Production maintenance and purchase cooperative scheduling method based on improved dung beetle optimization algorithm

Abstract

The invention provides a production maintenance and purchase collaborative scheduling method and system based on an improved dung beetle optimization algorithm, a storage medium and electronic equipment, and relates to the field of production maintenance and purchase collaborative scheduling. According to production characteristics of new energy batteries, after production resources and workpiece processing tasks are obtained, the parallel machine scheduling problem of preventive maintenance and order material purchase expected arrival time is considered at the same time, collaborative optimization is carried out on production, maintenance and purchase, and a mixed integer programming model is constructed with the goal of minimizing the maximum completion time. Besides, a dung beetle optimization algorithm combined with a VND local search algorithm is designed, the convergence speed and the search capability of the algorithm are greatly enhanced, and new energy battery manufacturing enterprises can be effectively helped to reduce the number of preventive maintenance times, so that the maintenance cost is reduced, meanwhile, more efficient production scheduling is realized, and the enterprise competitiveness is improved.

Description

Technical Field

[0001] The present invention relates to the field of production maintenance and procurement collaborative scheduling, and in particular to a production maintenance and procurement collaborative scheduling method, system, storage medium and electronic equipment based on an improved dung beetle optimization algorithm. Background Art

[0002] In the context of new energy battery manufacturing, with the urgent global demand for sustainable development and clean energy, the production and research and development of new energy batteries have become the key driving force for the development of emerging industries such as new energy vehicles and smart grids.

[0003] For new energy battery manufacturing companies, parallel machine production is a common production method in workshops. Parallel machine scheduling is also a very important workshop scheduling problem in actual production scheduling. It mainly studies the sorting and scheduling problems of multiple workpieces processed on multiple parallel machines. In addition, unexpected machine downtime will cause production interruptions, resulting in poor efficiency and other problems. Therefore, preventive maintenance must be integrated with parallel machine scheduling. Preventive maintenance here means that the continuous processing time of the workpiece or the age of the machine cannot exceed the threshold value of the preset maintenance period.

[0004] However, related technologies have either only studied the scheduling problem of multiple parallel machines considering preventive maintenance, and the form of preventive maintenance is still mainly fixed time intervals and regular maintenance, or only studied the scheduling problem of parallel machines considering the expected arrival time of workpiece procurement. Both cannot directly provide decision support for new energy battery manufacturers. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies in the prior art, the present invention provides a production maintenance and procurement collaborative scheduling method, system, storage medium and electronic device based on an improved dung beetle optimization algorithm, which solves the technical problem of parallel machine scheduling integration of preventive maintenance and expected arrival time of workpiece procurement for new energy battery manufacturing enterprises.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A production maintenance and procurement collaborative scheduling method based on an improved dung beetle optimization algorithm comprises:

[0010] Obtain production resources and workpiece processing tasks;

[0011] Based on the production resources and workpiece processing tasks, considering the preventive maintenance of the machine, and under the known constraint of the expected arrival time of each workpiece procurement, with the goal of minimizing the maximum completion time, a mixed integer programming model is constructed;

[0012] The mixed integer programming model is solved by the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0013] Preferably, the mixed integer programming model includes:

[0014]

[0015]

[0016] X ijk ∈{0, 1}, Y ik ∈{0,1} (12)

[0017] Wherein, formula (1) represents minimizing the maximum completion time; Min represents the minimization function, C max represents the maximum completion time of all machines; i represents the machine index, m represents the number of machines; C i represents the completion time of machine i;

[0018] Constraint (2) indicates that a workpiece can only be assigned to one position on one machine; j represents the workpiece index, n represents the number of workpieces; X ijk is a decision variable, which is 1 if workpiece j is scheduled to be processed at the kth position of machine i, otherwise it is 0;

[0019] Constraint (3) indicates that a workpiece can only be assigned to one position on one machine; k represents the position index of the workpiece processed on the machine;

[0020] Constraint (4) means that no preventive maintenance is performed after the last workpiece of each machine is processed; Y in Indicates whether maintenance is performed after the last workpiece of machine i is processed;

[0021] Constraint (5) indicates that the start processing time of each position of each machine is greater than or equal to the arrival time of the workpiece processed at that position; ST ik represents the starting processing time of the kth position of machine i; r j represents the expected arrival time of the procurement of workpiece j;

[0022] Constraint (6) indicates that the start processing time of each position of each machine is greater than or equal to the completion time of the previous position plus the preventive maintenance time (if maintenance is required); CT i(k-1)represents the completion time of the k-1th position of machine i; T represents the fixed preventive maintenance time; Y i(k-1) is a decision variable, which is 1 if preventive maintenance is performed after machining at the k-1th position of machine i, and 0 otherwise;

[0023] Constraint (7) indicates that the completion time of each position of each machine is greater than or equal to the start time plus the processing time of the workpiece; CT ik represents the completion time of the kth position of machine i;

[0024] Constraint (8) represents the cumulative processing time of the first position of each machine; Q i1 represents the cumulative processing time of the first position of each machine;

[0025] Constraint (9) represents the cumulative processing time of each position of each machine; Q i(k-1) represents the cumulative processing time of the k-1th position of machine i; p j represents the processing time of workpiece j; M represents a large positive integer;

[0026] Constraint (10) indicates that the accumulated processing time of each position of each machine cannot exceed the threshold value UT; Q ik represents the cumulative processing time of the kth position of machine i; UT represents the threshold value of the preset maintenance period;

[0027] Constraint (11) represents the completion time of each workpiece; C j represents the completion time of workpiece j;

[0028] Constraints (12) give the variables X ijk , Y ik The domain of definition.

