Production maintenance and procurement collaborative scheduling method based on improved dung beetle optimization algorithm

Abstract

This invention provides a method, system, storage medium, and electronic device for collaborative scheduling of production, maintenance, and procurement based on an improved dung beetle optimization algorithm, relating to the field of collaborative scheduling of production, maintenance, and procurement. Targeting the production characteristics of new energy batteries, this invention, after acquiring production resources and workpiece processing tasks, simultaneously considers the parallel machine scheduling problem of preventative maintenance and the expected arrival time of order material procurement. It performs collaborative optimization of production, maintenance, and procurement, constructing a mixed-integer programming model with the objective of minimizing the maximum completion time. Furthermore, a dung beetle optimization algorithm combined with the VND local search algorithm is designed, significantly enhancing the algorithm's convergence speed and search capability. This effectively helps new energy battery manufacturers reduce the number of preventative maintenance operations, thereby lowering maintenance costs, while achieving more efficient production scheduling and improving enterprise competitiveness.

Description

Technical Field

[0001] This invention relates to the field of collaborative scheduling of production, maintenance and procurement, and specifically to a collaborative scheduling method, system, storage medium and electronic device for production, maintenance and procurement based on an improved dung beetle optimization algorithm. Background Technology

[0002] Against the backdrop of new energy battery manufacturing, and with the urgent global demand for sustainable development and clean energy, the production and R&D of new energy batteries have become a key driving force for the development of emerging industries such as new energy vehicles and smart grids.

[0003] For new energy battery manufacturers, parallel machine production is a common production method in workshops. Parallel machine scheduling is a crucial workshop scheduling problem, primarily focusing on the sequencing and scheduling of multiple workpieces processed on multiple parallel machines. Furthermore, unexpected machine downtime can cause production interruptions, leading to poor efficiency. Therefore, it is essential to integrate preventative maintenance with parallel machine scheduling. Here, preventative maintenance refers to ensuring that the continuous processing time of workpieces or the age of the machine does not exceed a preset maintenance period threshold.

[0004] However, the relevant technologies 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 periodic maintenance, or only studied the scheduling problem of parallel machines considering the expected arrival time of workpiece procurement. Neither of them can directly provide decision support for new energy battery manufacturing companies. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a collaborative scheduling method, system, storage medium, and electronic device for production maintenance and procurement based on an improved dung beetle optimization algorithm. This solves the technical problem of parallel machine scheduling integration for 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 provides the following technical solution:

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

[0010] Acquire production resources and workpiece processing tasks;

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

[0012] 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.

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

[0014]

[0015]

[0016] X ijk ∈{0,1},Y ik ∈{0,1} (12)

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

[0018] Constraint (2) means that a workpiece can only be assigned to one position on one machine; j represents the workpiece index, and n represents the number of workpieces; X ijk The variable is 1 if workpiece j is processed at the k-th position of machine i, and 0 otherwise.

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

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

[0021] Constraint (5) means that the start time of processing at each position on each machine is greater than or equal to the arrival time of the workpiece at that position; ST ik This represents the start time of processing at the k-th position of machine i; r j This indicates the expected arrival time of the procurement of workpiece j;

[0022] Constraint (6) means that the start time of each position on 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)Y represents the completion time of the (k-1)th position of machine i; T represents the fixed preventive maintenance time; i(k-1) The variable is denoted as 1 if preventive maintenance is performed after processing at the (k-1)th position of machine i, and 0 otherwise.

[0023] Constraint (7) states that the completion time at each position on each machine is greater than or equal to the start time plus the workpiece machining time; CT ik This represents the completion time of the k-th position of machine i;

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

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

[0026] Constraint (10) means that the cumulative processing time at each position of each machine cannot exceed the threshold value UT; Q ik UT represents the cumulative processing time at the k-th position of machine i; UT represents the preset threshold value for the maintenance period.

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

[0028] Constraint (12) gives the variable X respectively ijk Y ik The domain of definition.

[0029] Preferably, the step of using the dung beetle optimization algorithm combined with the VND local search algorithm to solve the mixed integer programming model and obtain the optimal machine allocation sequence and workpiece processing sequence includes:

[0030] S31. Initialize the parameters of the dung beetle optimization algorithm, including the dung beetle population size N, the problem dimension D, and the maximum number of iterations T. max , the number of neighborhood search times q;

[0031] The machine-workpiece two-level list structure is used to encode the position of the dung beetle, represented as x = [π1, π2], where π1 is the machine assignment code and π2 is the workpiece processing sequence code.

