CN117634772A - Strip mine unmanned truck scheduling method considering truck performance - Google Patents
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Abstract
The invention discloses a strip mine unmanned truck scheduling method considering truck performance, which comprises the following steps of; step1: establishing a strip mine truck running condition database; step2: establishing a truck operation efficiency prediction model; step3: establishing a multi-objective unmanned strip mine truck dispatching optimization model according to the strip mine truck running condition database and the truck running efficiency prediction model; step4: and (3) solving the scheduling optimization model established in the step (3) by adopting a multi-target particle swarm algorithm, and optimizing a final scheduling scheme. And establishing a prediction model of truck transportation efficiency by utilizing historical data of mine production, and introducing a prediction result into a solution model of real-time dispatching optimization of the mine car to obtain a dispatching scheme closer to actual production, thereby reducing transportation cost in production.
Description
Technical Field
The invention relates to the technical field of surface mine operation scheduling methods, in particular to a surface mine unmanned truck scheduling method considering truck performance.
Background
The strip mine production planning is carried out by the deployment of the transport equipment between the blasting stacks and the dump area. The primary task of mine car transportation is to transport ore from the blast pile to a dump or storage site. Therefore, optimizing the dispatching of mine car, improving the transportation efficiency of mine car has great significance to reduce production cost, improve production efficiency. Optimization of mine car dispatching is also an important link in optimization of the strip mine system.
Conventional mine car dispatch optimization generally considers the performance and transportation efficiency of the mine car to be at the same level and unchanged. But this is not practical for practical production. In actual production, the model, load, mileage, engine condition, tire wear condition, and maintenance condition of each working truck are different. These factors result in different transport efficiency for each truck. Therefore, different performances of each truck should be considered in the dispatching optimization of the mine car, so that the optimization result is closer to the actual production condition. This requires the establishment of a new approach to unmanned truck dispatching for strip mines that takes truck performance into account. The historical data of the mine car condition in the open pit production is analyzed and a prediction model is established, which has construction significance for establishing the new scheduling method considering the truck performance.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide the unmanned truck scheduling method for the strip mine, which takes the performance of the truck into consideration, establishes a prediction model of the transportation efficiency of the truck by using the historical data of mine production, brings the prediction result into a solution model for real-time scheduling optimization of the mine car, and obtains a scheduling scheme which is closer to actual production, thereby reducing the transportation cost in production.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a strip mine unmanned truck scheduling method considering truck performance comprises the following steps;
step1: establishing a strip mine truck running condition database;
step2: establishing a truck operation efficiency (speed) prediction model;
step3: establishing a multi-objective unmanned strip mine truck dispatching optimization model according to a strip mine truck running condition database and a truck running efficiency (speed) prediction model;
step4: and solving the scheduling optimization model by adopting a multi-target particle swarm algorithm, and optimizing a final scheduling scheme.
The step1 specifically comprises the following steps: collecting historical data of truck load, completed mileage, engine condition, tire wear condition, maintenance condition and average speed of each class of each truck in the production of the strip mine, and establishing a strip mine truck running condition database; and (5) making a data statistics table of the running conditions of the strip mine trucks, and counting historical data of the running conditions of the strip mine trucks.
The step2 specifically comprises the following steps: by analyzing factors influencing the transportation performance of the truck, a prediction model is established, wherein five influencing factors including the load of the truck, the number of mileage driven, the number of times of engine faults, the number of mileage used by tires and the number of times of daily maintenance are taken as input parameters, and the average speed of each shift of the vehicle is taken as output parameters.
In the step3, according to the actual production and transportation process of the strip mine, the unmanned truck dispatching optimization model of the strip mine is established with the aim of minimizing the waiting time of the truck, the residual yield and the ore grade deviation; the strip mine unmanned truck dispatching optimization model comprises an objective function and constraint conditions.