[0029] Preferably, the mixed integer programming model is solved by using a dung beetle optimization algorithm combined with a VND local search algorithm to obtain an optimal machine allocation sequence and workpiece processing sequence; including:

[0030] S31, initialize the parameters of the dung beetle optimization algorithm, including the dung beetle population N, the problem latitude D, and the maximum number of iterations T max , the number of neighborhood searches q;

[0031] The machine-workpiece double-layer list structure is used to encode the position of the dung beetle individual, which is expressed as x = [π 1 , π 2 ],π 1 Assign a code to the machine, π 2 Encode the workpiece processing sequence;

[0032] S32, using a mixed method of random strategy and heuristic algorithm to initialize the population, and dividing the population into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles;

[0033] S33, calculating the fitness of the dung beetle individual based on formula (1);

[0034] S34, respectively updating the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the stealing dung beetle;

[0035] S35, adopting a greedy strategy to update the dung beetle individuals and their fitness values;

[0036] S36, based on the preset neighborhood structure, using the VND algorithm to perform local search for the global optimal solution;

[0037] S37, repeat S33 to S36 until the number of iterations t ≥ T max , output the global optimal solution and its fitness value, and decode the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing order sequence.

[0038] Preferably, the population is initialized by a mixture of random strategy and heuristic algorithm, and is divided into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles; specifically, the following includes:

[0039] S321, sorting the workpieces in ascending order according to the arrival time of the workpieces according to the first heuristic rule;

[0040] S322, adjusting the order of workpieces with the same arrival time according to the LPT rule according to the second heuristic rule;

[0041] S323, take the first m workpieces and arrange them in the first position on the m machines respectively;

[0042] S324, repeat S323, and assign workpieces to the machines in order;

[0043] S325, when the cumulative processing time after a certain machine is assigned to a workpiece exceeds the threshold value UT, reselect the workpiece from the remaining workpieces according to the third heuristic rule;

[0044] Among them, the third heuristic rule specifically means that when the workpieces assigned to the machine are determined, the smaller the difference between the continuous processing time of the workpiece batch between two preventive maintenance and the threshold value UT, the better;

[0045] S326, obtaining a feasible solution, and placing the encoded dung beetle individuals into the initial population;

[0046] S327, using a random strategy to generate N-1 dung beetle individuals and putting them into the initial population;

[0047] S328. Divide the obtained initial population into four roles, namely, rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles, according to a preset ratio of 6:6:7:11.

[0048] Preferably, the respectively updating of the positions of the ball-rolling dung beetle, the breeding dung beetle, the foraging dung beetle and the stealing dung beetle comprises:

[0049] (1) For the ball-rolling dung beetle:

[0050] Generate a random number δ between 0 and 1. If δ<0.9, perform the location update in barrier-free mode, otherwise perform the location update in barrier-free mode.

[0051] (a) Accessible mode, the position update formula is defined as follows:

[0052]

[0053] Among them, x e (t) represents the position of the e-th dung beetle in the population at the t-th iteration; the coefficient h∈(0, 0.2] is the deflection coefficient constant, b∈(0, 1) is a fixed constant, and α is a natural coefficient with a value of -1 or 1; X w represents the worst position in the current population, and △x represents the change in light intensity, which is used to guide the position update of the dung beetle;

[0054] (b) In the obstacle mode, the tangent function is used to simulate the dancing behavior to determine the new rolling direction. The position update formula of the dung beetle's dancing behavior is defined as follows:

[0055] x e (t+1)=x e (t)+tan(θ)×|x e (t)-x e (t-1)| (14) where the deflection angle θ∈[0,π], when or π, the position of the dung beetle will not be updated;

[0056] (2) For breeding dung beetles

[0057] Introduce a boundary selection strategy to simulate the oviposition area selection of female dung beetles:

[0058]

[0059] Among them, X * Represents the local optimal position in the current population, Lb * and Ub * The lower and upper bounds of the spawning area are defined respectively. Lb and Ub are the lower and upper bounds of the optimization problem respectively;

[0060] After the egg-laying area is determined, the female dung beetle will lay eggs in this area, and the position update formula is defined as follows:

[0061] B e (t+1)=X * +b 1 ×(B e (t)-Lb * )+b 2 ×(B e (t)-Ub * ) (16)

[0062] Among them, B e (t) represents the position of the e-th dung beetle egg at the t-th iteration, b 1 and b 2 represents two independent random vectors whose dimensions are the same as the dimension D of the optimization problem;

[0063] (3) For foraging dung beetles

[0064] The update formula is as follows:

[0065]

[0066] Among them, X b is the global optimal position of the dung beetle population during the iteration process, Lb b and Ub b The lower and upper bounds of the foraging area are defined respectively;

[0067] The position update formula of the dung beetle is defined as follows:

[0068] x i (t+1)=X b +C 1 ×(x i (t)-Lb b )+C 2 ×(x i (t)-Ub b ) (18)

[0069] Among them, C 1 represents a random variable that follows a normal distribution, C 2 represents a random vector of size 1×D belonging to (0, 1);

[0070] (4) For the stealing dung beetle

[0071] The position update formula is defined as follows:

[0072] x e (t+1)=Xb +S×g×(|x e (t)-X * |+|x e (t)-X b |) (19)

[0073] Where S represents a fixed constant and g represents a random vector of size 1×D that follows a normal distribution.

[0074] Preferably, the greedy strategy is specifically expressed as:

[0075]

[0076] Among them, x i (t) represents the dung beetle individuals in the current iteration population, x e(new) (t+1) represents the dung beetle individual after position update, and f(x·) represents the fitness function.