[0032] S32. The population is initialized using a combination of random strategy and heuristic algorithm, and divided into four roles according to a preset ratio: rolling dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle.

[0033] S33. Calculate the fitness of individual dung beetles based on equation (1);

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

[0035] S35. Update dung beetle individuals and their fitness values ​​using a greedy strategy;

[0036] S36. Based on the preset neighborhood structure, the VND algorithm is used to perform a local search for the global optimal solution;

[0037] S37. Repeat S33 to S36 until the number of iterations t ≥ T. max The system outputs the global optimal solution and its fitness value, and decodes the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0038] Preferably, the population is initialized using a hybrid approach of random strategy and heuristic algorithm, and divided into four roles according to a preset ratio: rolling dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle; specifically including:

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

[0040] S322. Adjust the order of workpieces with the same arrival time according to the LPT rule based on the second heuristic rule;

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

[0042] S324. Repeat S323 to assign workpieces to the machine in sequence.

[0043] S325. When the cumulative processing time of a machine after being assigned a workpiece exceeds the threshold value UT, a new workpiece is selected from the remaining workpieces according to the third heuristic rule.

[0044] The third heuristic rule specifically refers to the principle that when the number of workpieces assigned by the machine is fixed, the smaller the difference between the continuous processing time of the workpiece batch and the threshold value UT between two preventive maintenance operations, the better.

[0045] S326. A feasible solution is obtained, and the encoded dung beetle individuals are placed into the initial population;

[0046] S327. Generate N-1 dung beetle individuals using a random strategy and place them into the initial population;

[0047] S328. Divide the acquired initial population into four roles according to a preset ratio of 6:6:7:11: dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle.

[0048] Preferably, updating the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the thieving dung beetle respectively includes:

[0049] (1) For the dung beetle:

[0050] Generate a random number δ between 0 and 1. If δ < 0.9, update the location in accessible mode; otherwise, update the location in accessible mode.

[0051] (a) In accessibility mode, the location update formula is defined as follows:

[0052]

[0053] Where, 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 This indicates the worst position in the current population, while △x represents the change in light intensity, used to guide the dung beetle's position update;

[0054] (b) Obstacle-prone mode: Tangent function is used to simulate dance behavior to determine the new rolling ball direction. The position update formula for dung beetle 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 When π is used, the dung beetle's position will not be updated;

[0056] (2) For breeding dung beetles

[0057] A boundary selection strategy is introduced to simulate the oviposition area selection of female dung beetles:

[0058]

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

[0060] Once the oviposition area is determined, the female dung beetle will lay her eggs within this area. The location update formula is defined as follows:

[0061] B e (t+1)=X * +b1×(B e (t)-Lb * )+b2×(B e (t)-Ub * (16)

[0062] Among them, B e (t) represents the position of the e-th dung beetle egg in the t-th iteration, and b1 and b2 represent two independent random vectors with the same dimension as the dimension D of the optimization problem;

[0063] (3) For dung beetles

[0064] The updated formula is as follows:

[0065]

[0066] Among them, X b It is the globally optimal position of the dung beetle population during the iterative process, Lb b and Ub b The lower and upper boundaries of the foraging area were defined respectively;

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

[0068] x i (t+1)=X b +C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b (18)

[0069] Where C1 represents a random variable that follows a normal distribution, and C2 represents a random vector of size 1×D belonging to (0, 1);

[0070] (4) Regarding dung beetles

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

[0072] x e (t+1)=X b +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] Where, x i (t) represents the number of 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: two different workpieces on a random exchange machine;

[0079] Second neighborhood structure: Select the machine with the longest completion time as the critical machine, and select the workpiece with the shortest processing time on that machine to be assigned to the machine with the shortest longest completion time.

[0080] Third Neighborhood Structure: Select a workpiece with a longer processing time from the machine with the longest completion time and exchange it with a workpiece with a shorter processing time from the machine with the shortest completion time.

[0081] Fourth Neighborhood Structure: For workpieces after the last preventative maintenance on the machine, a left-shift strategy is adopted to insert them into the preceding idle waiting time;

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

[0083] S361. Determine the initial solution as the optimal position X of the dung beetle. b Let the neighborhood solution equal the initial solution;

[0084] S362. Execute the first neighborhood structure q times. If the generated new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0086] S364. Execute the second neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0088] S366. Execute the third neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0090] S368. Execute the fourth neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

[0091] S369. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, the algorithm ends and outputs the best individual and the best fitness value.

