Improved empire competition algorithm for multi-load AGVS task scheduling

Abstract

The invention relates to the technical field of automatic guided vehicle systems, in particular to an improved empire competition algorithm for multi-load AGVS task scheduling, which comprises the following steps of: generating a plurality of station material supplementing sequence schemes according to a station material supplementing sequence heuristic generation rule; performing multi-target evaluation on each individual in the initial population; distributing individuals according to the target function value, and constructing original imperialist and colonies of the imperialist competition algorithm; an improved difference algorithm is introduced in the assimilation process, and a heuristic rule is integrated in the improved difference algorithm; after revolution and interchange operation, whether colonies with the same cost value exist in the same empire or not is judged, and if the colonies with the same cost value exist, a disturbance mechanism of a fusion heuristic rule is added; the poor imperialist and the colonies thereof are gradually eliminated through imperialist competition, finally only one strongest country is left, and the optimal country is output as the optimal scheduling scheme; the response speed is high, and the actual regulation and control effect of the regulation and control scheme can be predicted and evaluated during scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic guided vehicle systems, and in particular to an improved imperial competition algorithm for multi-load AGVS task scheduling. Background Art

[0002] In order to meet the increasingly personalized and diversified needs of customers, more and more automobile companies have transformed the traditional small-variety and large-batch production mode into a multi-variety and small-batch mixed-flow production mode. The change in production mode has led to a significant increase in the types and quantities of materials in automobile production lines. How to improve the on-time delivery of materials in automobile production lines has become a hot topic for many automobile companies. The automatic guided vehicle system is a multi-mobile robot system used for material delivery. It has many advantages such as high flexibility and automation, low operating noise, and strong system expansion capabilities. Therefore, more and more automobile companies have adopted AGVS to achieve efficient and on-time delivery of materials. However, the vehicle assembly manufacturing process is complex, the beat is fast, the material types are many, and the delivery punctuality requirements are high. Compared with the port application environment, the workshop space is relatively limited, resulting in a large scale and high density of multi-load AGVS for material delivery. The scheduling problem of multi-load AGVS in this application environment presents high complexity, dynamics and uncertainty, which has become a key problem that vehicle companies need to solve urgently. Therefore, it is necessary to study the corresponding efficient task scheduling algorithm based on the operating characteristics of multi-load AGVS in the vehicle assembly manufacturing environment.

[0003] At present, the existing multi-load AGVS task scheduling methods are all based on single or combined scheduling rules. The advantages are short decision-making time and fast response speed, but they cannot predict or evaluate the actual control effect of the control scheme during scheduling. Not only do they lack global optimization capabilities, but they are also difficult to meet the needs of multi-objective optimization and coordinated control of the system. Intelligent optimization algorithms (such as genetic algorithms, particle swarm optimization, ant colony algorithms, etc.) have strong global search capabilities, can find global optimal solutions in a large solution space, have good robustness and stability, and can dynamically adjust search strategies according to changes in the problem, thereby improving the flexibility of problem solving. In practical applications, the advantages of the two methods are often combined, and a method combining heuristic scheduling rules and intelligent optimization algorithms is used to improve the efficiency and effect of solving scheduling problems.

[0004] The imperial competition algorithm is an intelligent optimization algorithm based on population. Although ICA has fast optimization speed, good robustness and strong global optimization ability, the convergence speed of ICA is still restricted by the scale of the problem. With the decrease in the number of colonies and empires, the diversity of the population will decrease, the global optimization ability of the population will be limited, and premature phenomenon will occur easily. Therefore, according to the characteristics of multi-load AGVS task scheduling, the optimization of the station replenishment sequence is taken as the starting point, the heuristic scheduling rules and the intelligent optimization algorithm are combined, and an improved imperial competition algorithm for multi-load AGVS task scheduling is proposed. Firstly, the station replenishment sequence heuristic rule base is constructed to generate the station replenishment sequence scheme set, and the scheme set is used as part of the countries of the IICA initial country population, so that part of the initial population can be obtained; then, the improved differential evolution algorithm with fusion heuristic rules is introduced in the assimilation process to improve the convergence speed of the population; finally, after the revolution and exchange operations, the perturbation mechanism of the fusion heuristic rules is added to avoid premature phenomenon. Summary of the invention

[0005] The purpose of the present invention is to provide an improved imperial competition algorithm for multi-load AGVS task scheduling to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] An improved imperial competition algorithm for multi-load AGVS task scheduling, the method comprising:

[0008] S100, generating several workstation replenishment sequence plans according to the workstation replenishment sequence heuristic generation rule, and using them as part of the individuals in the initial population of the intelligent optimization algorithm, and the remaining individuals are randomly generated according to the rule to complete the initial population;

[0009] S200, performing multi-objective evaluation on each individual in the initial population, setting the optimization objectives to minimize the task delivery distance and maximize the remaining time of the chassis assembly line waiting for materials to be stopped, and performing weighted superposition on the two optimization objective functions, using the weighted value as the evaluation of the individual;

[0010] S300, allocate individuals according to the objective function value, and construct the original imperialists and colonies of the imperial competition algorithm;

[0011] S400, introduce an improved differential algorithm into the assimilation process and integrate heuristic rules into it;

[0012] S500. After the revolution and exchange operations, determine whether there are colonies with the same cost value in the same empire. If there are colonies with the same cost value, add a perturbation mechanism of the fusion heuristic rule;

[0013] S600. Gradually eliminate the inferior imperialists and their colonies through imperial competition, until only one strongest country remains, and export the best country as the optimal scheduling plan.