The objective function is specifically:
minimizing truck transportation costs:
the transportation cost of the truck is composed of the transportation cost from the loading point to the unloading point of the heavy load of the truck, the transportation cost from the unloading point to the loading point of the empty load of the truck and the transportation cost from the unloading point to the filling point of the empty load;
minimizing truck waiting time:
the waiting time of the truck is formed by subtracting the actual running time of the truck from the working time of the truck shift, wherein the actual running time comprises the running time between loading and unloading points, the loading and unloading time, the time for going to and from a gas station and the refueling time;
minimizing residual yield:
to ensure that ore produced in one shift at a mining site can be transported as closely as possible, i.e., the capacity and capacity of the arrangement are as close as possible, the absolute value of the required yield minus the capacity is minimized;
minimizing ore grade deviation:
the sum of ore grade deviations at each unloading point is minimal;
the constraint conditions are specifically as follows:
capacity constraints for unloading points:
the total amount of ore to be transported to each unloading point must not exceed the maximum capacity of that unloading point;
yield planning requirements for unloading points:
the total amount of ore unloaded at each unloading point at least meets the production requirements of the unloading point;
capacity constraints of loading points:
the total amount of ore loaded at each loading point must not exceed the maximum capacity of that loading point;
ore grade requirement at unloading point:
the deviation of ore grade unloaded at each unloading point must not exceed the maximum deviation of grade;
traffic continuity constraints ensure equal traffic in and out of each loading/unloading point;
remaining oil mass constraint:
the unmanned truck is no longer like a traditional truck, and a driver can pay attention to the residual oil quantity at any time in the driving process, so that the residual oil quantity of the unmanned truck needs to be monitored when the unmanned truck is subjected to traffic planning and scheduling. It is embodied in that the amount of oil remaining per truck must not be below K in order to return to the fueling point for fueling. Wherein, the minimum residual oil quantity K=maximum oil quantity from unloading point to loading point, maximum oil quantity from loading point to unloading point, maximum oil quantity from unloading point and maximum oil quantity from oil filling point O;
the number of truck shipments requires that the number of truck shipments must be a positive integer;
X ijkl ,Y ijkl ∈{0,1,2,3...}
constraint conditions under collision avoidance conditions;
when two trucks are simultaneously at a certain loading point or unloading point, the routes of the trucks can collide, and the running cost and queuing time of the trucks are greatly influenced. In order to reduce the impact of truck mission conflicts as much as possible, it is necessary to prioritize trucks under the constraints of the initial state. Considering that a fully loaded truck may be more costly to operate than an empty truck, a full truck is preferred over an empty truck. In addition, the priority determination is also applicable to the avoidance of an empty unmanned truck when two trucks meet at the same intersection.
Constraints in case of truck failure;
once the truck fails and cannot run, the truck should exit the traffic planning and scheduling system in time. Therefore, after a certain unmanned truck gives an early warning to a total dispatching center, the truck should be immediately excluded from the system, and the priority level of the path, loading point or unloading point of the truck is reduced, so that the planning and dispatching scheme can be adjusted in time, and the established aim of an enterprise is fulfilled;
the reference symbols used in the above formula are defined as follows:
i: index number of loading point, indicating I-th loading point (i.e. excavator position), i=1, 2, …, I, respectively; j: index number of unloading point indicates the J-th unloading point (i.e. crushing station position), j=1, 2, …, J, the numberThe method comprises the steps of carrying out a first treatment on the surface of the k: truck model index number, representing the K-th model truck, k=1, 2, …, K; l: truck number index number, indicating the first k-truck, l=1, 2, …, L, vehicle; x is X ijkl : the number of times the k-type truck numbered l reloads from the loading point i to the unloading point j, the times; y is Y ijkl : the number of times the k-type truck numbered l is unloaded from the unloading point j to the loading point i, and the times; d, d ij : the distance from the loading point i to the unloading point j is km; d, d jo : distance from unloading point j to oiling point O, km; d, d oi Optimal distance from the oiling point O to the loading point i, km; c (C) k : the loading capacity of a k-model truck, t; CE (CE) kl : the unit distance cost of the k-type truck with the number of l is Yuan/km; CL (CL) kl : the unit distance cost of the k-type truck with the number of l is equal to yuan/km when