[0077] Preferably, the following four neighborhood structures are designed:

[0078] First Neighborhood Structure: Randomly swapping two different artifacts on a machine;

[0079] Second neighborhood structure: select the machine with the largest completion time as the key machine, select the workpiece with the smallest processing time on the machine and assign it to the machine with the smallest maximum completion time;

[0080] The third neighborhood structure: select the workpiece with longer processing time from the machine with the largest completion time and exchange it with the workpiece with shorter processing time on the machine with the smallest completion time;

[0081] Fourth neighborhood structure: adopt the left shift strategy for the workpieces after the last preventive maintenance on the machine and insert them into the previous idle waiting time;

[0082] The method uses the VND algorithm to perform local search for the global optimal solution based on the preset neighborhood structure; specifically includes:

[0083] S361, determine the initial solution as the optimal dung beetle position X b , let the neighborhood solution be equal to the initial solution;

[0084] S362, executing the first neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0085] S363, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S364;

[0086] S364, executing the second neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0087] S365, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S366;

[0088] S366, executing the third neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, then updating the original neighborhood solution;

[0089] S367, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S368;

[0090] S368, executing the fourth neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0091] S369: If the final neighborhood solution is better than the initial solution, the initial solution is updated and the process goes to S362; otherwise, the algorithm ends and the optimal individual and the optimal fitness value are output.

[0092] A production maintenance and procurement collaborative scheduling system based on an improved dung beetle optimization algorithm, comprising:

[0093] Acquisition module, used to acquire production resources and workpiece processing tasks;

[0094] A construction module is used to consider preventive maintenance of the machine based on the production resources and the workpiece processing tasks, and to construct a mixed integer programming model with the goal of minimizing the maximum completion time under the constraint of the known expected arrival time of each workpiece procurement;

[0095] The solution module is used to solve the mixed integer programming model by using the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0096] A storage medium stores a computer program for production maintenance and procurement collaborative scheduling based on an improved dung beetle optimization algorithm, wherein the computer program enables a computer to execute the production maintenance and procurement collaborative scheduling method as described above.

[0097] An electronic device, comprising:

[0098] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the production maintenance and procurement collaborative scheduling method as described above.

[0099] (III) Beneficial effects

[0100] The present invention provides a production maintenance and procurement collaborative scheduling method, system, storage medium and electronic device based on an improved dung beetle optimization algorithm. Compared with the prior art, it has the following advantages:

[0101] Beneficial effects:

[0102] This invention targets the production characteristics of new energy batteries. After obtaining production resources and workpiece processing tasks, it considers the parallel machine scheduling problem of the expected arrival time of preventive maintenance and order material procurement, and performs collaborative optimization of production, maintenance and procurement. With the goal of minimizing the maximum completion time, a mixed integer programming model is constructed. In addition, a dung beetle optimization algorithm combined with a VND local search algorithm is designed, which greatly enhances the convergence speed and search ability of the algorithm, and can effectively help new energy battery manufacturers reduce the number of preventive maintenance, thereby reducing maintenance costs, while achieving more efficient production scheduling and improving corporate competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0104] Figure 1 A block diagram of a production maintenance and procurement collaborative scheduling method based on an improved dung beetle optimization algorithm provided by an embodiment of the present invention;

[0105] Figure 2 An example Gantt chart of a parallel machine scheduling problem model for preventive maintenance and expected arrival time of order material procurement provided by an embodiment of the present invention;

[0106] Figure 3 A flowchart of a dung beetle optimization algorithm combined with a VND local search algorithm provided by an embodiment of the present invention;

[0107] Figure 4 An example Gantt chart of an optimal machine allocation sequence and workpiece processing sequence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0108] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are 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.

[0109] The embodiments of the present application solve the technical problem of parallel machine scheduling integration of preventive maintenance and expected arrival time of workpiece procurement for new energy battery manufacturers by providing a production maintenance and procurement collaborative scheduling method, system, storage medium and electronic equipment based on an improved dung beetle optimization algorithm.

[0110] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0111] In an embodiment of the present invention, in order to solve the scheduling problem of multiple parallel machines considering preventive maintenance and workpiece arrival time, the machine needs to perform PM flexible maintenance (i.e., preventive maintenance) to ensure that it is in a normal state, so that the continuous processing time of the workpiece or the age of the machine cannot exceed the threshold value UT during the maintenance period. The workpiece arrival time is known, and a mathematical model is established with the goal of minimizing the maximum completion time.

[0112] The scheduling problem can be expressed as Pm|r j ,nr,FPM|C max According to the three-stage representation of α|β|γ, α represents the equipment environment in the production and processing system, α=Pm represents the parallel machines of the same type and speed in the production system; Xi represents the constraints and operation characteristics in the production system, β={r j , nr, FPM} represents the arrival time of workpieces, workpiece non-interruptibility and flexible maintenance; γ represents the optimization goal of the scheduling problem, γ=C max The optimization goal of the scheduling problem is to minimize the maximum completion time.

[0113] In addition, based on Pm|r j ,nr,FPM|C max Based on the characteristics and optimization properties of the algorithm, a heuristic algorithm containing multiple heuristic rules is designed to generate the initial solution, thereby improving the quality of the initial solution. Then, it is combined with the improved DBO algorithm for iterative optimization. The VND local search algorithm containing multiple neighborhood structures is combined with the standard DBO algorithm, which greatly enhances the convergence speed and search ability of the algorithm, and can effectively help factories reduce the number of preventive maintenance, thereby reducing maintenance costs, while achieving more efficient production scheduling and improving corporate competitiveness.

[0114] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0115] Embodiment 1:

[0116] like Figure 1 As shown, an embodiment of the present invention provides a production maintenance and procurement collaborative scheduling method based on an improved dung beetle optimization algorithm, comprising:

[0117] S1. Obtain production resources and workpiece processing tasks;

[0118] S2. Based on the production resources and workpiece processing tasks, consider the preventive maintenance of the machine, and build a mixed integer programming model with the goal of minimizing the maximum completion time under the known expected arrival time of each workpiece purchase;

[0119] S3. Use the dung beetle optimization algorithm combined with the VND local search algorithm to solve the mixed integer programming model to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0120] The embodiment of the present invention targets the production characteristics of new energy batteries. After obtaining production resources and workpiece processing tasks, it considers the parallel machine scheduling problem of the expected arrival time of preventive maintenance and order material procurement at the same time, performs collaborative optimization of production, maintenance and procurement, and constructs a mixed integer programming model with the goal of minimizing the maximum completion time. In addition, a dung beetle optimization algorithm combined with the VND local search algorithm is designed, which greatly enhances the convergence speed and search ability of the algorithm, and can effectively help new energy battery manufacturers reduce the number of preventive maintenance, thereby reducing maintenance costs, while achieving more efficient production scheduling and improving corporate competitiveness.