[0014] Preferably, the heuristic generation rule of the station replenishment sequence in S100 includes:

[0015] 1) The workstation with the most vacant trailers will be given priority:

[0016] According to the formula: Confirm the order of material replenishment at the workstations;

[0017] Among them, ↓ indicates that this rule will W each workstation i According to its utility value Determine the order of filling materials at the workstations from high to low. Indicates workstation W i The number of empty trailers at the current moment, Indicates AGV G m From workstation W i Number of empty trailers loaded, N G is the total number of AGVs in the system, Indicates workstation W i The number of empty trailers that have been assigned to carry AGVs but have not yet been mounted;

[0018] 2) The one with the most non-empty trailers at the workstation will be given priority:

[0019] According to the formula: Confirm the order of material replenishment at the workstations;

[0020] Among them, ↑ indicates that this rule will W each station i According to its utility value Determine the order of filling materials at the workstation from low to high; workstation W i The number of non-empty trailers at the current moment; Indicates AGV G m This delivery is to workstation W i Number of fully loaded trailers; Indicates that the transport AGV has been assigned but has not yet been delivered to the workstation W i Number of fully loaded trailers;

[0021] 3) The one with the least number of parts at the workstation will be given priority:

[0022] According to the formula: Confirm the order of material replenishment at the workstations;

[0023] in, Indicates workstation W i The number of auxiliary materials at the current moment; K i Indicates workstation W iThe maximum number of sets of auxiliary materials that can be loaded on each full trailer;

[0024] 4) Multi-attribute rules based on the current state of the system:

[0025] According to the formula: Confirm the order of material replenishment at the workstations;

[0026] 5) The one with the most empty trailers will be given priority when the next material replenishment is made:

[0027] According to the formula: Confirm the order of material replenishment at the workstations;

[0028] Among them, τ(0,i) means that the AGV runs directly from the auxiliary material inventory area to the workstation P i The average time of the vehicle assembly line; ξ is the production rhythm of the vehicle assembly line;

[0029] 6) The one with the least number of non-empty trailers will be given priority when the next material replenishment is made at the workstation:

[0030] According to the formula: Confirm the order of material replenishment at the workstations;

[0031] Among them, Ceil() represents the rounding up operation;

[0032] 7) The workstation with fewer parts sets will be given priority when refilling next time:

[0033] According to the formula: Confirm the order of material replenishment at the workstations;

[0034] 8) One or more of the multi-attribute rules based on the system state at the next refill of the workstation;

[0035] Determine the order of filling materials at the workstations according to the formula:

[0036]

[0037] Preferably, S200 includes:

[0038] S201. In order to improve the operating efficiency of multi-load AGVS, the order in which AGV visits each station must be optimized to reduce the running distance of AGV. Therefore, minimizing the average distribution distance of the task is taken as the first optimization goal, which can be expressed as:

[0039]

[0040] Among them, d(P(G m ),P0) indicates AGV G m The distance from the current dynamic position to the auxiliary material inventory, P(G m ) indicates AGV G mThe current dynamic position, P0 represents the location of the auxiliary material inventory area, Indicates AGV G m The target workstation for the nth full trailer delivery task that needs to be delivered this time, Indicates AGV G m The number of full-load trailer delivery tasks that need to be executed this time; Indicates AGV G m The execution order of this full-load trailer delivery task; Indicates AGV G m The workstation where the nth empty trailer that needs to be delivered is located; Indicates AGV G m The number of empty trailer return tasks that need to be executed this time; Indicates AGV G m The execution order of the task of returning the empty trailer to the warehouse;

[0041] S202. The purpose of auxiliary material distribution is to ensure that each assembly station has sufficient auxiliary materials to avoid the entire assembly line from stopping production due to lack of materials. Therefore, the second optimization goal is to maximize the time from the chassis assembly line entering the shutdown state due to lack of materials at a certain station, which can be expressed as:

[0042] in, For workstation W i The number of sets of auxiliary materials at time t; ξ is the production cycle of the vehicle assembly line; N W Indicates the total number of workstations on the assembly line;

[0043] S203, combining the two optimization objectives of individuals and assigning certain weight values, individual α i The objective function value is expressed as:

[0044]

[0045] Among them, k1 and k2 are weighted factors for optimizing the objective function value; v is the running speed of AGV.

[0046] Preferably, S300 includes:

[0047] S301, after decoding and evaluating the individual targets, the initial national solution set Ω is obtained α ={α1,,,α i ,,,α Q}; Among them, the solution set Ω α Every solution α i Both represent an initial state, i.e., a solution to the original problem;

[0048] Perform non-dominated sorting on the initial national solution set and select Q from the Pareto frontierimp a state as an empire;

[0049] If the number of initial countries in the Pareto frontier is less than the set value Q imp , then select from the second level Pareto frontier in turn, and the total number of remaining colonies is Q col =QQ imp ;

[0050] Among them, Q is the total number of initial countries;

[0051] S302, according to the formula Calculate the normalized cost of each imperialist, assuming that the Kth imperialist is Then the Kth imperialist The normalized cost is denoted as C K ;

[0052] in, Represents the maximum cost value among all empires; c K It's the Empire The cost of K It's the Empire The normalized cost of

[0053] S303, according to the formula Calculate the power value of each empire, where the Kth empire The power value is denoted as P K ;

[0054] According to the power value of each empire, colonies are allocated by roulette method. At this time, the country is divided into several empires, each empire consists of several colonies and an imperial country, then the Kth empire is:

[0055]

[0056] in, represents the Kth empire Ω K of imperialists; Represents EmpireΩ K The jth colony in Q K Represents EmpireΩ K The number of colonies.