the k-type truck is empty; e (E) kl : the fuel tank capacity of the k-model truck with the number of L; EE kl : the unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is overloaded; EL (electro luminescence) kl The unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is empty; k: minimum residual oil, L; g i : maximum throughput of loading point i, t; f (f) j : minimum production demand (within one shift) for unloading point j, t; q j : maximum capacity of unloading point j, t; e: limitation of ore grade; alpha i : ore grade at loading point i; beta: ore grade tolerance; g j : unloading the target grade of the point j; t (T) lim : working time of the shift; SE (SE) kl : the average speed of the k-type truck with the number of l when the k-type truck is overloaded is km/h; SL (SL) device kl : the average speed of the k-type truck with the number of l when no load exists, km/h; t (T) O Average time for truck fueling, min; t (T) z : average time of truck loading, min; t (T) q : average time for unloading of the truck, min; k (K) jOkl : the number of times the k-type truck numbered l runs from the unloading point j to the oiling point O when no load exists, and the number of times is the same; k (K) Oikl : the number of runs from fueling point O to loading point i, number i, of the k-truck no-load.
The step4 specifically comprises the following steps:
setting basic parameters such as inertia weight omega and flying speeds c1 and c2 of a multi-target particle swarm algorithm, and solving a strip mine unmanned truck dispatching optimization model through the multi-target particle swarm algorithm;
judging whether the current solution (scheme) meets the constraints of three aspects of production capacity, grade requirement and traffic flow control, and then updating the position and speed of particles; the whole process is completed as one iteration, when the algorithm iteration is completed, a result is output, the operation process is ended, and otherwise, the multi-objective particle swarm algorithm solution is continued; the specific operation steps are as follows:
step1: initializing a particle group P; for each particle, determining the initial position and speed of the particle, and calculating the target vector of each particle in the particle group P;
step2: storing part of the particles in the particle swarm P in an external particle swarm NP, wherein the positions of the particles are non-inferior solutions;
step3: determining the initial self-best position of each particle, namely the initial position of each particle;
step4: dividing a target space into a plurality of grids (hypercube), and determining the grid of each particle according to the target vector corresponding to the particle;
step5: defining fitness values (the ratio of the number equal to or greater than 1 to the number of NP members contained in the lattices) for lattices each containing at least one external particle group individual, then selecting one lattice for each particle according to the roulette method, and randomly selecting one external particle group individual therefrom as a gbest of the particle;
step6: updating the positions and the speeds of all particles according to a basic formula of PSO;
step7: the following measures are taken to avoid particles from flying out of the search space: once a particle flies out of the boundary of a certain decision variable, the particle stays on the boundary while changing the flight direction;
step8: calculating a target vector of each particle in the particle swarm P;
step9: updating and maintaining the external particle swarm NP by using an adaptive grid method;
step10: updating the particle's pbest. Comparing the new solution obtained in the particle flight process with the best position pbest of the new solution, and if the new solution dominates the pbest, obtaining the new solution as the new pbest; otherwise, the pbest remains unchanged; if the new solution and the pbest are not subject to each other, randomly selecting one from the new solution and the pbest as a new self best position;
step11: if the termination condition is met, stopping searching, and judging whether the scheme meets three constraint conditions of production capacity, grade limitation and traffic flow control in the model; when the conditions are met, outputting an optimization scheme; otherwise, go to Step6.
The invention has the beneficial effects that:
the scheduling model constructed in the invention is more fit with the actual production situation of the mine, and the scheduling scheme obtained by the scheme can effectively reduce the transportation cost and improve the transportation efficiency.
Aiming at the problem of scheduling and optimizing the unmanned open-pit mine truck taking truck performance into consideration, the invention aims at minimizing the waiting time of the truck, minimizing the residual yield and minimizing the ore grade deviation, considers different transportation efficiency of the truck, and builds an unmanned open-pit mine truck scheduling and optimizing model. The optimization solution is carried out by adopting a multi-target particle swarm algorithm, so that the scheduling optimization problem of the unmanned truck of the strip mine is effectively solved, and the requirement of actual mine transportation is met.