[0121] First of all, it should be noted that the embodiment of the present invention is aimed at the parallel machine scheduling problem of the expected arrival time of the preventive maintenance and order material procurement, which is a NP-hard problem. The example Gantt chart of the problem model is as follows: Figure 2 As shown, the relevant problem description is:

[0122] Assume that there is a set of non-interruptible workpieces J = {1, 2, ..., n} that need to be processed on m equivalent parallel machines M = {1, 2, ..., m}. The processing time of workpiece j is p j , arrival time r j , completion time C j The total processing time of all workpieces is S. The starting processing time, cumulative processing time, and completion time of the kth position of machine i are represented by ST ik , Q ik and CT ik The machine needs to be maintained by PM elastic maintenance to ensure that it is in a normal state, that is, the continuous processing time of the workpiece or the age of the machine cannot exceed the threshold value UT during the maintenance period, and p j ≤UT. The time required for maintenance work is fixed at T. Workpieces are not allowed to be interrupted during processing. The collection of all workpieces between two consecutive PMs is called a batch, and the sum of their processing time must be less than or equal to UT.

[0123] Next, we will introduce the steps of the above scheme in detail:

[0124] In step S1, production resources and workpiece processing tasks are obtained.

[0125] In this step, the production resources and workpiece processing task data used for mathematical modeling in the subsequent steps are obtained, and the model variables and descriptions shown in Table 1 are defined:

[0126] Table 1

[0127]

[0128]

[0129] In step S2, based on the production resources and workpiece processing tasks, preventive maintenance of the machine is considered, and a mixed integer programming model is constructed with the goal of minimizing the maximum completion time under the known constraint of the expected arrival time of each workpiece procurement.

[0130] It is necessary to clarify the following assumptions related to model construction in advance:

[0131] (1) Workpieces arrive dynamically, and each workpiece has an arrival time;

[0132] (2) The processing time and arrival time of the workpiece are known in advance;

[0133] (3) The maintenance cycle and maintenance time are known in advance;

[0134] (4) The time it takes to process the same workpiece on any machine is the same;

[0135] (5) Each machine can only process one workpiece at any time and the processing should be continuous and cannot be interrupted by other workpieces;

[0136] (6) There is no distinction in importance between artifacts, that is, weight is not considered.

[0137] On this basis, this step constructs the following mixed integer programming model:

[0138]

[0139]

[0140] X ijk ∈{0,1},Y ik ∈{0,1} (12)

[0141] Wherein, formula (1) represents minimizing the maximum completion time; Min represents the minimization function,

[0142] Constraint (2) indicates that a workpiece can only be assigned to one position on one machine;

[0143] Constraint (3) indicates that a workpiece can only be assigned to one position on one machine; k represents the position index of the workpiece processed on the machine;

[0144] Constraint (4) means that no preventive maintenance is performed after the last workpiece of each machine is processed;

[0145] Constraint (5) indicates that the start processing time of each position of each machine is greater than or equal to the arrival time of the workpiece processed at that position;

[0146] Constraint (6) indicates that the start processing time of each position of each machine is greater than or equal to the completion time of the previous position plus the preventive maintenance time (if maintenance is required);

[0147] Constraint (7) indicates that the completion time of each position of each machine is greater than or equal to the start time plus the processing time of the workpiece;

[0148] Constraint (8) represents the cumulative processing time of the first position of each machine;

[0149] Constraint (9) represents the cumulative processing time of each position of each machine;

[0150] Constraint (10) indicates that the accumulated processing time of each position of each machine cannot exceed the threshold value UT;

[0151] Constraint (11) represents the completion time of each workpiece;

[0152] Constraints (12) give the variables X ijk , Y ik The domain of definition.

[0153] In step S3, the mixed integer programming model is solved by using the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0154] In this step, a heuristic algorithm containing multiple heuristic rules is first designed to generate an initial solution, thereby improving the quality of the initial solution; then, it is combined with the improved DBO algorithm for iterative optimization. The VND local search algorithm containing multiple neighborhood structures is combined with the standard DBO algorithm, which greatly enhances the convergence speed and search ability of the algorithm.

[0155] It should be noted that the Dung Beetle Optimizer (DBO) is a new swarm intelligence optimization algorithm, which is inspired by the rolling, dancing, reproduction, foraging and stealing behaviors of dung beetles in nature. The optimization variable is used as the position of the dung beetle in the solution space, and its position is changed by modeling its behavior. It continuously iterates and optimizes in multi-dimensional space to find the global optimal solution that meets the requirements.

[0156] like Figure 3 As shown, Figure 3 A complete flowchart of the dung beetle optimization algorithm combined with the VND local search algorithm is given. Figure 3 , the specific steps are as follows:

[0157] S31, initialize the parameters of the dung beetle optimization algorithm, including the dung beetle population N, the problem latitude D, and the maximum number of iterations T max , number of neighborhood searches q.

[0158] The machine-workpiece double-layer list structure is used to encode the position of the dung beetle individual, which is expressed as x = [π 1 ,π 2 ],π 1 Assign a code to the machine, π 2 Encode the workpiece processing sequence.

[0159] It can be understood that the corresponding decoding rule means that by decoding the individual positions of dung beetles into workpiece processing sequence and machine allocation sequence according to certain rules, a scheduling Gantt chart can be output.