[0057] Preferably, S400 includes:

[0058] S401, respectively set the order sequence of material replenishment for the workstation operator, according to the Operators implement the mutation and crossover operations required for colony assimilation:

[0059] Custom subtraction symbol

[0060] Get EmpireΩ K Two individuals in and EmpireΩ K Two individuals in and For example, the operation result Recorded as Take the 9-station replenishment sequence as an example. The implementation principle is shown in the attached figure;

[0061] For individuals Set the index number and directly Each station in is replaced by the station in The index number in , and the replaced sequence is used as Get the first workstation number W4. Take the first workstation number W4 as an example. W4 is in the individual The index number in is 3, then the vector The first workstation number is W3;

[0062] Custom addition symbol

[0063] Get EmpireΩ K Two individuals in and Vector Recorded as

[0064] Get the order sequence of 9 stations to replenish materials and display the operation results Recorded as The implementation principle is shown in the attached figure; Set the index number and directly Each workstation in the The station number corresponding to the index number is replaced as Get the first workstation number W2, individual The index number in is 2, and the corresponding workstation number is W1. Then the vector The first workstation number is W1;

[0065] Custom cross symbol

[0066] Get EmpireΩ K Two individuals in and The operation results Recorded as

[0067] Take the 9-station replenishment sequence as an example. The implementation principle is shown in the figure. First, a crossover point is randomly generated. Split into two parts, The first half of the Then the individual The second part of is reordered according to the heuristic rule, and the sorted sequence is copied back to Complete the whole

[0068] S402, to any colony Perform mutation operation: According to the formula:

[0069]

[0070] Randomly select two colonies from the colony set and and use As from New colonies obtained through mutation;

[0071] Where F represents the variation scaling factor, F min and F max are the lower and upper limits of the variation scaling factor, respectively; G max represents the total number of iterations, and g represents the current number of iterations;

[0072] S403, crossover operation: In order to promote the mutant colonies to move closer to the empire and speed up the convergence of the algorithm, according to the formula:

[0073]

[0074] The mutant colonies and its corresponding imperial colonies Perform a crossover operation to obtain the crossover probability CR;

[0075] Among them, CR min and CR max are the lower and upper bounds of the crossover probability respectively; G max represents the total number of iterations, and g represents the current number of iterations;

[0076] S404, select operation:

[0077] Assume that the number of colonies of the Kth empire is Q K , obtained through mutation and crossover operations The new colonies will be connected to the original Q KThe colonies are combined to form a candidate colony population; the cost value of each individual in the candidate population is calculated, and Q is selected from the candidate population. K optimal and different individuals as a new population; if the cost value of the optimal colony in the new colony population is better than the imperial state of the original empire, then the optimal colony will become the new imperial state of the Kth empire;

[0078] This selection operation not only ensures the steady improvement of Empire K's overall power, but also ensures diversity among colonies, thus avoiding local optimality.

[0079] Preferably, the revolution and interchange in S500 includes:

[0080] Colonial revolution is a random perturbation of the colony coding order, which makes some colonies mutate in the solution space; the colonial revolution operation is similar to the mutation operation in genetic algorithms; by adopting four revolution operation methods: random single-point exchange, random double-point exchange, random single-point forward insertion and random double-point forward insertion;

[0081] Assume that the number of colonies in empire K remains Q K , each colony undergoes the above four types of revolution operations, which means that each original colony can generate four new colonies; taking random single-point exchange as an example, two positions, position 1 and position 2, are randomly generated, and the station numbers corresponding to position 1 and position 2 are exchanged to form a new colony; when the empire Ω K After all colonies in have undergone four revolution operations, a new set of colony candidates will be obtained. The original colonies and the new colonies are merged into a candidate colony population; Q is selected by calculating the cost value of each colony in the candidate colony population. K The best colonies are used as the new colony population. Finally, the empire country is updated. If the cost value of the best colony in the new colony population is better than that of the original empire country, the best colony becomes the empire Ω K of the new imperial state.

[0082] Preferably, S500 includes:

[0083] S501, In the EmpireΩ K Select colonies with the same cost value and

[0084] S502. Keep all the workstation numbers at the same position in the two colonies directly in the disturbed new colony according to their original positions, and record the new colony as

[0085] S503: Keep the remaining non-repeated workstations as a sequence And randomly select a rule pair sequence from the constructed heuristic rule base reorder; the reordered sequence Will become a new sequence

[0086] S504, sequence Insert into sequence New colony sequences are formed in Direct replacement of the original colony

[0087] Preferred, S600 Imperial Competition, including:

[0088] The essence of imperial competition is the process of the stronger empire gradually annexing the weaker empire. The combined power of an empire includes the sum of its own power and the power of all its colonies. K As an example, the actual power is TC K , and its calculation formula is:

[0089]

[0090] Among them, C K Represents imperialists strength, μ is the strength coefficient, Q K Represents EmpireΩ K The number of colonies owned, Represents EmpireΩ K The average cost value of all colonies in the

[0091] EmpireΩ K The normalized strength is: NTC K =γMax 1≤i≤Q {TC i}-TC K ;

[0092] In the process of imperial competition, the empire with strong comprehensive strength will be the first to control the weak colonies among other weak empires. When the weak colonies are annexed one by one, the corresponding weak imperialists will also be annexed by other powerful empires. In the end, only one empire with the strongest comprehensive strength will remain, and the algorithm will terminate.

[0093] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned improved imperial competition algorithm for multi-load AGVS task scheduling.

[0094] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the improved imperial competition algorithm for multi-load AGVS task scheduling are implemented.

[0095] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0096] The present invention aims at the problem of multi-load AGVS task scheduling for large-scale auxiliary material distribution in vehicle assembly application environment, and provides a multi-load AGVS anti-deadlock task scheduling method; firstly, the method designs an anti-deadlock strategy to ensure that there is no blocking AGV in the system; secondly, in order to improve the quality of the initial population of the intelligent optimization algorithm, a heuristic rule library for generating some high-quality individuals of the initial population is established; in order to effectively reduce the scale of the solution space and facilitate the rapid finding of near-optimal solutions; the scheduling decision of the empty trailer return task and the fully loaded trailer delivery task scheduling decision are designed to decide on the anti-deadlock task scheduling scheme that meets all constraints; in order to accelerate the convergence speed of the intelligent optimization algorithm, a population evolution mechanism with elite retention strategy and neighborhood search is designed; the method combines the advantages of intelligent optimization algorithm and heuristic scheduling rules, not only has a fast response speed, but also can predict and evaluate the actual control effect of the control scheme during scheduling, has a strong global optimization ability, and can meet the needs of multi-objective optimization and anti-deadlock coordinated control of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0098] Figure 1 It is an overall flow chart of an improved imperial competition algorithm for multi-load AGVS task scheduling of the present invention;