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

[0093] The acquisition module is used to acquire production resources and workpiece processing tasks;

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

[0095] The solution module is used to solve the mixed integer programming model using a 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 storing a computer program for collaborative scheduling of production, maintenance, and procurement based on an improved dung beetle optimization algorithm, wherein the computer program causes a computer to execute the collaborative scheduling method for production, maintenance, and procurement 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 configured to be executed by the one or more processors, the programs including methods for performing production maintenance and procurement collaborative scheduling as described above.

[0099] (III) Beneficial Effects

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

[0101] Beneficial effects:

[0102] This invention addresses the production characteristics of new energy batteries. After acquiring production resources and workpiece processing tasks, it simultaneously considers the parallel machine scheduling problem of preventative maintenance and the expected arrival time of order material procurement. It collaboratively optimizes production, maintenance, and procurement, constructing a mixed-integer programming model with the objective of minimizing the maximum completion time. Furthermore, a dung beetle optimization algorithm combining the VND local search algorithm is designed, significantly enhancing the algorithm's convergence speed and search capability. This effectively helps new energy battery manufacturers reduce the number of preventative maintenance operations, thereby lowering maintenance costs, while achieving more efficient production scheduling and improving enterprise competitiveness. Attached Figure Description

[0103] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

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

[0106] Figure 3 A flowchart of a dung beetle optimization algorithm combining the VND local search algorithm is provided for an embodiment of the present invention;

[0107] Figure 4 An example Gantt chart of an optimal machine allocation sequence and workpiece processing sequence provided for embodiments of the present invention. Detailed Implementation

[0108] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0109] This application 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 for preventive maintenance and expected arrival time of workpiece procurement for new energy battery manufacturing enterprises.

[0110] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0111] In this embodiment of the invention, in order to solve the problem of scheduling multiple parallel machines considering preventive maintenance and workpiece arrival time, the machines need to perform PM elastic maintenance (i.e., preventive maintenance) to ensure that they are in normal condition, 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] This scheduling problem can be represented as Pm|r j ,nr,FPM|C max According to the α|β|γ three-stage representation, α represents the equipment environment in the production system, α = Pm indicates that the production system consists of parallel machines of the same type and speed; γ represents the constraints and operational characteristics of the production system, β = {r j ,nr,FPM} represent the workpiece arrival time, uninterrupted workpiece operation, and flexible maintenance; γ represents the optimization objective of the scheduling problem, γ=C max The optimization objective of the scheduling problem is to minimize the maximum completion time.

[0113] Furthermore, based on Pm|r j ,nr,FPM|C max Based on the characteristics and optimization properties of the algorithm, a heuristic algorithm incorporating multiple heuristic rules is designed to generate initial solutions, thereby improving the quality of the initial solutions. Then, an improved DBO algorithm is used for iterative optimization. This algorithm incorporates a VND local search algorithm with multiple neighborhood structures into the standard DBO algorithm, significantly enhancing the convergence speed and search capability. This effectively helps factories reduce the number of preventative maintenance operations, thereby lowering maintenance costs, while achieving more efficient production scheduling and improving enterprise competitiveness.

[0114] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0115] Example 1:

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

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

[0118] S2. Based on the production resources and workpiece processing tasks, considering preventive maintenance of the machine, and under the constraint of the known expected arrival time of each workpiece procurement, construct a mixed integer programming model with the objective of minimizing the maximum completion time.

[0119] S3. The dung beetle optimization algorithm, which combines the VND local search algorithm, is used to solve the mixed integer programming model to obtain the optimal machine allocation sequence and workpiece processing sequence.

[0120] This invention addresses the production characteristics of new energy batteries. After acquiring production resources and workpiece processing tasks, it simultaneously considers the parallel machine scheduling problem of preventative maintenance and the expected arrival time of order material procurement. It performs collaborative optimization of production, maintenance, and procurement, constructing a mixed-integer programming model with the objective of minimizing the maximum completion time. Furthermore, a dung beetle optimization algorithm combining the VND local search algorithm is designed, significantly enhancing the algorithm's convergence speed and search capability. This effectively helps new energy battery manufacturers reduce the number of preventative maintenance operations, thereby lowering maintenance costs, while achieving more efficient production scheduling and improving enterprise competitiveness.