[0160] For example, π 1 =[1,2,2,3,1], which means that workpiece 1 is assigned to machine 1 for processing, workpiece 2 is assigned to machine 2 for processing, workpiece 3 is assigned to machine 2 for processing, workpiece 4 is assigned to machine 3 for processing, and workpiece 5 is assigned to machine 1 for processing. π 2 =[5,2,4,1,3], indicating that the processing order of the workpieces is to process workpiece 5 first, and then workpiece 2, workpiece 4, workpiece 1 and workpiece 3 in sequence.

[0161] S32, using a random strategy and a heuristic algorithm to mix and initialize the population, and divide the population into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles, and stealing dung beetles; specifically including:

[0162] S321. Sort the workpieces according to the first heuristic rule from the smallest to the largest arrival time.

[0163] It is not difficult to understand that the first heuristic rule specifically means that the first arriving workpieces should be produced first, which can reduce unnecessary waiting time and thus shorten the maximum completion time.

[0164] S322. According to the second heuristic rule, the order of the workpieces with the same arrival time is adjusted according to the LPT rule.

[0165] It is not difficult to understand that the second heuristic rule specifically means that the closer the sum of the processing time of the workpieces assigned to each machine is, the smaller the maximum completion time will be, so a method is designed to evenly distribute the workpieces according to their working hours.

[0166] S323, take the first m workpieces and arrange them in the first position on the m machines respectively;

[0167] S324, repeat S323, and assign workpieces to the machines in sequence.

[0168] S325. When the cumulative processing time after a certain machine is assigned to a workpiece exceeds the threshold value UT, a workpiece is reselected from the remaining workpieces according to the third heuristic rule.

[0169] Among them, the third heuristic rule specifically means that when the workpieces assigned to the machine are determined, the smaller the difference between the continuous processing time of the workpiece batch between two preventive maintenance and the threshold value UT, the better;

[0170] S326. Obtain a feasible solution and place the encoded dung beetle individuals into the initial population.

[0171] S327. Use a random strategy to generate N-1 dung beetle individuals and put them into the initial population.

[0172] S328. Divide the obtained initial population into four roles, namely, rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles, according to a preset ratio of 6:6:7:11.

[0173] S33. Calculate the fitness of the dung beetle individual based on formula (1).

[0174] S34, respectively updating the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the stealing dung beetle; specifically including:

[0175] (1) For the ball-rolling dung beetle:

[0176] Generate a random number δ between 0 and 1. If δ<0.9, perform the location update in barrier-free mode, otherwise perform the location update in barrier-free mode.

[0177] (a) In the barrier-free mode, the dung beetle rolls the dung ball along a straight path by using the intensity of sunlight as a guide. The position update formula is defined as follows:

[0178]

[0179] Among them, xe (t) represents the position of the e-th dung beetle in the population at the t-th iteration; the coefficient h∈(0, 0.2] is the deflection coefficient constant, b∈(0, 1) is a fixed constant, and α is a natural coefficient with a value of -1 or 1 (its value can be set by presetting a probability value and randomly generating a random number from 0 to 1. If the generated random number is less than the probability value, it takes 1, otherwise it takes -1); X w represents the worst position in the current population, and △x represents the change in light intensity, which is used to guide the dung beetle's position update.

[0180] (b) Obstacle mode: When the dung beetle encounters an obstacle and cannot move forward while rolling the dung ball, it needs to adjust its forward direction by performing a dance action. The tangent function is used to simulate the dance behavior to determine the new rolling direction. The position update formula of the dung beetle's dance behavior is defined as follows:

[0181] x e (t+1)=x e (t)+tan(θ)×|x e (t)-x e (t-1)| (14)

[0182] Among them, the deflection angle θ∈[0,π], when or π, the position of the dung beetle will not be updated;

[0183] (2) For breeding dung beetles

[0184] In nature, female dung beetles roll their dung balls to a safe place and hide them for laying eggs. Selecting a suitable oviposition site is one of their important behaviors. Based on this ecological behavior, a boundary selection strategy is introduced to simulate the oviposition area selection of female dung beetles:

[0185]

[0186] Among them, X * Represents the local optimal position in the current population, Lb * and Ub * The lower and upper bounds of the spawning area are defined respectively. Lb and Ub are the lower and upper bounds of the optimization problem respectively;

[0187] After the egg-laying area is determined, the female dung beetle will lay eggs in this area. From formula (15), it can be seen that the dynamic change of the boundary of the egg-laying area is determined by the value of R. Therefore, the position of the dung beetle eggs is also dynamic during the iteration process. The position update formula is defined as follows:

[0188] B e (t+1)=X * +b 1 ×(Be (t)-Lb * )+b 2 ×(B e (t)-Ub * ) (16)

[0189] Among them, B e (t) represents the position of the e-th dung beetle egg at the t-th iteration, b 1 and b 2 represents two independent random vectors whose dimensions are the same as the dimension D of the optimization problem;

[0190] (3) For foraging dung beetles

[0191] This behavior is mainly aimed at hatched dung beetles. When they mature, they will drill out of the soil to look for food. The boundaries of their optimal foraging area will also be dynamically updated. The update formula is as follows:

[0192]

[0193] Among them, X b is the global optimal position of the dung beetle population during the iteration process, Lb b and Ub b The lower and upper bounds of the foraging area are defined respectively;

[0194] The position update formula of the dung beetle is defined as follows:

[0195] x i (t+1)=X b +C 1 ×(x i (t)-Lb b )+C 2 ×(x i (t)-Ub b ) (18)

[0196] Among them, C 1 represents a random variable that follows a normal distribution, C 2 represents a random vector of size 1×D belonging to (0, 1). It should be noted that, like the breeding dung beetles, the individual position update of the foraging dung beetles also needs to be corrected according to the foraging area.

