Production and transportation collaborative scheduling method considering order delivery date based on VNS-H algorithm

Abstract

The invention provides a VNS-H algorithm-based production and transportation collaborative scheduling method and system considering an order delivery date, a storage medium and electronic equipment, and relates to the technical field of production and transportation collaborative scheduling. According to the method, firstly, an order set and production and manufacturing resources are acquired; secondly, introducing a loading deterioration rate and a production deterioration rate, and obtaining the total production and transportation time consumption of all orders in each factory by combining the basic processing time of each order and the transportation completion time of the factory allocated by the orders; thirdly, on the basis of the order set and the production and manufacturing resources, a mathematical model is constructed with the goal of minimizing the sum of the total production and transportation time consumption and the delay cost; and finally, solving the mathematical model by adopting a VNS-H algorithm to obtain an optimal production and transportation collaborative scheduling scheme. The modeling process is closer to the actual production by considering the influence of the deterioration effect production and transportation total time consumption; and meanwhile, a VNS-H algorithm solving model is designed, so that a reasonable production and transportation collaborative scheduling decision is made for an enterprise within limited time.

Description

Technical Field

[0001] The present invention relates to the technical field of production and transportation collaborative scheduling, and in particular to a production and transportation collaborative scheduling method, system, storage medium and electronic device based on a VNS-H algorithm and taking order delivery time into consideration. Background Art

[0002] With the rapid development of new generation information technology, batteries, as an indispensable component of modern high-end equipment, are also constantly improving their manufacturing processes. Battery production involves a variety of complex manufacturing processes and technical requirements. From battery cell production to battery pack assembly, each link requires efficient scheduling and resource allocation. In the battery production system, factories usually need to produce a variety of different models of batteries and related parts, and manage multiple production workshops at the same time. Each workshop has unique resources and equipment, and will accept orders with different needs. It is crucial to make effective battery production scheduling decisions and resource allocation strategies, which can not only reduce the production costs of enterprises and improve production efficiency, but also enhance the competitiveness of enterprises in the market.

[0003] In related technologies, existing studies have been conducted on production scheduling problems that take into account deterioration effects, but many scheduling algorithms are based on a specific production situation, such as production and transportation coordination, assembly line transportation with fuzzy processing time, and production scheduling problems that take into account workpiece arrival time. However, there are few studies that simultaneously consider production and transportation coordination and order delivery constraints, and the source of the problem is not closely linked to actual production, which results in the algorithm deviating from the theory in the actual production process and failing to achieve good results. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a production and transportation collaborative scheduling method, system, storage medium and electronic device based on the VNS-H algorithm taking into account the order delivery period, which solves the technical problem of parallel machine continuous batch production scheduling and transportation coordination considering the deterioration effect.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A production and transportation collaborative scheduling method considering order delivery time based on VNS-H algorithm, including:

[0009] Obtain order collection and production resources;

[0010] Introducing loading deterioration rate and production deterioration rate, combined with the basic processing time of each order and the transportation completion time of the factory assigned to the order, the total production and transportation time of all orders in each factory is obtained;

[0011] Based on the order set and manufacturing resources, a mathematical model is constructed with the goal of minimizing the sum of the total production and transportation time and the delay cost;

[0012] The VNS-H algorithm is used to solve the mathematical model to obtain the optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

[0013] Preferably, the introduction of loading deterioration rate and production deterioration rate, combined with the basic processing time of each order and the transportation completion time of the factory assigned to the order, to obtain the total production and transportation time of all orders in each factory; including:

[0014] Assume each factory as a serial batch machine participating in order production, introduce loading deterioration rate and production deterioration rate, and correct the processing start time of any order in the factory

[0015] Combined with the basic processing time of each order, correct the processing completion time of any order in the factory

[0016] Combined with the transportation completion time of the factory where the order is located, the total production and transportation time of all orders in each factory is obtained; expressed as:

[0017]

[0018] Where i represents the factory index;

[0019] r represents the processing location of the order in the factory;

[0020] represents the number of orders for factory i, and n represents the total number of orders, M i represents the i-th factory, and m represents the total number of factories;

[0021] θ represents the loading deterioration rate;

[0022] b represents the production deterioration rate;

[0023] a represents any of the first r-1 processing locations of the order in the factory;

[0024] g represents any one of the processing positions from a to r-1 in the factory where the order is processed;

[0025] represents the order of the rth position of factory i, J [j] represents the jth order;

[0026] represents the processing start time of the order at the rth position of factory i;

[0027] represents the processing completion time of the order at the rth position of factory i;

[0028] μ[r][i] represents the basic processing time of the order at the rth position in factory i;

[0029] μ[a][i] represents the basic processing time of the order at the ath position in factory i;

[0030] C i represents the sum of the delivery times of all orders in factory i;

[0031] is the decision variable, when and are assigned to different batches, then Otherwise, 0;

[0032] t i represents the transportation completion time of factory i.