Drawings
FIG. 1 is a flow chart of a truck production schedule.
Fig. 2 is a flow chart of the unmanned truck dispatch for a strip mine in consideration of truck performance in accordance with the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
A strip mine unmanned truck scheduling method considering truck performance comprises the following steps;
firstly, establishing a strip mine truck running condition database according to historical data collected in strip mine production, wherein the historical data relate to the load of each truck, the number of completed mileage, the condition of an engine, the abrasion condition of tires, the maintenance condition and the average speed of each shift;
secondly, training a prediction model based on the BP neural network according to historical data, and obtaining real-time running speed of the truck through prediction;
finally, establishing an unmanned truck scheduling model of the strip mine, which takes the production capacity, the grade requirement and the traffic flow control as constraint conditions, and aims at minimizing the waiting time of the truck, the residual yield and the grade deviation of the ore, and solving the model by adopting a multi-target particle swarm algorithm.
As shown in fig. 1, a flow chart of the strip mine truck production schedule is shown. In actual production, trucks travel to and from loading points, unloading points and gas stations. For on-site truck dispatching, one of the main approaches to improve production efficiency is to increase.
As shown in fig. 2, the invention provides a method for dispatching unmanned trucks of a strip mine, which takes truck performance into consideration, and mainly comprises the following steps:
step1, establishing a strip mine truck running condition database:
collecting historical data of truck load, completed mileage, engine condition, tire wear condition, maintenance condition and average speed of each class of each truck in the production of the strip mine, and establishing a strip mine truck running condition database; and (5) making a data statistics table of the running conditions of the strip mine trucks, and counting historical data of the running conditions of the strip mine trucks. The truck running condition data statistics designed in the invention are as follows:
step2, establishing a truck operation efficiency (speed) prediction model:
by analyzing factors influencing the transportation performance of the truck, a prediction model is established, wherein five influencing factors including the load of the truck, the number of mileage driven, the number of times of engine faults, the number of mileage used by tires and the number of times of daily maintenance are taken as input parameters, and the average speed of each shift of the vehicle is taken as output parameters.
In the invention, 100 groups of historical statistical data from 1 st 2021 to 4 th 2021 are selected to train the regression model, wherein 90 groups constitute a training data set for training a network to acquire a calculation model; the rest 10 groups are simulation data used for verifying the calculation accuracy of the model.
Step3, establishing a dispatching optimization model of the unmanned truck of the multi-target strip mine:
according to the actual production and transportation process of the strip mine, taking the purposes of minimizing the waiting time of the truck, minimizing the residual yield and minimizing the ore grade deviation as targets, and establishing an unmanned truck dispatching optimization model of the strip mine; the specific expression of the model is as follows:
(1): objective function:
minimizing truck transportation costs:
the transportation cost of the truck is composed of the transportation cost from the loading point to the unloading point of the heavy load of the truck, the transportation cost from the unloading point to the loading point of the empty load of the truck and the transportation cost from the unloading point to the filling point of the empty load;
minimizing truck waiting time:
the waiting time of the truck is formed by subtracting the actual running time of the truck from the working time of the truck shift, wherein the actual running time comprises the running time between loading and unloading points, the loading and unloading time, the time for going to and from a gas station and the refueling time;
minimizing residual yield:
to ensure that ore produced in one shift at a mining site can be transported as closely as possible, i.e., the capacity and capacity of the arrangement are as close as possible, the absolute value of the required yield minus the capacity is minimized;
minimizing ore grade deviation:
the sum of ore grade deviations at each unloading point is minimal;
(2) Constraint conditions:
capacity constraints for unloading points:
the total amount of ore to be transported to each unloading point must not exceed the maximum capacity of that unloading point;
yield planning requirements for unloading points:
the total amount of ore unloaded at each unloading point at least meets the production requirements of the unloading point;
capacity constraints of loading points:
the total amount of ore loaded at each loading point must not exceed the maximum capacity of that loading point;
ore grade requirement at unloading point:
the deviation of ore grade unloaded at each unloading point must not exceed the maximum deviation of grade;
traffic continuity constraints ensure equal traffic in and out of each loading/unloading point;
remaining oil mass constraint:
the unmanned truck is no longer like a traditional truck, and a driver can pay attention to the residual oil quantity at any time in the driving process, so that the residual oil quantity of the unmanned truck needs to be monitored when the unmanned truck is subjected to traffic planning and scheduling. It is embodied in that the amount of oil remaining per truck must not be below K in order to return to the fueling point for fueling. Wherein, the minimum residual oil quantity K=maximum oil quantity from unloading point to loading point, maximum oil quantity from loading point to unloading point, maximum oil quantity from unloading point and maximum oil quantity from oil filling point O;
the number of truck shipments requires that the number of truck shipments must be a positive integer;
X ijkl ,Y ijkl ∈{0,1,2,3...}
constraint conditions under collision avoidance conditions;
when two trucks are simultaneously at a certain loading point or unloading point, the routes of the trucks can collide, and the running cost and queuing time of the trucks are greatly influenced. In order to reduce the impact of truck mission conflicts as much as possible, it is necessary to prioritize trucks under the constraints of the initial state. Considering that a fully loaded truck may be more costly to operate than an empty truck, a full truck is preferred over an empty truck. In addition, the priority determination is also applicable to the avoidance of an empty unmanned truck when two trucks meet at the same intersection.
Constraints in case of truck failure;
once the truck fails and cannot run, the truck should exit the traffic planning and scheduling system in time. Therefore, after a certain unmanned truck gives an early warning to a total dispatching center, the truck should be immediately excluded from the system, and the priority level of the path, loading point or unloading point of the truck is reduced, so that the planning and dispatching scheme can be adjusted in time, and the established aim of an enterprise is fulfilled;
the reference symbols used in the above formula are defined as follows:
i: index number of loading point, indicating I-th loading point (i.e. excavator position), i=1, 2, …, I, respectively; j: the index number of the unloading point indicates the J-th unloading point (i.e. the crushing station position), j=1, 2, …, J, respectively; k: truck model index number, representing the K-th model truck, k=1, 2, …, K; l: truck number index number, indicating the first k-truck, l=1, 2, …, L, vehicle; x is X ijkl : the number of times the k-type truck numbered l reloads from the loading point i to the unloading point j, the times; y is Y ijkl : the number of times the k-type truck numbered l is unloaded from the unloading point j to the loading point i, and the times; d, d ij : the distance from the loading point i to the unloading point j is km; d, d jo : distance from unloading point j to oiling point O, km; d, d oi Optimal distance from the oiling point O to the loading point i, km; c (C) k : the loading capacity of a k-model truck, t; CE (CE) kl : the unit distance cost of the k-type truck with the number of l is Yuan/km; CL (CL) kl : the unit distance cost of the k-type truck with the number of l is equal to yuan/km when the k-type truck is empty; e (E) kl : the fuel tank capacity of the k-model truck with the number of L; EE kl : the unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is overloaded; EL (electro luminescence) kl The unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is empty; k: minimum residual oil, L; g i : maximum throughput of loading point i, t; f (f) j : minimum production demand (within one shift) for unloading point j, t; q j : maximum capacity of unloading point j, t; e: limitation of ore grade; alpha i : ore grade at loading point i; beta: ore grade tolerance; g j : unloading the target grade of the point j; t (T) lim : working time of the shift; SE (SE) kl : the average speed of the k-type truck with the number of l when the k-type truck is overloaded is km/h; SL (SL) device kl : the average speed of the k-type truck with the number of l when no load exists, km/h; t (T) O Average time for truck fueling, min; t (T) z : average time of truck loading, min; t (T) q : average time for unloading of the truck, min; k (K) jOkl : the number of times the k-type truck numbered l runs from the unloading point j to the oiling point O when no load exists, and the number of times is the same; k (K) Oikl : the number of runs from fueling point O to loading point i, number i, of the k-truck no-load.