[0197] (4) For the stealing dung beetle

[0198] In a population, some dung beetles will exhibit the behavior of stealing dung balls from other individuals. The position update formula of such dung beetles is defined as follows:

[0199] x e (t+1)=X b +S×g×(|x e(t)-X * |+|x e (t)-X b |) (19)

[0200] Where S represents a fixed constant and g represents a random vector of size 1×D that follows a normal distribution.

[0201] S35. Use a greedy strategy to update dung beetle individuals and their fitness values.

[0202] The algorithm uses the greedy rule to compare the updated position with the original position to select individuals with better fitness to enter the next iteration. The greedy rule here can be expressed as:

[0203]

[0204] Among them, x i (t) represents the dung beetle individuals in the current iteration population, x e(new) (t+1) represents the dung beetle individual after position update, and f(x·) represents the fitness function.

[0205] S36. Based on the preset neighborhood structure, a VND algorithm is used to perform a local search for the global optimal solution.

[0206] In order to further improve the local search capability of the algorithm, an embodiment of the present invention designs a local search algorithm that includes four neighborhood structures as shown below. These four different neighborhood structures are used simultaneously during the local search process, which can prevent the algorithm from falling into the local optimum, thereby improving the quality of the global search.

[0207] First Neighborhood Structure: Randomly swapping two different artifacts on a machine;

[0208] Second neighborhood structure: select the machine with the largest completion time as the key machine, select the workpiece with the smallest processing time on the machine and assign it to the machine with the smallest maximum completion time;

[0209] The third neighborhood structure: select the workpiece with longer processing time from the machine with the largest completion time and exchange it with the workpiece with shorter processing time on the machine with the smallest completion time;

[0210] Fourth neighborhood structure: adopt the left shift strategy for the workpieces after the last preventive maintenance on the machine and insert them into the previous idle waiting time;

[0211] Accordingly, based on the preset neighborhood structure, the VND algorithm is used to perform local search for the global optimal solution; specifically, it includes:

[0212] S361, determine the initial solution as the optimal dung beetle position X b , let the neighborhood solution be equal to the initial solution;

[0213] S362, executing the first neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0214] S363, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S364;

[0215] S364, executing the second neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0216] S365, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S366;

[0217] S366, executing the third neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, then updating the original neighborhood solution;

[0218] S367, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S368;

[0219] S368, executing the fourth neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution;

[0220] S369: If the final neighborhood solution is better than the initial solution, the initial solution is updated and the process goes to S362; otherwise, the algorithm ends and the optimal individual and the optimal fitness value are output.

[0221] S37, repeat S33 to S36 until the number of iterations t ≥ T max , output the global optimal solution and its fitness value, and decode the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing order sequence.

[0222] For example, Figure 4 As shown, Figure 4 An example Gantt chart of the optimal machine allocation sequence and workpiece processing sequence is given.

[0223] So far, the embodiment of the present invention has completed the entire process of the production maintenance and procurement collaborative scheduling method based on the improved dung beetle optimization algorithm.

[0224] Embodiment 2:

[0225] The embodiment of the present invention provides a production maintenance and procurement collaborative scheduling system based on an improved dung beetle optimization algorithm, including:

[0226] Acquisition module, used to acquire production resources and workpiece processing tasks;

[0227] A construction module is used to consider preventive maintenance of the machine based on the production resources and the workpiece processing tasks, and to construct a mixed integer programming model with the goal of minimizing the maximum completion time under the constraint of the known expected arrival time of each workpiece procurement;

[0228] The solution module is used to solve the mixed integer programming model by using the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0229] Embodiment 3:

[0230] An embodiment of the present invention provides a storage medium storing a computer program for collaborative scheduling of production, maintenance and procurement based on an improved dung beetle optimization algorithm, wherein the computer program enables a computer to execute the collaborative scheduling method of production, maintenance and procurement as described in Example 1.

[0231] Embodiment 4:

[0232] An embodiment of the present invention provides an electronic device, including:

[0233] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the production maintenance and procurement collaborative scheduling method as described in Example 1

[0234] It can be understood that the production maintenance and procurement collaborative scheduling system based on the improved dung beetle optimization algorithm, storage medium and electronic device provided in the embodiments of the present invention correspond to the production maintenance and procurement collaborative scheduling method based on the improved dung beetle optimization algorithm provided in the embodiments of the present invention. The explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the production maintenance and procurement collaborative scheduling method, and will not be repeated here.

[0235] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0236] 1. The embodiment of the present invention aims at the production characteristics of new energy batteries. After obtaining production resources and workpiece processing tasks, the parallel machine scheduling problem of the expected arrival time of preventive maintenance and order material procurement is considered at the same time, and the production, maintenance and procurement are collaboratively optimized. With the goal of minimizing the maximum completion time, a mixed integer programming model is constructed. In addition, a dung beetle optimization algorithm combined with the VND local search algorithm is designed, which greatly enhances the convergence speed and search ability of the algorithm, and can effectively help new energy battery manufacturers reduce the number of preventive maintenance, thereby reducing maintenance costs, while achieving more efficient production scheduling and improving corporate competitiveness.

[0237] 2. The embodiment of the present invention aims to minimize the maximum completion time, studies the scheduling problem of multiple equivalent parallel machines that considers both preventive maintenance and workpiece arrival time, designs several heuristic rules based on the nature of the problem, and uses a heuristic algorithm to generate the initial solution of the population, so that the algorithm converges faster. In order to further balance the global exploration ability and local development ability of the algorithm, a VND local search algorithm containing four neighborhood structures is designed according to the coding and problem characteristics. In large, medium and small-scale examples, the proposed algorithm can quickly converge to an approximate optimal solution in a relatively short time, and as the scale of the problem increases, the performance is getting better and better than other algorithms.