[0121] First, it should be noted that the embodiments of this invention address the parallel machine scheduling problem of preventative maintenance and the expected arrival time of ordered material procurement, which is an NP-hard problem. An example Gantt diagram of this problem model is shown below. Figure 2 As shown, the relevant problem description is as follows:

[0122] Suppose there is a set of uninterruptible 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 for all workpieces is S. The start time, cumulative processing time, and completion time of the k-th position of machine i are denoted by ST. ik Q ik and CT ik This indicates that the machine requires PM (Performance Maintenance) to ensure it remains in normal working order. This means that the continuous processing time of the workpiece or the age of the machine must not exceed the maintenance period threshold UT, and p j ≤UT. The time required for maintenance operations is fixed at T. Workpieces cannot be preempted or interrupted during processing. The set of all workpieces between two consecutive PMs is called a batch, and the sum of their processing times must be less than or equal to UT.

[0123] The following will detail each step of the above solution:

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

[0125] In this step, we obtain production resource and workpiece processing task data for mathematical modeling in subsequent steps, and define the model variables and their descriptions as shown in Table 1:

[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 constraint of the known expected arrival time of each workpiece procurement.

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

[0131] (1) The 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 processing time for the same workpiece is the same regardless of which machine it is assigned to;

[0135] (5) Each machine can only process one workpiece at any given time and should maintain continuous processing without being interrupted by other workpieces.

[0136] (6) There is no distinction between important and unimportant workpieces, i.e., weight is not considered.

[0137] Based on this, this step constructs the following mixed-integer programming model:

[0138]

[0139]

[0140] X ijk ∈{0,1},Y ik ∈{0,1} (12)

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

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

[0143] Constraint (3) means that a workpiece can only be assigned to one position on one machine; k represents the position index of the workpiece 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) means that the start time of processing at each position of each machine is greater than or equal to the arrival time of the workpiece at that position.

[0146] Constraint (6) means that the start 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) means that the completion time of each position on each machine is greater than or equal to the start time plus the workpiece processing time.

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

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

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

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

[0152] Constraint (12) gives the variable X respectively ijk Y ik The domain of definition.

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

[0154] This step first designs a heuristic algorithm incorporating multiple heuristic rules to generate initial solutions, thereby improving the quality of the initial solutions. Then, iterative optimization is performed by combining an improved DBO algorithm. The standard DBO algorithm is integrated with a VND local search algorithm that incorporates multiple neighborhood structures, greatly enhancing the algorithm's convergence speed and search capability.

[0155] It should be noted that the Dung Beetle Optimizer (DBO) is a novel swarm intelligence optimization algorithm inspired by the dung beetle's behaviors in nature, such as rolling balls, dancing, reproducing, foraging, and stealing. The optimization variable is the dung beetle's position in the solution space. By modeling its behavior, the position is changed, and the algorithm iteratively seeks the best solution in a multi-dimensional space to find the globally 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 size N, the problem dimension D, and the maximum number of iterations T. max , the number of neighborhood searches q.

[0158] The position of each dung beetle is encoded using a machine-workpiece two-level list structure, represented as x = [π1, π2], where π1 is the machine assignment code and π2 is the workpiece processing sequence code.

[0159] Understandably, the corresponding decoding rules refer to the following: by decoding the individual positions of dung beetles into a workpiece processing sequence and a machine allocation sequence according to certain rules, a scheduling Gantt chart can be output.

[0160] For example, π1 = [1,2,2,3,1] indicates 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. π2 = [5,2,4,1,3] indicates that the processing order of the workpieces is to process workpiece 5 first, and then process workpieces 2, 4, 1, and 3 in sequence.

[0161] S32. The population is initialized using a combination of random strategies and heuristic algorithms, and divided into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles, and thieving dung beetles; specifically including:

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

[0163] It's easy to understand that the first heuristic rule specifically means that the first workpiece to arrive should be produced first, which can reduce unnecessary waiting time and thus shorten the maximum completion time.

[0164] S322. Adjust the order of workpieces with the same arrival time according to the LPT rule based on the second heuristic rule.

[0165] It is not difficult to understand that the second heuristic rule specifically refers to the fact that the closer the sum of the processing times of the workpieces allocated on each machine is, the smaller the maximum completion time will be. Therefore, a method of allocating workpieces based on the average processing time of the workpieces was designed.

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

[0167] S324. Repeat S323 to assign workpieces to the machine in sequence.