Step4, solving a scheduling model by adopting a multi-target particle swarm algorithm: for optimizing the final scheduling scheme.
Setting basic parameters such as inertia weight omega and flying speeds c1 and c2 of a multi-target particle swarm algorithm, and solving a strip mine unmanned truck dispatching optimization model through the multi-target particle swarm algorithm;
judging whether the current solution (scheme) meets the constraints of three aspects of production capacity, grade requirement and traffic flow control, and then updating the position and speed of particles; the whole process is completed as one iteration, when the algorithm iteration is completed, a result is output, the operation process is ended, and otherwise, the multi-objective particle swarm algorithm solution is continued; the specific operation steps are as follows:
step1: initializing a particle group P; for each particle, determining the initial position and speed of the particle, and calculating the target vector of each particle in the particle group P;
step2: storing part of the particles in the particle swarm P in an external particle swarm NP, wherein the positions of the particles are non-inferior solutions;
step3: determining the initial self-best position of each particle, namely the initial position of each particle;
step4: dividing a target space into a plurality of grids (hypercube), and determining the grid of each particle according to the target vector corresponding to the particle;
step5: defining fitness values (the ratio of the number equal to or greater than 1 to the number of NP members contained in the lattices) for lattices each containing at least one external particle group individual, then selecting one lattice for each particle according to the roulette method, and randomly selecting one external particle group individual therefrom as a gbest of the particle;
step6: updating the positions and the speeds of all particles according to a basic formula of PSO;
step7: the following measures are taken to avoid particles from flying out of the search space: once a particle flies out of the boundary of a certain decision variable, the particle stays on the boundary while changing the flight direction;
step8: calculating a target vector of each particle in the particle swarm P;
step9: updating and maintaining the external particle swarm NP by using an adaptive grid method;
step10: updating the particle's pbest. Comparing the new solution obtained in the particle flight process with the best position pbest of the new solution, and if the new solution dominates the pbest, obtaining the new solution as the new pbest; otherwise, the pbest remains unchanged; if the new solution and the pbest are not subject to each other, randomly selecting one from the new solution and the pbest as a new self best position;
step11: if the termination condition is met, stopping searching, and judging whether the scheme meets three constraint conditions of production capacity, grade limitation and traffic flow control in the model; when the conditions are met, outputting an optimization scheme; otherwise, go to Step6.
In summary, aiming at the problem of scheduling and optimizing the unmanned open-pit mine truck taking truck performance into consideration, the invention aims at minimizing truck waiting time, minimizing residual yield and minimizing ore grade deviation, and builds an unmanned open-pit mine truck scheduling and optimizing model by taking different transportation efficiencies of the truck into consideration. The optimization solution is carried out by adopting a multi-target particle swarm algorithm, so that the scheduling optimization problem of the unmanned truck of the strip mine is effectively solved, and the requirement of actual mine transportation is met.
Claims (6)
1. The strip mine unmanned truck dispatching method considering the truck performance is characterized by comprising the following steps of;
step1: establishing a strip mine truck running condition database;
step2: establishing a truck operation efficiency prediction model;
step3: establishing a multi-objective unmanned strip mine truck dispatching optimization model according to the strip mine truck running condition database and the truck running efficiency prediction model;
step4: and solving the scheduling optimization model by adopting a multi-target particle swarm algorithm, and optimizing a final scheduling scheme.
2. The method for dispatching the unmanned truck in the strip mine taking truck performance into consideration as recited in claim 1, wherein the step1 is specifically: collecting historical data of truck load, completed mileage, engine condition, tire wear condition, maintenance condition and average speed of each class of each truck in the production of the strip mine, and establishing a strip mine truck running condition database; and (5) making a data statistics table of the running conditions of the strip mine trucks, and counting historical data of the running conditions of the strip mine trucks.