[0099] Figure 2 It is a schematic diagram of the subtraction operation of the assimilation process definition of the present invention;

[0100] Figure 3 It is a schematic diagram of the addition operation of the assimilation process definition of the present invention;

[0101] Figure 4 It is a schematic diagram of the crossover operation of the assimilation process fusion heuristic rule definition of the present invention;

[0102] Figure 5 It is a schematic diagram of the revolution and interchange operation of the present invention;

[0103] Figure 6 It is a schematic diagram of the operation of the perturbation mechanism of the fusion heuristic rule of the present invention;

[0104] Figure 7 is the objective function value corresponding to the non-dominated frontier individuals of the improved algorithm of the present invention and the five classical algorithms;

[0105] Figure 8 The average hourly capacity of the improved algorithm of the present invention and the five classic algorithms

[0106] Fig. 9 The single task execution time of all AGVs of the improved algorithm of the present invention and the five classic algorithms is

[0107] Fig.10 The average task punctuality rate of the improved algorithm of the present invention and the five classic algorithms

[0108] Fig.11 The average production line utilization rate of the improved algorithm of the present invention and the five classic algorithms DETAILED DESCRIPTION

[0109] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] See also Figure 1-Figure 11 , the present invention provides a technical solution:

[0111] Embodiment 1:

[0112] Although there are many decision variables in the multi-load AGVS task scheduling problem for vehicle assembly and manufacturing, its fundamental purpose is to ensure the sufficiency of auxiliary materials at each assembly station. Therefore, the present invention combines the advantages of intelligent optimization algorithm and heuristic scheduling rules to propose an improved imperial competition algorithm for multi-load AGVS task scheduling.

[0113] The overall process is as follows Figure 1As shown. First, the station replenishment sequence plan set is generated through the constructed station replenishment sequence heuristic generation rule, and it is used as part of the individuals in the initial population of the intelligent optimization algorithm; then the objective function value of the individuals in the population is evaluated, and the individuals are assigned to form the original imperialists and colonies of the imperial competition algorithm; secondly, the improved differential evolution algorithm with fusion heuristic rules is introduced in the assimilation process; then, after the revolution and exchange operations, the perturbation mechanism of the fusion heuristic rules is added. Finally, the inferior imperialists and their colonies are gradually eliminated through imperial competition, and finally only the strongest imperialist and its colonies remain, and the optimal individuals are output as the optimal scheduling plan.

[0114] Improve the empire competition algorithm, including the following steps:

[0115] S100, according to the station replenishment sequence generation rule, generate several station replenishment sequence plans, and use them as some individuals in the initial population of the intelligent optimization algorithm, and complete the initial population according to the individual generation rule.

[0116] S200, perform multi-objective evaluation on each individual in the population, set the optimization goal to minimize the task delivery distance and maximize the remaining time of chassis assembly line suspension waiting for materials, and use the weighted value as the evaluation of the individual by weighted superposition of the two optimization objective functions.

[0117] S300, allocate individuals according to the objective function value distribution, and construct the original imperialists and colonies of the imperial competition algorithm.

[0118] S400, introduce an improved differential algorithm into the assimilation process and integrate heuristic rules into it.

[0119] S500. After the revolution and exchange operations, determine whether there are colonies with the same cost value in the same empire. If there are colonies with the same cost value, add a disturbance mechanism for the fusion heuristic rule.

[0120] S600. Through imperial competition, the inferior imperialists and their colonies are gradually eliminated, and finally only the strongest imperialist and his colonies remain, and the best individuals are output as the optimal scheduling plan.

[0121] Furthermore, S100 includes:

[0122] Considering that the quality of the initial population has a great influence on the convergence speed of the intelligent optimization algorithm, in order to improve the quality of the initial population, the present invention selects some state attribute indicators of the system to calculate the utility value of the auxiliary material reserve state of each workstation, and designs heuristic rules for generating the order of replenishment of the workstation. Heuristic rules can be divided into single attribute rules and multi-attribute rules according to the number of selected state attribute indicators. In the multi-load AGVS for vehicle assembly manufacturing, the state attribute indicators that can be selected mainly include: workstation W i Number of non-empty trailers Workstation W i Number of vacant trailers Workstation W i Parts Sets Workstation W i Distance d from the parts storage area i,j By selecting the above-mentioned state attribute indicators at different times or combining multiple state attribute indicators, multiple heuristic rules for generating the replenishment sequence of workstations can be constructed. Some rules and their corresponding workstations W i Utility Value The calculation method is as follows:

[0123] 1) The workstation with the most vacant trailers will be given priority:

[0124]

[0125] Where: ↓ indicates that this rule sets each station W i According to its utility value Determine the order of replenishing materials at the workstations from high to low;

[0126] Indicates workstation W i The number of empty trailers that have been assigned to carry AGVs but have not yet been mounted;

[0127] 2) The one with the least number of non-empty trailers at the workstation will be given priority:

[0128]

[0129] Where: ↑ indicates that this rule sets each station W i According to its utility value Determine the order of replenishing materials at the workstations from low to high;

[0130] Indicates that the transport AGV has been assigned but has not yet been delivered to the workstation W i Number of fully loaded trailers;

[0131] 3) The one with the least number of parts at the workstation will be given priority:

[0132]

[0133] 4) Multi-attribute rules based on the current state of the system:

[0134]

[0135] 5) The one with the most empty trailers will be given priority when the next material replenishment is made:

[0136]

[0137] 6) The one with the least number of non-empty trailers will be given priority when the next material replenishment is made at the workstation:

[0138]

[0139] In the formula: Ceil() means rounding up operation;

[0140] 7) The workstation with fewer parts sets will be given priority when refilling next time:

[0141]

[0142] 8) Multi-attribute rules based on the system status at the next refill of the workstation:

[0143]

[0144] Each of the above rules generates a workstation replenishment order sequence according to the corresponding utility value. These workstation replenishment order sequences are used as individuals of the initial population, while other individuals of the initial population are generated completely randomly.