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

[0169] The third heuristic rule specifically refers to the principle that when the number of workpieces assigned by the machine is fixed, the smaller the difference between the continuous processing time of the workpiece batch and the threshold value UT between two preventive maintenance operations, the better.

[0170] S326. A feasible solution is obtained, and the encoded dung beetle individuals are placed 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 acquired initial population into four roles according to a preset ratio of 6:6:7:11: dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle.

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

[0174] S34. Update the locations of the rolling dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle respectively; specifically including:

[0175] (1) For the dung beetle:

[0176] Generate a random number δ between 0 and 1. If δ < 0.9, update the location in accessible mode; otherwise, update the location in accessible mode.

[0177] (a) In the unobstructed mode, dung beetles use the intensity of sunlight for guidance, rolling their dung balls along a straight path. The position update formula is defined as follows:

[0178]

[0179] Where, 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 (its value can be obtained by pre-setting a probability value and randomly generating a random number between 0 and 1; if the generated random number is less than the probability value, then take 1, otherwise take -1); X w This indicates the worst position in the current population, while △x represents the change in light intensity, used to guide the dung beetle's position update.

[0180] (b) Obstacle Mode: When a dung beetle encounters an obstacle while rolling its dung ball and cannot continue, it needs to perform a dance-like movement to adjust its direction of movement. A tangent function is used to simulate the dance behavior to determine the new direction of the rolling ball. The position update formula for 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] Where the deflection angle θ∈[0,π], when When π is used, the dung beetle's position will not be updated;

[0183] (2) For breeding dung beetles

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

[0185]

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

[0187] After determining the oviposition area, the female dung beetle will lay its eggs within this area. As can be seen from equation (15), the dynamic change of the boundary of the oviposition area is determined by the value of R. Therefore, the position of the dung beetle eggs is also dynamic during the iteration process. Thus, the position update formula is defined as follows:

[0188] B e (t+1)=X * +b1×(B e (t)-Lb * )+b2×(B e (t)-Ub * (16)

[0189] Among them, B e (t) represents the position of the e-th dung beetle egg in the t-th iteration, and b1 and b2 represent two independent random vectors with the same dimension as the dimension D of the optimization problem;

[0190] (3) For dung beetles

[0191] This behavior primarily targets the hatched dung beetles. Once mature, the dung beetles emerge from the soil to search for food, and their optimal foraging area boundaries are dynamically updated using the following formula:

[0192]

[0193] Among them, X b It is the globally optimal position of the dung beetle population during the iterative process, Lb b and Ub b The lower and upper boundaries of the foraging area were defined respectively;

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

[0195] x i (t+1)=X b +C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b (18)

[0196] Here, C1 represents a random variable that follows a normal distribution, and C2 represents a random vector of size 1×D belonging to (0, 1). It should be noted that, similar to breeding dung beetles, the location updates of foraging dung beetles also require corresponding boundary adjustments based on the foraging area.

[0197] (4) Regarding dung beetles

[0198] Within a population, some dung beetles exhibit the behavior of stealing dung balls from other individuals. The position update formula for these 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 the individual dung beetles and their fitness values.

[0202] The algorithm uses a greedy rule to compare the updated position with the original position, selecting the individual with better fitness to proceed to the next iteration. This greedy rule can be expressed as:

[0203]

[0204] Where, x i (t) represents the number of 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, the VND algorithm is used to perform a local search for the global optimal solution.

[0206] To further improve the local search capability of the algorithm, this embodiment of the invention designs a local search algorithm that includes the following four neighborhood structures. These four different neighborhood structures are used simultaneously in the local search process, which can prevent the algorithm from getting stuck in local optima, thereby improving the quality of the global search.

[0207] First neighborhood structure: two different workpieces on a random exchange machine;

[0208] Second neighborhood structure: Select the machine with the longest completion time as the critical machine, and select the workpiece with the shortest processing time on that machine to be assigned to the machine with the shortest longest completion time.

[0209] Third Neighborhood Structure: Select a workpiece with a longer processing time from the machine with the longest completion time and exchange it with a workpiece with a shorter processing time from the machine with the shortest completion time.

[0210] Fourth Neighborhood Structure: For workpieces after the last preventative maintenance on the machine, a left-shift strategy is adopted to insert them into the preceding idle waiting time;

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

[0212] S361. Determine the initial solution as the optimal position X of the dung beetle. b Let the neighborhood solution equal the initial solution;

[0213] S362. Execute the first neighborhood structure q times. If the generated new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0215] S364. Execute the second neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0217] S366. Execute the third neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

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

[0219] S368. Execute the fourth neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution.

