080] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 900 using, for example, the removable storage unit 914, the media drive 910 or the communications interface 918. The control logic (in this example, software instructions or computer program code), when executed by the processor 902, causes the processor 902 to perform the functions of the invention as described herein.
[081] Various embodiments of the present disclosure provide method and system for resource allocation optimization in a mine. The disclosed method and system may dynamically improve utilization of resources in a system like a mining system or a logistics system. The method and system may further provide a multi-stage and multi-objective approach with mixed integer non-linear programming. The method and system may further derive one dynamic constraint based on route distance (i.e., what’s the maximum trip number possible in a route). The method and system may further provide need-based excavator selection and allocation. The method and system may further take into account break hours dynamically for calculating the discrete event simulation to give a more accurate image of simulation. The method and system may further include an innovative dynamic dispatch module that may be utilized in other domains also. The method and system may further provide a flexible and reusable framework that may be applied in other mines and other domains (such as, logistics, flight reservation, etc.) to handle varied set of business needs (for example, equipment break down or any kind of change in the system).
[082] It will be appreciated that, for clarity purposes, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processors or domains may be used without detracting from the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Hence, references to specific functional units are only to be seen as references to suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization.
[083] Although the present invention has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the present invention is limited only by the claims. Additionally, although a feature may appear to be described in connection with particular embodiments, one skilled in the art would recognize that various features of the described embodiments may be combined in accordance with the invention.
[084] Furthermore, although individually listed, a plurality of means, elements or process steps may be implemented by, for example, a single unit or processor. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that a combination of features is not feasible and/or advantageous. Also, the inclusion of a feature in one category of claims does not imply a limitation to this category, but rather the feature may be equally applicable to other claim categories, as appropriate. , Claims:CLAIMS
We Claim:
1. A method (200) of resource allocation optimization in a mine, the method comprising:
receiving (202), by an allocation device (100), production-related input data and cost-related input data corresponding to each of a plurality of resources, wherein the production-related input data comprises at least one target production output, blast zone inventory details, and a set of production-related operating parameters, wherein the cost-related input data comprises at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources, and wherein the plurality of resources comprises a plurality of equipment and a plurality of operators;
determining (204), by the allocation device (100), a production objective function to obtain an optimized production output with respect to the target production output through a mixed integer non-linear programming model based on the production-related input data and a set of production constraints, wherein the set of production constraints comprises blending constraints at a radical level;
determining (206), by the allocation device (100), a cost objective function to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model based on the cost-related input data, the set of production constraints, and a set of cost-related constraints;
performing (208), by the allocation device (100), a discrete event simulation for the plurality of resources based on the production-related input data and the cost-related input data, in accordance with the optimized production output and the optimized cost output to obtain a task allocation for each of the plurality of resources; and
dynamically updating (210), by the allocation device (100), the task allocation of one or more of the plurality of resources in near real-time based on the set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources, in accordance with the optimized production output and the optimized cost output.

2. The method (200) of claim 1, comprising:
determining a plurality of Key Performance Indicators (KPIs) corresponding to each of the plurality of resources; and
predicting each of the plurality of KPIs in real-time.

3. The method (200) of claim 1, wherein performing (208) the discrete event simulation for the plurality of resources comprises determining an expected start time, break hours, and an expected end time for each of one or more tasks in the task allocation of a resource and, wherein dynamically updating (210) the task allocation of one or more of the plurality of resources comprises assigning a next task in the task allocation of an unavailable resource to an available resource when the unavailable resource is performing a current task beyond the expected end time of the current task.

4. The method (200) of claim 1, comprising predicting sweetener quantity required based on the optimized production output and the optimized cost output through the mixed integer non-linear programming model.

5. The method (200) of claim 1, comprising calculating a waiting time for a resource using a single channel queuing theory.

6. The method (200) of claim 1, comprising:
receiving operator details corresponding to each of the plurality of operators, wherein the operator details comprise operator Identity Document (ID) and operator skill levels; and
mapping an operator to an available equipment based on the corresponding operator details.

7. The method (200) of claim 1, wherein the set of production-related operating parameters comprises availability information and productivity information, and wherein the set of cost-related operating parameters comprises fuel consumption information, payload information, and route details.

8. A system (100) for resource allocation optimization in a mine, the system (100) comprising:
a processor (902); and
a memory (906) communicatively coupled to the processor (902), wherein the memory (906) stores processor executable instructions, which when executed by the processor, cause the processor (902) to:
receive (202) production-related input data and cost-related input data corresponding to each of a plurality of resources, wherein the production-related input data comprises at least one target production output, blast zone inventory details, and a set of production-related operating parameters, wherein the cost-related input data comprises at least one cost-related target and a set of cost-related operating parameters corresponding to each of the plurality of resources, and wherein the plurality of resources comprises a plurality of equipment and a plurality of operators;
determine (204) a production objective function to obtain an optimized production output respect to the target production output through a mixed integer non-linear programming model based on the production-related input data and a set of production constraints, wherein the set of production constraints comprises blending constraints at a radical level;
determine (206) a cost objective function to obtain an optimized cost output with respect to the at least one cost-related target through the mixed integer non-linear programming model based on the cost-related input data, the set of production constraints, and a set of cost-related constraints;
perform (208) a discrete event simulation for the plurality of resources based on the production-related input data and the cost-related input data, in accordance with the optimized production output and the optimized cost output to obtain a task allocation for each of the plurality of resources; and
dynamically update (210) the task allocation of one or more of the plurality of resources in near real-time based on the set of production-related operating parameters, the set of cost-related operating parameters, and previous states of the one or more of the plurality of resources, in accordance with the optimized production output and the optimized cost output.

9. The system (100) of claim 8, wherein the processor executable instructions cause the processor (902) to perform (208) the discrete event simulation for the plurality of resources by determining an expected start time, break hours, and an expected end time for each of one or more tasks in the task allocation of a resource and, wherein the processor executable instructions cause the processor (902) to dynamically update (210) the task allocation of one or more of the plurality of resources by assigning a next task in the task allocation of an unavailable resource to an available resource when the unavailable resource is performing a current task beyond the expected end time of the current task.

10. The system (100) of claim 8, wherein the processor executable instructions cause the processor (902) to:
predict sweetener quantity required based on the optimized production output and the optimized cost output through the mixed integer non-linear programming model; and
calculate a waiting time for a resource using a single channel queuing theory.