[0145] Furthermore, the individual multi-objective evaluation in S200 includes:

[0146] First, in order to improve the operating efficiency of multi-load AGVS, the order in which AGV visits each station must be optimized to reduce the running distance of AGV. Therefore, minimizing the average delivery distance of the task is taken as the first optimization goal, which can be expressed as:

[0147]

[0148] in:

[0149]

[0150] d(P(G m ),P0) indicates AGV G m The distance from the current dynamic position to the auxiliary material inventory, P(G m ) indicates AGV G m The current dynamic position of P0 represents the position of auxiliary material inventory area; Indicates AGV G mThe target workstation for the nth full trailer delivery task that needs to be delivered this time; Indicates AGV G m The number of full-load trailer delivery tasks that need to be executed this time; Indicates AGVG m The execution order of this full-load trailer delivery task; Indicates AGV G m The workstation where the nth empty trailer that needs to be delivered is located; Indicates AGV G m The number of empty trailer return tasks that need to be executed this time;

[0151] Indicates AGV G m The execution order of the empty trailer return task.

[0152] Secondly, the purpose of auxiliary material distribution is to ensure that each assembly station has sufficient auxiliary materials to avoid the entire assembly line from stopping production due to lack of materials. Therefore, the second optimization goal is to maximize the time from the chassis assembly line entering the shutdown state due to lack of materials at a certain station, which can be expressed as:

[0153]

[0154] in:

[0155]

[0156] For workstation W i The number of sets of auxiliary materials at time t; ξ is the production cycle of the vehicle assembly line; N W Represents the total number of workstations on the assembly line.

[0157] Combining the two optimization objectives of individuals and assigning certain weight values, individual α i The objective function value is expressed as:

[0158]

[0159] k1 and k2 are weighted factors for optimizing the objective function value; v is the running speed of the AGV.

[0160] Furthermore, the original imperialists and colonies are constructed in S300, including:

[0161] S301, after decoding and evaluating the individual targets, the initial national solution set Ω is obtained α ={α1,,,α i ,,,α Q}, solution set Ω α Every solution α iis an initial country, that is, a solution to the original problem. Perform non-dominated sorting on the initial country solution set and select Q from the Pareto frontier. imp countries as empires. If the number of initial countries in the Pareto frontier is less than the set value Q imp , then select from the second level Pareto frontier in turn. The total number of remaining colonies is:

[0162] Q col =QQ imp

[0163] Among them, Q is the total number of initial countries.

[0164] S302. Calculate the normalized cost of each imperialist. Assume that the Kth imperialist is Then the Kth imperialist The normalized cost is denoted as C K ,Right now:

[0165]

[0166] in, Represents the maximum cost value among all empires; c K It's the Empire The cost of K It's the Empire The normalized cost of .

[0167] S303. Calculate the power value of each empire. The Kth empire The strength value is denoted as P K ,

[0168]

[0169] S304. According to the power value of each empire, colonies are allocated by roulette method. In this way, the national population is divided into multiple empires, each of which consists of several colonies and an imperial country. The Kth empire can be expressed as:

[0170]

[0171] in, It is the Kth Empire of the imperial state; is the jth colony of Empire K; Q K It's Empire K The number of colonies.

[0172] Furthermore, the assimilation process in S400 introduces an improved differential algorithm and incorporates heuristic rules into it, including:

[0173] S401, define the sequence of material replenishment for the workstation Operators. These operators are used to implement the mutation and crossover operations required for colony assimilation.

[0174] Custom subtraction symbol

[0175] EmpireΩ K Two individuals in and For example, the operation result Recorded as Take the 9-station replenishment sequence as an example. The implementation principle is as follows Figure 2 First, for individual Set the index number, then directly Each station in is replaced by the station in The index number in , and the replaced sequence is used as Take the first workstation number W4 as an example. The index number in is 3, then the vector The first workstation number is W3.

[0176] Custom addition symbol

[0177] EmpireΩ K Two individuals in and For example, the vector Recorded as Take the 9-station refill sequence as an example, and Recorded as The implementation principle is as follows Figure 3 First, for individual Set the index number, then directly Each workstation in the The station number corresponding to the index number is replaced as Taking the first workstation number W2 as an example, individual The index number in is 2, and the corresponding workstation number is W1. Then the vector The first workstation number is W1.

[0178] Custom cross symbol

[0179] EmpireΩ K Two individuals in and For example, the operation result Recorded as Take the 9-station replenishment sequence as an example. The implementation principle is as follows Figure 4 First, a crossover point is randomly generated. Split into two parts, The first half of the Then the individual α l K The second part of is reordered according to the heuristic rule, and the sorted sequence is copied back to Complete the whole

[0180] S402, mutation operation, for any colony Perform mutation operation as follows: Randomly select two colonies from the colony set and and use As from The new colony obtained by mutation is represented as follows:

[0181]

[0182] Where: F represents the variation scaling factor, F min and F max are the lower and upper limits of the variation scaling factor, respectively; G max represents the total number of iterations, and g represents the current number of iterations.

[0183] S403, crossover operation, in order to promote the mutant colonies to move closer to the empire and speed up the convergence of the algorithm, the mutant colonies and its corresponding imperial colonies Perform a crossover operation. The new colony after the crossover is recorded as It is expressed as follows:

[0184]

[0185] Among them: CR represents the crossover probability; CR min and CR max are the lower and upper bounds of the crossover probability respectively. ; G max represents the total number of iterations, and g represents the current number of iterations.