[0220] S369. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, the algorithm ends and outputs the best individual and the best fitness value.

[0221] S37. Repeat S33 to S36 until the number of iterations t ≥ T. max The system outputs the global optimal solution and its fitness value, and decodes the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing sequence.

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

[0223] Thus, this embodiment of the invention completes the entire process of the collaborative scheduling method for production maintenance and procurement based on the improved dung beetle optimization algorithm.

[0224] Example 2:

[0225] This invention provides a collaborative scheduling system for production, maintenance, and procurement based on an improved dung beetle optimization algorithm, comprising:

[0226] The acquisition module is used to acquire production resources and workpiece processing tasks;

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

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

[0229] Example 3:

[0230] This 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 causes a computer to execute the collaborative scheduling method for production, maintenance, and procurement as described in Embodiment 1.

[0231] Example 4:

[0232] This invention provides an electronic device, comprising:

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

[0234] It is understood that the production maintenance and procurement collaborative scheduling system, storage medium and electronic equipment based on the improved dung beetle optimization algorithm 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 explanation, examples and beneficial effects of the relevant contents can be referred to the corresponding parts of the production maintenance and procurement collaborative scheduling method, and will not be repeated here.

[0235] In summary, compared with existing technologies, it has the following beneficial effects:

[0236] 1. This invention addresses the production characteristics of new energy batteries. After acquiring production resources and workpiece processing tasks, it simultaneously considers the parallel machine scheduling problem of preventative maintenance and the expected arrival time of order material procurement. It performs collaborative optimization of production, maintenance, and procurement, constructing a mixed-integer programming model with the objective of minimizing the maximum completion time. Furthermore, a dung beetle optimization algorithm combining the VND local search algorithm is designed, significantly enhancing the algorithm's convergence speed and search capability. This effectively helps new energy battery manufacturers reduce the number of preventative maintenance operations, thereby lowering maintenance costs, while achieving more efficient production scheduling and improving enterprise competitiveness.

[0237] 2. This invention aims to minimize the maximum completion time and studies the scheduling problem of multiple equivalent parallel machines that simultaneously consider preventive maintenance and workpiece arrival time. Based on the problem's characteristics, several heuristic rules are designed, and a heuristic algorithm is used to generate an initial solution for the population, enabling faster convergence. To further balance the algorithm's global exploration capability and local exploitation capability, a VND local search algorithm with four neighborhood structures is designed based on the encoding and problem characteristics. In large, medium, and small-scale examples, the proposed algorithm can quickly converge to a near-optimal solution in a short time, and its performance improves relative to other algorithms as the problem size increases.

[0238] 3. The problem model solved by the embodiments of the present invention can be transformed into other similar problems by adding or removing certain specific constraints. For example, considering the uncertainty of machine setup time and 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 this invention are mainly based on in-depth on-site research of a new energy battery manufacturing enterprise and its factory. The problem background is consistent with reality, and the factors considered are aspects that the enterprise can currently optimize and improve. Simultaneously considering the scheduling problem of multiple equivalent parallel machines for preventive maintenance and workpiece arrival time, according to the second and third heuristic rules, the optimal scheduling scheme results in similar completion times for each machine, while minimizing idle time between two preventive maintenance operations, thus improving machine utilization and reducing the number of preventive maintenance operations. This provides ideas for enterprises to reduce maintenance costs and achieve further lean production, offering decision-making theoretical support for enterprise managers and possessing certain demonstrative reference value.