[0238] 3. The problem model solved by the embodiment of the present invention can become other similar problems by adding or reducing certain specific constraints, such as considering the uncertainty of the machine setting time and the workpiece processing time. Since the basic problem remains unchanged, the designed heuristic algorithm can also be applied to other problems, thereby improving the quality of the initial solution and accelerating the convergence speed.

[0239] 4. The embodiments of the present invention are mainly derived from an in-depth field investigation of a new energy battery manufacturing enterprise and its factory. The problem background is in line with reality, and the factors considered are the aspects that the enterprise can currently optimize and improve. Considering the scheduling problem of multiple equivalent parallel machines with preventive maintenance and workpiece arrival time at the same time, according to the second and third heuristic rules, the final optimal scheduling scheme has a similar completion time for each machine. At the same time, the idle time between two preventive maintenance on the machine is less, which improves the machine utilization rate and thus reduces the number of preventive maintenance. It provides ideas for enterprises to reduce maintenance costs and achieve further lean production, and provides decision-making theory support for enterprise managers, which has a certain display reference significance.

[0240] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0241] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production maintenance and procurement collaborative scheduling method based on an improved dung beetle optimization algorithm, characterized in that: include: Obtain production resources and workpiece processing tasks; Based on the production resources and workpiece processing tasks, considering the preventive maintenance of the machine, and under the known constraint of the expected arrival time of each workpiece procurement, with the goal of minimizing the maximum completion time, a mixed integer programming model is constructed; The mixed integer programming model is solved by the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

2. The production maintenance and procurement collaborative scheduling method according to claim 1, characterized in that: The mixed integer programming model includes: X ijk ∈{0,1},Y ik ∈{0,1} (12) Wherein, formula (1) represents minimizing the maximum completion time; Min represents the minimization function, C max represents the maximum completion time of all machines; i represents the machine index, m represents the number of machines; C i represents the completion time of machine i; Constraint (2) indicates that a workpiece can only be assigned to one position on one machine; j represents the workpiece index, n represents the number of workpieces; X ijk is a decision variable, which is 1 if workpiece j is scheduled to be processed at the kth position of machine i, otherwise it is 0; Constraint (3) indicates that a workpiece can only be assigned to one position on one machine; k represents the position index of the workpiece processed on the machine; Constraint (4) means that no preventive maintenance is performed after the last workpiece of each machine is processed; Y in Indicates whether maintenance is performed after the last workpiece of machine i is processed; Constraint (5) indicates that the start processing time of each position of each machine is greater than or equal to the arrival time of the workpiece processed at that position; ST ik represents the starting processing time of the kth position of machine i; r j represents the expected arrival time of the procurement of workpiece j; Constraint (6) indicates that the start processing time of each position of each machine is greater than or equal to the completion time of the previous position plus the preventive maintenance time (if maintenance is required); CT i(k-1) represents the completion time of the k-1th position of machine i; T represents the fixed preventive maintenance time; Y i(k-1) is a decision variable, which is 1 if preventive maintenance is performed after machining at the k-1th position of machine i, and 0 otherwise; Constraint (7) indicates that the completion time of each position of each machine is greater than or equal to the start time plus the processing time of the workpiece; CT ik represents the completion time of the kth position of machine i; Constraint (8) represents the cumulative processing time of the first position of each machine; Q i1 represents the cumulative processing time of the first position of each machine; Constraint (9) represents the cumulative processing time of each position of each machine; Q i(k-1) represents the cumulative processing time of the k-1th position of machine i; p j represents the processing time of workpiece j; M represents a large positive integer; Constraint (10) indicates that the accumulated processing time of each position of each machine cannot exceed the threshold value UT; Q ik represents the cumulative processing time of the kth position of machine i; UT represents the threshold value of the preset maintenance period; Constraint (11) represents the completion time of each workpiece; C j represents the completion time of workpiece j; Constraints (12) give the variables X ijk , Y ik The domain of definition.

3. The production maintenance and procurement collaborative scheduling method according to claim 2, characterized in that: The method adopts the dung beetle optimization algorithm combined with the VND local search algorithm to solve the mixed integer programming model to obtain the optimal machine allocation sequence and workpiece processing sequence sequence; including: S31, initialize the parameters of the dung beetle optimization algorithm, including the dung beetle population N, the problem latitude D, and the maximum number of iterations T max , the number of neighborhood searches q; Among them, the machine-workpiece double-layer list structure is used to encode the position of the dung beetle individual, which is expressed as x=[π1,π2], where π1 is the machine allocation code and π2 is the workpiece processing order code; S32, using a mixed method of random strategy and heuristic algorithm to initialize the population, and dividing the population into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles; S33, calculating the fitness of the dung beetle individual based on formula (1); S34, respectively updating the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the stealing dung beetle; S35, adopting a greedy strategy to update the dung beetle individuals and their fitness values; S36, based on the preset neighborhood structure, using the VND algorithm to perform local search for the global optimal solution; S37, repeat S33 to S36 until the number of iterations t ≥ T max , output the global optimal solution and its fitness value, and decode the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing order sequence.

4. The production maintenance and procurement collaborative scheduling method according to claim 3 is characterized in that: The population is initialized by a mixture of random strategy and heuristic algorithm, and is divided into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles; specifically, the following are included: S321, sorting the workpieces in ascending order according to the arrival time of the workpieces according to the first heuristic rule; S322, adjusting the order of workpieces with the same arrival time according to the LPT rule according to the second heuristic rule; S323, take the first m workpieces and arrange them in the first position on the m machines respectively; S324, repeat S323, and assign workpieces to the machines in order; S325, when the cumulative processing time after a certain machine is assigned to a workpiece exceeds the threshold value UT, reselect the workpiece from the remaining workpieces according to the third heuristic rule; Among them, the third heuristic rule specifically means that when the workpieces assigned to the machine are determined, the smaller the difference between the continuous processing time of the workpiece batch between two preventive maintenance and the threshold value UT, the better; S326, obtaining a feasible solution, and placing the encoded dung beetle individuals into the initial population; S327, using a random strategy to generate N-1 dung beetle individuals and putting them into the initial population; S328. Divide the obtained initial population into four roles, namely, rolling dung beetles, breeding dung beetles, foraging dung beetles and stealing dung beetles, according to a preset ratio of 6:6:7:

11.