3. The method for dispatching the unmanned truck in the strip mine taking truck performance into consideration as recited in claim 1, wherein the step2 is specifically: by analyzing factors influencing the transportation performance of the truck, a prediction model is established, wherein five influencing factors including the load of the truck, the number of mileage driven, the number of times of engine faults, the number of mileage used by tires and the number of times of daily maintenance are taken as input parameters, and the average speed of each shift of the vehicle is taken as output parameters.
4. The method for dispatching the unmanned surface mine trucks considering the truck performance according to claim 1, wherein in the step3, according to the actual production and transportation process of the surface mine, the dispatching optimization model of the unmanned surface mine trucks is built with the aim of minimizing the waiting time of the trucks, the residual yield and the ore grade deviation; the strip mine unmanned truck dispatching optimization model comprises an objective function and constraint conditions.
5. The method for dispatching the unmanned truck of the strip mine taking truck performance into consideration as recited in claim 4, wherein the objective function is specifically:
minimizing truck transportation costs:
the transportation cost of the truck is composed of the transportation cost from the loading point to the unloading point of the heavy load of the truck, the transportation cost from the unloading point to the loading point of the empty load of the truck and the transportation cost from the unloading point to the filling point of the empty load;
minimizing truck waiting time:
the waiting time of the truck is formed by subtracting the actual running time of the truck from the working time of the truck shift, wherein the actual running time comprises the running time between loading and unloading points, the loading and unloading time, the time for going to and from a gas station and the refueling time;
minimizing residual yield:
to ensure that ore produced in one shift at a mining site can be transported as closely as possible, i.e., the capacity and capacity of the arrangement are as close as possible, the absolute value of the required yield minus the capacity is minimized;
minimizing ore grade deviation:
the sum of ore grade deviations at each unloading point is minimal;
the constraint conditions are specifically as follows:
capacity constraints for unloading points:
the total amount of ore to be transported to each unloading point must not exceed the maximum capacity of that unloading point;
yield planning requirements for unloading points:
the total amount of ore unloaded at each unloading point at least meets the production requirements of the unloading point;
capacity constraints of loading points:
the total amount of ore loaded at each loading point must not exceed the maximum capacity of that loading point;
ore grade requirement at unloading point:
the deviation of ore grade unloaded at each unloading point must not exceed the maximum deviation of grade;
traffic continuity constraints ensure equal traffic in and out of each loading/unloading point;
remaining oil mass constraint:
minimum remaining oil k=maximum oil usage from unloading point to loading point+maximum oil usage from loading point to unloading point+maximum oil usage from unloading point to refueling point O;
the number of truck shipments requires that the number of truck shipments must be a positive integer;
X ijkl ,Y ijkl ∈{0,1,2,3...}
constraint conditions under collision avoidance conditions;
when two trucks are at a certain loading point or unloading point at the same time, the fully loaded trucks are in advance preferentially, and in addition, the judgment of the priority is also applicable to the situation that the two trucks meet at the same intersection, and the empty unmanned trucks should avoid;
constraints in case of truck failure;
when a certain unmanned truck gives an early warning to a main dispatching center, the truck should be immediately excluded from the system, and the priority level of the path, loading point or unloading point of the truck is reduced, so that the planning and dispatching scheme can be adjusted in time, and the established aim of an enterprise is fulfilled;
the reference symbols used in the above formula are defined as follows:
i: index number of loading point, indicating I-th loading point (i.e. excavator position), i=1, 2, …, I, respectively; j: index number of unloading point, indicating the j-th unloading point (i.e. crushing station position), j =1,2, …, J, respectively; k: truck model index number, representing the K-th model truck, k=1, 2, …, K; l: truck number index number, indicating the first k-truck, l=1, 2, …, L, vehicle; x is X ijkl : the number of times the k-type truck numbered l reloads from the loading point i to the unloading point j, the times; y is Y ijkl : the number of times the k-type truck numbered l is unloaded from the unloading point j to the loading point i, and the times; d, d ij : the distance from the loading point i to the unloading point j is km; d, d jo : distance from unloading point j to oiling point O, km; d, d oi Optimal distance from the oiling point O to the loading point i, km; c (C) k : the loading capacity of a k-model truck, t; CE (CE) kl : the unit distance cost of the k-type truck with the number of l is Yuan/km; CL (CL) kl : the unit distance cost of the k-type truck with the number of l is equal to yuan/km when the k-type truck is empty; e (E) kl : the fuel tank capacity of the k-model truck with the number of L; EE kl : the unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is overloaded; EL (electro luminescence) kl The unit distance oil consumption of the k-type truck with the number of L is L/km when the truck is empty; k: minimum residual oil, L; gi: maximum throughput of loading point i, t; f (f) j : minimum production demand (within one shift) for unloading point j, t; q j : maximum capacity of unloading point j, t; e: limitation of ore grade; alpha i : ore grade at loading point i; beta: ore grade tolerance; g j : unloading the target grade of the point j; t (T) lim : working time of the shift; SE (SE) kl : the average speed of the k-type truck with the number of l when the k-type truck is overloaded is km/h; SL (SL) device kl : the average speed of the k-type truck with the number of l when no load exists, km/h; t (T) O Average time for truck fueling, min; t (T) z : average time of truck loading, min; t (T) q : average time for unloading of the truck, min; k (K) jOkl : the number of times the k-type truck numbered l runs from the unloading point j to the oiling point O when no load exists, and the number of times is the same; k (K) Oikl : the number of runs from fueling point O to loading point i, number i, of the k-truck no-load.
6. The method for dispatching the unmanned truck in the strip mine taking truck performance into consideration as recited in claim 5, wherein the step4 is specifically:
setting basic parameters such as inertia weight omega and flying speeds c1 and c2 of a multi-target particle swarm algorithm, and solving a strip mine unmanned truck dispatching optimization model through the multi-target particle swarm algorithm;
judging whether the current solution meets the constraints of three aspects of production capacity, grade requirement and traffic flow control, and then updating the position and speed of particles; the whole process is completed as one iteration, when the algorithm iteration is completed, a result is output, the operation process is ended, and otherwise, the multi-objective particle swarm algorithm solution is continued; the specific operation steps are as follows:
step1: initializing a particle group P; for each particle, determining the initial position and speed of the particle, and calculating the target vector of each particle in the particle group P;
step2: storing part of the particles in the particle swarm P in an external particle swarm NP, wherein the positions of the particles are non-inferior solutions;
step3: determining the initial self-best position of each particle, namely the initial position of each particle;
step4: dividing a target space into a plurality of grids (hypercube), and determining the grid of each particle according to the target vector corresponding to the particle;
step5: defining fitness values (the ratio of the number equal to or greater than 1 to the number of NP members contained in the lattices) for lattices each containing at least one external particle group individual, then selecting one lattice for each particle according to the roulette method, and randomly selecting one external particle group individual therefrom as a gbest of the particle;
step6: updating the positions and the speeds of all particles according to a basic formula of PSO;
step7: the following measures are taken to avoid particles from flying out of the search space: once a particle flies out of the boundary of a certain decision variable, the particle stays on the boundary while changing the flight direction;
step8: calculating a target vector of each particle in the particle swarm P;
step9: updating and maintaining the external particle swarm NP by using an adaptive grid method;
step10: updating the particle's pbest. Comparing the new solution obtained in the particle flight process with the best position pbest of the new solution, and if the new solution dominates the pbest, obtaining the new solution as the new pbest; otherwise, the pbest remains unchanged; if the new solution and the pbest are not subject to each other, randomly selecting one from the new solution and the pbest as a new self best position;
step11: if the termination condition is met, stopping searching, and judging whether the scheme meets three constraint conditions of production capacity, grade limitation and traffic flow control in the model; when the conditions are met, outputting an optimization scheme;
otherwise, go to Step6.
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