[0240] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0241] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative scheduling method for production maintenance and procurement based on an improved dung beetle optimization algorithm, characterized in that, include: Acquire production resources and workpiece processing tasks; Based on the production resources and workpiece processing tasks, considering preventive maintenance of the machines, and under the constraint of the known expected arrival time of each workpiece procurement, with the objective of minimizing the maximum completion time, a mixed integer programming model is constructed. 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. The mixed integer programming model includes: Wherein, equation (1) represents minimizing the maximum completion time; Min represents minimizing the function, C max This represents the maximum completion time across all machines; i represents the machine index, m represents the number of machines; C i Indicates the completion time of machine i; Constraint (2) means that a workpiece can only be assigned to one position on one machine; j represents the workpiece index, and n represents the number of workpieces; X ijk The variable is 1 if workpiece j is processed at the k-th position of machine i, and 0 otherwise. Constraint (3) means that a workpiece can only be assigned to one position on one machine; k represents the position index of the workpiece on the machine. Constraint (4) indicates that no preventative maintenance is performed after the last workpiece of each machine is processed; Y in This indicates whether maintenance is performed after the last workpiece of machine i is processed; Constraint (5) means that the start time of processing at each position on each machine is greater than or equal to the arrival time of the workpiece at that position; ST ik This represents the start time of processing at the k-th position of machine i; r j This indicates the expected arrival time of the procurement of workpiece j; Constraint (6) means that the start time of each position on 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) Y represents the completion time of the (k-1)th position of machine i; T represents the fixed preventive maintenance time; i(k-1) The variable is denoted as 1 if preventive maintenance is performed after processing at the (k-1)th position of machine i, and 0 otherwise. Constraint (7) states that the completion time at each position on each machine is greater than or equal to the start time plus the workpiece machining time; CT ik This represents the completion time of the k-th position of machine i; Constraint (8) represents the cumulative processing time for the first position of each machine; Q i1 This represents the cumulative processing time for the first position of each machine; Constraint (9) represents the cumulative processing time at each position of each machine; Q i(k-1) p represents the cumulative processing time at the (k-1)th position of machine i; j This represents the processing time for workpiece j; M represents a very large positive integer. Constraint (10) means that the cumulative processing time at each position of each machine cannot exceed the threshold value UT; Q ik UT represents the cumulative processing time at the k-th position of machine i; UT represents the preset threshold value for the maintenance period. Constraint (11) represents the completion time of each workpiece; C j Indicates the completion time of workpiece j; Constraint (12) gives the variable X respectively ijk Y ik The domain of definition.

2. The production, maintenance, and procurement collaborative scheduling method as described in claim 1, characterized in that, The method employs a dung beetle optimization algorithm combined with the VND local search algorithm to solve the mixed integer programming model, obtaining the optimal machine allocation sequence and workpiece processing sequence; including: S31. Initialize the parameters of the dung beetle optimization algorithm, including the dung beetle population size N, the problem dimension D, and the maximum number of iterations T. max , the number of neighborhood search times q; The machine-workpiece two-level list structure is used to encode the position of the dung beetle, represented as x=[π1,π2], where π1 is the machine assignment code and π2 is the workpiece processing sequence code; S32. The population is initialized using a combination of random strategy and heuristic algorithm, and divided into four roles according to a preset ratio: rolling dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle. S33. Calculate the fitness of individual dung beetles based on equation (1); S34. Update the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the stealing dung beetle respectively; S35. Update dung beetle individuals and their fitness values ​​using a greedy strategy; S36. Based on the preset neighborhood structure, the VND algorithm is used to perform a local search for the global optimal solution; S37. Repeat S33 to S36 until the number of iterations t ≥ T. max The system outputs the global optimal solution and its fitness value, and decodes the global optimal solution to obtain the optimal machine allocation sequence and workpiece processing sequence.

3. The production, maintenance, and procurement collaborative scheduling method as described in claim 2, characterized in that, The population is initialized using a hybrid approach combining random strategies and heuristic algorithms, and divided into four roles according to a preset ratio: rolling dung beetles, breeding dung beetles, foraging dung beetles, and thieving dung beetles; specifically including: S321. Sort the workpieces in ascending order of arrival time according to the first heuristic rule; S322. Adjust the order of workpieces with the same arrival time according to the LPT rule based on the second heuristic rule; S323. Take the first m workpieces and arrange them in the first position on m machines respectively; S324. Repeat S323 to assign workpieces to the machine in sequence. S325. When the cumulative processing time of a machine after being assigned a workpiece exceeds the threshold value UT, a new workpiece is selected from the remaining workpieces according to the third heuristic rule. The third heuristic rule specifically refers to the principle that when the number of workpieces assigned by the machine is fixed, the smaller the difference between the continuous processing time of the workpiece batch and the threshold value UT between two preventive maintenance operations, the better. S326. A feasible solution is obtained, and the encoded dung beetle individuals are placed into the initial population; S327. Generate N-1 dung beetle individuals using a random strategy and place them into the initial population; S328. Divide the acquired initial population into four roles according to the preset ratio of 6:6:7:11: dung beetle, breeding dung beetle, foraging dung beetle, and thieving dung beetle.