5. The production maintenance and procurement collaborative scheduling method according to claim 3, characterized in that: The respectively updating of the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle and the stealing dung beetle specifically includes: (1) For the ball-rolling dung beetle: Generate a random number δ between 0 and 1. If δ<0.9, perform the location update in barrier-free mode, otherwise perform the location update in barrier-free mode. (a) Accessible mode, the position update formula is defined as follows: x e (t+1)=x e (g)+α×h×x e (t-1)+b×Δx,Δx (13)=|x e (t)-X w | Among them, x e (t) represents the position of the e-th dung beetle in the population at the t-th iteration; the coefficient h∈(0,0.2] is the deflection coefficient constant, b∈(0,1) is a fixed constant, and α is a natural coefficient with a value of -1 or 1; X w represents the worst position in the current population, and Δx represents the change in light intensity, which is used to guide the position update of the dung beetle; (b) In the obstacle mode, the tangent function is used to simulate the dancing behavior to determine the new rolling direction. The position update formula of the dung beetle's dancing behavior is defined as follows: x e (x+1)=x e (t)+tan(θ)×|x e (t)-x e (t-1)| (14) Among them, the deflection angle θ∈[0,π], when or π, the position of the dung beetle will not be updated; (2) For breeding dung beetles Introduce a boundary selection strategy to simulate the oviposition area selection of female dung beetles: Among them, X * Represents the local optimal position in the current population, Lb * and Ub * The lower and upper bounds of the spawning area are defined respectively. Lb and Ub are the lower and upper bounds of the optimization problem respectively; After the egg-laying area is determined, the female dung beetle will lay eggs in this area, and the position update formula is defined as follows: B e (t+1)=X * +b1×(B e (t)-Lb * )+b2×(B e (t)-Ub * ) (16) Among them, B e (t) represents the position of the e-th dung beetle egg at the t-th iteration, b1 and b2 represent two independent random vectors, whose dimensions are the same as the dimension D of the optimization problem; (3) For foraging dung beetles The update formula is as follows: Among them, X b is the global optimal position of the dung beetle population during the iteration process, Lb b and Ub b The lower and upper bounds of the foraging area are defined respectively; The position update formula of the dung beetle is defined as follows: x i (t+1)=X b +C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b ) (18) Among them, C1 represents a random variable that obeys the normal distribution, and C2 represents a random vector of size 1×D belonging to (0,1); (4) For the stealing dung beetle The position update formula is defined as follows: x e (t+1)=X b +S×g×(|x e (t)-X * |+|x e (t)-X b |) (19) Where S represents a fixed constant and g represents a random vector of size 1×D that follows a normal distribution.

6. The production maintenance and procurement collaborative scheduling method according to claim 5, characterized in that: The greedy strategy is specifically expressed as: Among them, x i (t) represents the dung beetle individuals in the current iteration population, x e(new) (t+1) represents the dung beetle individual after position update, and f(x·) represents the fitness function.

7. The production maintenance and procurement collaborative scheduling method according to claim 3, characterized in that: The following four neighborhood structures are designed: First Neighborhood Structure: Randomly swapping two different artifacts on a machine; Second neighborhood structure: select the machine with the largest completion time as the key machine, select the workpiece with the smallest processing time on the machine and assign it to the machine with the smallest maximum completion time; The third neighborhood structure: select the workpiece with longer processing time from the machine with the largest completion time and exchange it with the workpiece with shorter processing time on the machine with the smallest completion time; Fourth neighborhood structure: adopt the left shift strategy for the workpieces after the last preventive maintenance on the machine and insert them into the previous idle waiting time; The method uses the VND algorithm to perform local search for the global optimal solution based on the preset neighborhood structure; specifically includes: S361, determine the initial solution as the optimal dung beetle position X b , let the neighborhood solution be equal to the initial solution; S362, executing the first neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution; S363, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S364; S364, executing the second neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution; S365, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S366; S366, executing the third neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, then updating the original neighborhood solution; S367, if the final neighborhood solution is better than the initial solution, update the initial solution and proceed to S362; otherwise, proceed to S368; S368, executing the fourth neighborhood structure q times, if the generated new neighborhood solution is better than the original neighborhood solution, updating the original neighborhood solution; S369: If the final neighborhood solution is better than the initial solution, the initial solution is updated and the process goes to S362; otherwise, the algorithm ends and the optimal individual and the optimal fitness value are output.

8. A production maintenance and procurement collaborative scheduling system based on an improved dung beetle optimization algorithm, characterized in that: include: Acquisition module, used to acquire production resources and workpiece processing tasks; A construction module is used to consider preventive maintenance of the machine based on the production resources and the workpiece processing tasks, and to construct a mixed integer programming model with the goal of minimizing the maximum completion time under the constraint of the known expected arrival time of each workpiece procurement; The solution module is used to solve the mixed integer programming model by using the dung beetle optimization algorithm combined with the VND local search algorithm to obtain the optimal machine allocation sequence and workpiece processing sequence.

9. A storage medium, characterized in that: It stores a computer program for production maintenance and procurement collaborative scheduling based on an improved dung beetle optimization algorithm, wherein the computer program enables a computer to execute the production maintenance and procurement collaborative scheduling method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include programs for executing the production maintenance and procurement collaborative scheduling method as described in any one of claims 1 to 7.

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