[0186] S404, select operation, assuming that the number of colonies of the Kth empire is Q K , Q obtained through mutation and crossover operations K The new colonies will be connected to the original Q K The colonies are combined to form a candidate colony population. The cost value of each individual in the candidate population is calculated, and Q is selected from the candidate population. KThe optimal and different individuals are selected as the new population. If the cost value of the optimal colony in the new colony population is better than the imperial state of the original empire, then the optimal colony will become the new imperial state of the Kth empire. This selection operation not only ensures the steady improvement of the overall power of Empire K, but also ensures the diversity among colonies, thereby avoiding local optimality.

[0187] S500 Revolution and Interchange, including:

[0188] Colonial revolution is a random perturbation of the colony coding order, which causes some colonies to mutate in the solution space. This operation effectively increases the search space of the algorithm. The colonial revolution operation is similar to the mutation operation in the genetic algorithm. Figure 5 As shown, this paper adopts four revolutionary operation methods: random single-point exchange, random double-point exchange, random single-point forward insertion and random double-point forward insertion.

[0189] Specifically, suppose the number of colonies in empire K remains Q K , each colony undergoes the above four types of revolution operations, which means that each original colony can generate four new colonies. Taking random single-point exchange as an example, two positions are randomly generated, position 1 and position 2, and the station numbers corresponding to position 1 and position 2 are exchanged to form a new colony. K After all colonies in have undergone four revolution operations, a new set of colony candidates will be obtained. The original colony and the new colony are merged into a candidate colony population. The cost value of each colony in the candidate colony population is calculated and Q is selected. K The best colonies are used as the new colony population. Finally, the empire country is updated. If the cost value of the best colony in the new colony population is better than the original empire country, the best colony becomes the empire Ω K of the new imperial state.

[0190] The perturbation mechanism of S500 integrates heuristic rules, including:

[0191] As attached Figure 6 As shown, the specific steps are as follows:

[0192] S501, In the EmpireΩ K Select colonies with the same cost value and

[0193] S502. Keep all the workstation numbers at the same position in the two colonies directly in the disturbed new colony according to their original positions, and record the new colony as

[0194] S503: Keep the remaining non-repeated workstations as a sequence And randomly select a rule pair sequence from the constructed heuristic rule base Reorder. The reordered sequence Will become a new sequence

[0195] S504, sequence Insert into sequence New colony sequences are formed in Direct replacement of the original colony

[0196] Further, the S600 Empire Competition includes:

[0197] The essence of imperial competition is the process of the stronger empire gradually annexing the weaker empire. The combined power of an empire includes the sum of its own power and the power of all its colonies. K As an example, the actual power is TC K , and its calculation formula is:

[0198]

[0199] Where: C K Represents imperialists strength; μ is the strength coefficient; Q K Represents EmpireΩ K Number of colonies owned; Represents EmpireΩ K The average cost of all colonies in

[0200] EmpireΩ K The normalized strength is:

[0201] NTC K =γMax 1≤i≤Q {TC i}-TC K

[0202] In the process of imperial competition, the empire with strong comprehensive strength will be the first to control the weak colonies among other weak empires. When the weak colonies are annexed one by one, the corresponding weak imperialists will also be annexed by other powerful empires. In the end, only one empire with the strongest comprehensive strength will remain, and the algorithm will terminate.

[0203] Scheduling effect:

[0204] In order to evaluate the performance of the proposed IICA, a new energy chassis production line in Nanchang, China was used as an example to conduct a case study, and the corresponding multi-load AGV simulation platform was constructed using Siemens Plant simulation 15.0 software. Through the simulation platform, the following verification experiments were conducted to verify the proposed algorithm improvement method.

[0205] This paper draws on classic algorithms (such as ADE, TICA and MOPSO) and compares them with new algorithms (such as NSGA-II and LSPM-WC) that have been used in recent years to solve similar problems.

[0206] Depend on Figure 7 It can be seen that with the increase of the number of iterations, the non-dominated frontiers of the six methods can gradually converge, which shows that the evolutionary mechanism of the intelligent optimization method can optimize the initial solution generated by the heuristic rule. Although there is no difference between the six algorithms at the beginning of 0.0 seconds, at 0.9 seconds, the gap between IICA and the other five optimization methods is obvious, which shows that ICA, which combines the improved differential evolution mechanism and the perturbation mechanism, has a better convergence effect, and from the perspective of the entire iterative process, with the increase of the number of iterations, IICA will be better than the other five evolutionary methods. And from Figure 7 (b) It can be seen that although the emerging algorithms NSGA-II and LSPM-WCA also show obvious advantages, they are always worse than IICA, which shows that the proposed improvement strategy is necessary and effective for TICA and significantly improves the convergence of IICA.

[0207] like Figure 8 As shown in the figure, when the number of AGVs is less than 55, the productivity of the chassis assembly line increases with the increase in the number of AGVs, but it is still difficult to meet the designed production capacity of 60 vehicles per hour. This phenomenon is due to the insufficient number of AGVs, which leads to limited transportation capacity, and thus directly leads to a low task on-time rate ( Fig.10 ), insufficient production line startup rate ( Fig.11 ). As the number of AGVs increases, the system's transportation capacity gradually improves, and the production capacity of the six algorithms is also improved. When the number of AGVs in the system reaches 55, IICA first achieves the design capacity of 60 vehicles per hour, with the highest average task on-time rate of 98.686%, and the highest average production line start-up rate of 99.072%. In addition, IICA is always better than the other five algorithms, which shows that the new method proposed in this paper is superior to other methods (classical algorithms and emerging algorithms) for multi-load AGVS task scheduling problems. Fig. 9,Whether it is the capacity value or the average single task execution time ,there are large differences in the standard deviation, but overall, the standard deviation of the IICA operation ,result is the smallest, which shows that under this algorithm, the higher ,the stability of the production capacity is, the smaller the fluctuation of the average single task ,execution time, and the more stable the operation of the entire transportation ,system.