4. The production, maintenance, and procurement collaborative scheduling method as described in claim 2, characterized in that, The process of updating the positions of the rolling dung beetle, the breeding dung beetle, the foraging dung beetle, and the stealing dung beetle, respectively, specifically includes: (1) For the dung beetle: Generate a random number δ between 0 and 1. If δ < 0.9, update the location in accessible mode; otherwise, update the location in accessible mode. (a) In accessibility mode, the location 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 | Where, 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 This indicates the worst position in the current population, while Δx represents the change in light intensity, used to guide the dung beetle's position update; (b) Obstacle-prone mode: Tangent function is used to simulate dance behavior to determine the new rolling ball direction. The position update formula for dung beetle dancing behavior is defined as follows: 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 θ=0, When π is used, the dung beetle's position will not be updated; (2) For breeding dung beetles A boundary selection strategy is introduced to simulate the oviposition area selection of female dung beetles: Among them, X * LB represents the local optimum position in the current population. * and Ub * The lower and upper boundaries of the spawning area were defined respectively. Lb and Ub are the lower and upper bounds of the optimization problem, respectively; Once the oviposition area is determined, the female dung beetle will lay her eggs within this area. The location 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 in the t-th iteration, and b1 and b2 represent two independent random vectors with the same dimension as the dimension D of the optimization problem; (3) For dung beetles The updated formula is as follows: Among them, X b It is the globally optimal position of the dung beetle population during the iterative process, Lb b and Ub b The lower and upper boundaries of the foraging area were defined respectively; The formula for updating the position 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) Where C1 represents a random variable that follows a normal distribution, and C2 represents a random vector of size 1×D belonging to (0,1); (4) Regarding dung beetles 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.

5. The production, maintenance, and procurement collaborative scheduling method as described in claim 4, characterized in that, The greedy strategy is specifically expressed as follows: Where, x i (t) represents the number of 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.

6. The production, maintenance, and procurement collaborative scheduling method as described in claim 2, characterized in that, Design the following four neighborhood structures: First neighborhood structure: two different workpieces on a random exchange machine; Second neighborhood structure: Select the machine with the longest completion time as the critical machine, and select the workpiece with the shortest processing time on that machine to be assigned to the machine with the shortest longest completion time. Third Neighborhood Structure: Select a workpiece with a longer processing time from the machine with the longest completion time and exchange it with a workpiece with a shorter processing time from the machine with the shortest completion time. Fourth Neighborhood Structure: For workpieces after the last preventative maintenance on the machine, a left-shift strategy is adopted to insert them into the preceding idle waiting time; The method, based on a preset neighborhood structure, employs the VND algorithm to perform a local search for the global optimal solution; specifically, it includes: S361. Determine the initial solution as the optimal position X of the dung beetle. b Let the neighborhood solution equal the initial solution; S362. Execute the first neighborhood structure q times. If the generated new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution. S363. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, go to S364. S364. Execute the second neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution. S365. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, go to S366. S366. Execute the third neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution. S367. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, go to S368. S368. Execute the fourth neighborhood structure q times. If the new neighborhood solution is better than the original neighborhood solution, then update the original neighborhood solution. S369. If the final neighborhood solution is better than the initial solution, update the initial solution and go to S362; otherwise, the algorithm ends and outputs the best individual and the best fitness value.

7. A collaborative scheduling system for production, maintenance, and procurement based on an improved dung beetle optimization algorithm, characterized in that, The method for implementing the production, maintenance, and procurement collaborative scheduling method as described in claim 1 includes: The acquisition module is used to acquire production resources and workpiece processing tasks; The construction module is used to construct a mixed integer programming model based on the production resources and workpiece processing tasks, considering preventive maintenance of the machine, and under the constraint of the known expected arrival time of each workpiece procurement, with the objective of minimizing the maximum completion time. The solution module is used to solve the mixed integer programming model using a dung beetle optimization algorithm combined with the VND local search algorithm, to obtain the optimal machine allocation sequence and workpiece processing sequence.

8. A storage medium, characterized in that, It stores a computer program for collaborative scheduling of production, maintenance and procurement based on an improved dung beetle optimization algorithm, wherein the computer program causes the computer to execute the collaborative scheduling method for production, maintenance and procurement as described in any one of claims 1 to 6.

9. 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 configured to be executed by the one or more processors, the programs including methods for performing the production maintenance and procurement collaborative scheduling method as described in any one of claims 1 to 6.

Images