[0208] Embodiment 2:

[0209] The computer-readable storage medium of this embodiment stores a computer program thereon, which, when executed by a processor, implements the steps of an improved imperial competition algorithm for multi-load AGVS task scheduling in embodiment 1.

[0210] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0211] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0212] Embodiment 3:

[0213] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of an improved imperial competition algorithm for multi-load AGVS task scheduling in embodiment 1 are implemented.

[0214] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0215] Those skilled in the art will appreciate that the disclosed content of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Moreover, the present solution may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0216] The present solution is described with reference to the method according to the embodiment of the present solution and the flowchart and / or block diagram of the computer program product. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 one or more processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0217] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 one or more processes and / or methods Figure 1 A function specified in one or more boxes.

[0218] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 one or more processes and / or methods Figure 1 The steps for the functions specified in one or more boxes.

[0219] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0220] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An improved imperial competition algorithm for multi-load AGVS task scheduling, characterized by: The method comprises: S100, generating several workstation replenishment sequence plans according to the workstation replenishment sequence heuristic generation rule, and using them as part of the individuals in the initial population of the intelligent optimization algorithm, and the remaining individuals are randomly generated according to the rule to complete the initial population; S200, performing multi-objective evaluation on each individual in the initial population, setting the optimization objectives to minimize the task delivery distance and maximize the remaining time of the chassis assembly line waiting for materials to be stopped, and performing weighted superposition on the two optimization objective functions, using the weighted value as the evaluation of the individual; S300, allocate individuals according to the objective function value, and construct the original imperialists and colonies of the imperial competition algorithm; S400, introduce an improved difference algorithm into the assimilation process and integrate heuristic rules into it; S500. After the revolution and exchange operations, determine whether there are colonies with the same cost value in the same empire. If there are colonies with the same cost value, add a perturbation mechanism of the fusion heuristic rule; S600. Gradually eliminate the inferior imperialists and their colonies through imperial competition, until only one strongest country remains, and export the best country as the optimal scheduling plan.

2. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The heuristic generation rules for the station replenishment sequence in S100 include: 1) The workstation with the most vacant trailers will be given priority: According to the formula: Confirm the order of material replenishment at the workstations; Among them, ↓ indicates that this rule will W each workstation i According to its utility value Determine the order of filling materials at the workstations from high to low. Indicates workstation W i The number of empty trailers at the current moment, Indicates AGV G m From workstation W i Number of empty trailers loaded, N G is the total number of AGVs in the system, Indicates workstation W i The number of empty trailers that have been assigned to carry AGVs but have not yet been mounted; 2) The one with the most non-empty trailers at the workstation will be given priority: According to the formula: Confirm the order of material replenishment at the workstations; Among them, ↑ indicates that this rule will W each station i According to its utility value Determine the order of filling materials at the workstation from low to high; workstation W i The number of non-empty trailers at the current moment; Indicates AGV G m This delivery is to workstation W i Number of fully loaded trailers; Indicates that the transport AGV has been assigned but has not yet been delivered to the workstation W i Number of fully loaded trailers; 3) The one with the least number of parts at the workstation will be given priority: According to the formula: Confirm the order of material replenishment at the workstations; in, Indicates workstation W i The number of auxiliary materials at the current moment; K i Indicates workstation W i The maximum number of sets of auxiliary materials that can be loaded on each full trailer; 4) Multi-attribute rules based on the current state of the system: According to the formula: Confirm the order of material replenishment at the workstations; 5) The one with the most empty trailers will be given priority when the next material replenishment is made: According to the formula: Confirm the order of material replenishment at the workstations; Among them, τ(0,i) means that the AGV runs directly from the auxiliary material inventory area to the workstation P i The average time of the vehicle assembly line; ξ is the production rhythm of the vehicle assembly line; 6) The one with the least number of non-empty trailers will be given priority when the next material replenishment is made at the workstation: According to the formula: Confirm the order of material replenishment at the workstations; Among them, Ceil() represents the rounding up operation; 7) The workstation with fewer parts sets will be given priority when refilling next time: According to the formula: Confirm the order of material replenishment at the workstations; 8) One or more of the multi-attribute rules based on the system state at the next refill of the workstation; Determine the order of filling materials at the workstations according to the formula:

3. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S200 includes: S201. In order to improve the operating efficiency of multi-load AGVS, the order in which AGV visits each station must be optimized to reduce the running distance of AGV. Therefore, minimizing the average distribution distance of the task is taken as the first optimization goal, which can be expressed as: Among them, d(P(G m ),P0) indicates AGV G m The distance from the current dynamic position to the auxiliary material inventory, P(G m ) indicates AGV G m The current dynamic position, P0 represents the location of the auxiliary material inventory area, Indicates AGV G m The target workstation for the nth full trailer delivery task that needs to be delivered this time, Indicates AGV G m The number of full-load trailer delivery tasks that need to be executed this time; Indicates AGV G m The execution order of this full-load trailer delivery task; Indicates AGV G m The workstation where the nth empty trailer that needs to be delivered is located; Indicates AGV G m The number of empty trailer return tasks that need to be executed this time; Indicates AGVG m The execution order of the task of returning the empty trailer to the warehouse; S202. The purpose of auxiliary material distribution is to ensure that each assembly station has sufficient auxiliary materials to avoid the entire assembly line from stopping production due to lack of materials. Therefore, the second optimization goal is to maximize the time from the chassis assembly line entering the shutdown state due to lack of materials at a certain station, which can be expressed as: in, For workstation W i The number of sets of auxiliary materials at time t; ξ is the production cycle of the vehicle assembly line; N W Indicates the total number of workstations on the assembly line; S203, combining the two optimization objectives of individuals and assigning certain weight values, individual α i The objective function value is expressed as: Among them, k1 and k2 are weighted factors for optimizing the objective function value; v is the running speed of the AGV.

4. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S300 includes: S301, after decoding and evaluating the individual targets, the initial national solution set Ω is obtained α ={α1,,,α i ,,,α Q }; Among them, the solution set Ω α Every solution α i Both represent an initial state, i.e., a solution to the original problem; Perform non-dominated sorting on the initial national solution set and select Q from the Pareto frontier imp a state as an empire; If the initial number of countries in the Pareto frontier is less than the set value Q imp , then select from the second level Pareto frontier in turn, and the total number of remaining colonies is Q col =QQ imp ; Among them, Q is the total number of initial countries; S302, according to the formula Calculate the normalized cost of each imperialist, assuming that the Kth imperialist is Then the Kth imperialist The normalized cost is denoted as C K ; in, Represents the maximum cost value among all empires; c K It's the Empire The cost of K It's the Empire The normalized cost of S303, according to the formula Calculate the power value of each empire, where the Kth empire The power value is denoted as P K ; According to the power value of each empire, colonies are allocated by roulette method. At this time, the country is divided into several empires, each empire consists of several colonies and an imperial country, then the Kth empire is: in, represents the Kth empire Ω K of imperialists; Represents EmpireΩ K The jth colony in Q K Represents EmpireΩ K The number of colonies.

5. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S400 includes: S401, respectively set the order sequence of material replenishment for the workstation operator, according to the Operators implement the mutation and crossover operations required for colony assimilation: Custom subtraction symbol Get EmpireΩ K Two individuals in and The operation results Recorded as For individuals Set the index number and directly Each station in is replaced by the station in The index number in , and the replaced sequence is used as Get the first workstation number W4, W4 in individual The index number in is 3, then the vector The first workstation number is W3; Custom addition symbol Get EmpireΩ K Two individuals in and Vector Recorded as Get the order sequence of 9 stations to replenish materials and display the operation results Recorded as For individuals Set the index number and directly Each workstation in the The station number corresponding to the index number is replaced as Get the first workstation number W2, individual The index number in is 2, and the corresponding workstation number is W1. Then the vector The first workstation number is W1; Custom cross symbol Get EmpireΩ K Two individuals in and The operation results Recorded as First, randomly generate a crossover point Split into two parts, The first half of the Then the individual The second part of is reordered according to the heuristic rule, and the sorted sequence is copied back to Complete the whole S402, to any colony Perform mutation operation: According to the formula: Randomly select two colonies from the colony set and and use As from New colonies obtained through mutation; Where F represents the variation scaling factor, F min and F max are the lower and upper limits of the variation scaling factor, respectively; G max represents the total number of iterations, and g represents the current number of iterations; S403, crossover operation: In order to promote the mutant colonies to move closer to the empire and speed up the convergence of the algorithm, according to the formula: The mutant colonies and its corresponding imperial colonies Perform a crossover operation to obtain the crossover probability CR; Among them, CR min and CR max are the lower and upper bounds of the crossover probability respectively; G max represents the total number of iterations, and g represents the current number of iterations; S404, select operation: Assume that the number of colonies of the Kth empire is Q K , obtained through mutation and crossover operations The new colonies will be connected to the original Q K colonies are combined to form a candidate colony population; the cost value of each individual in the candidate population is calculated, and Q is selected from the candidate population K optimal and different individuals as the new population; if the cost value of the optimal colony in the new colony population is better than the imperial state of the original empire, then the optimal colony will become the new imperial state of the Kth empire.

6. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S500 revolutions and interchanges include: Colonial revolution is a random perturbation of the colony coding order, which makes some colonies mutate in the solution space; the colonial revolution operation is similar to the mutation operation in genetic algorithms; by adopting four revolution operation methods: random single-point exchange, random double-point exchange, random single-point forward insertion and random double-point forward insertion; Assume that the number of colonies in empire K remains Q K , each colony undergoes the above four types of revolution operations, which means that each original colony can generate four new colonies; taking random single-point exchange as an example, two positions, position 1 and position 2, are randomly generated, and the station numbers corresponding to position 1 and position 2 are exchanged to form a new colony; when the empire Ω K After all colonies in have undergone four revolution operations, a new set of colony candidates will be obtained. The original colonies and the new colonies are merged into a candidate colony population; Q is selected by calculating the cost value of each colony in the candidate colony population. K The best colonies are used as the new colony population. Finally, the empire country is updated. If the cost value of the best colony in the new colony population is better than that of the original empire country, the best colony becomes the empire Ω K of the new imperial state.

7. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S500 comprises: S501, In the EmpireΩ K Select colonies with the same cost value and S502. Keep all the workstation numbers at the same position in the two colonies directly in the disturbed new colony according to their original positions, and record the new colony as S503: Keep the remaining non-repeated workstations as a sequence And randomly select a rule pair sequence from the constructed heuristic rule base reorder; the reordered sequence Will become a new sequence S504, sequence Insert into sequence New colony sequences are formed and new sequences are used Direct replacement of the original colony 8. The improved imperial competition algorithm for multi-load AGVS task scheduling as claimed in claim 1, characterized in that: The S600 Imperial Competition includes: The essence of imperial competition is the process of the stronger empire gradually annexing the weaker empire. The combined power of an empire includes the sum of its own power and the power of all its colonies. K As an example, the actual power is TC K , and its calculation formula is: Among them, C K Represents imperialists strength, μ is the strength coefficient, Q K Represents EmpireΩ K The number of colonies owned, Represents EmpireΩ K The average cost value of all colonies in the EmpireΩ K The normalized strength is: NTC K =γMax 1≤i≤Q {TC i }-TC K ; In the process of imperial competition, the empire with strong comprehensive strength will be the first to control the weak colonies among other weak empires. When the weak colonies are annexed one by one, the corresponding weak imperialists will also be annexed by other powerful empires. In the end, only one empire with the strongest comprehensive strength will remain, and the algorithm will terminate.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in an improved imperial competition algorithm for multi-load AGVS task scheduling as described in any one of claims 1-8 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the improved imperial competition algorithm for multi-load AGVS task scheduling as described in any one of claims 1-9 are implemented.

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