[0033] Preferably, the mathematical model includes:

[0034] MinF

[0035]

[0036]

[0037]

[0038] C(B [k-1][i] )≤S(B [k][i] ) (5)

[0039]

[0040]

[0041] Among them, formula (2) is the objective function, Min is the minimization function, and F is the total production and transportation time C i Plus the delay cost P i sum;

[0042] Formula (3) represents the delay cost P i is the sum of penalty costs for all orders in factory i; α represents the delay penalty parameter, max is the maximization function, d[r][i] represents the delivery time of the order at the rth location of factory i;

[0043] Constraint (4) indicates that the number of orders in any batch in the factory must not be greater than the batch capacity; k represents the location of the production batch in the factory, represents the number of orders contained in the kth batch of factory i, and c represents the capacity of one batch of the factory;

[0044] Constraint (5) means that each factory can only process one batch of orders at any time; B [k][i] represents the kth batch of factory i, S(B [k][i] ) represents the processing start time of the kth batch in factory i, C(B [k][i] ) represents the processing completion time of the kth batch of factory i;

[0045] Constraint (6) indicates that each factory can only process one order at any time; represents the processing completion time of the order at the r-1th position of factory i;

[0046] Constraint (7) indicates that an order can only be produced at one location in one factory; x [j][i][r] is a decision variable. When order j is assigned to the rth position of factory i, x [j][i][r] =1, otherwise 0.

[0047] Preferably, the method of using the VNS-H algorithm to solve the mathematical model to obtain the optimal production and transportation collaborative scheduling solution includes:

[0048] S41. Initialize the input parameters of the algorithm, including the order quantity, the delivery date of each order, the basic production time of each order, the deterioration rate of production and batch setting time, the penalty rate of delayed production, the transportation time and transportation cost of the third-party logistics solution, and encode the factory allocation of all orders using integer coding; wherein the integer coding method refers to:

[0049] Solution vector 1 is denoted as V s =(v1,v2,W3,...,v N ), v1,v2,v3,...,v N is any integer from 1 to m, v i The value of represents the corresponding factory to which the i-th order is assigned; the solution vector 2 is represented by V 2s =(v1,v2,v3,…,v e ), v1,v2,v3,...,v e Provide different types of third-party logistics transportation solutions from 1 to e;

[0050] S42, based on the heuristic algorithm, the workpiece processing order, batching method and fitness value of each factory are obtained, and the s The corresponding initial solution V1 is obtained 2s The corresponding initial transport mode V2; wherein the fitness function is the objective function of the mathematical model;

[0051] S43, setting the execution parameters of the VNS algorithm, wherein the VNS algorithm part includes the initial solution V s , the maximum number of iterations I max , minimum neighborhood structure index value o min , current neighborhood structure index value o, maximum neighborhood structure index value o max ; The local search algorithm includes the maximum number of iterations it max , the current iteration number it=1; the global optimal solution V best =V s ; Best transportation method V 2best =V 2s ;

[0052] S44, set o=o min ;

[0053] S45, select a random neighborhood structure to jitter V1 to obtain V1′;

[0054] S46, execute the oth neighborhood structure of the current V1′ to obtain V1″, and execute the first neighborhood structure of V2 to obtain V2′;

[0055] S47, based on the heuristic algorithm, update the production sequence, and obtain the fitness value of V1″ according to the objective function solution formula; if it is better than the current global optimal solution V best , then assign V1″ to V best , assign V2′ to V 2best , and go to S44; otherwise, assign o+1 to o, and determine whether o is not greater than o max , if established, go to S45, otherwise go to S48;

[0056] S48, assign I+1 to I, and judge I≤I max Is it true? If so, execute S44. Otherwise, end the algorithm and decode the global optimal solution V best and the best mode of transportV 2best , obtain the optimal production and transportation collaborative scheduling solution.

[0057] Preferably, the heuristic algorithm includes heuristic algorithm 1 and heuristic algorithm 2 for adjusting the order processing sequence;

[0058] The heuristic algorithm 1 refers to:

[0059] S101. For factory M i on the order set Execute the preset dynamic programming algorithm to obtain the batch set and calculate the production completion time of each order and the total fitness value after each batch is generated; Let the variable where represents the number of batches of factory i;

[0060] S102. If z > 1, go to S103; otherwise, go to S107;

[0061] S103. If Set r1 = 1, and go to S104; otherwise, set z = z - 1, and go to S102;

[0062] S104. If it satisfies and then insert into the back of batch B [z][i] in and go to S101; otherwise, go to S105;

[0063] S105. If r2 < s, set r2 = r2 + 1, and go to S104; otherwise, let and go to S106;

[0064] S106. If r1 < r2, set r1 = r1 + 1, and go to S104; otherwise, set z = z - 1, and go to S102;

[0065] S107. Output the grouping result of factory M i ;

[0066] The heuristic algorithm 2 refers to:

[0067] S201. For factory M i on the order set and the obtained batch set Calculate the production completion time of each order and the total fitness value after each batch is generated; Let z = 1,

[0068] S202. Arrange the orders on batch B [z][i] in non - decreasing order of the order - based processing time to form an order production sequence, and go to S203;

[0069] S203. If z < z0, set z = z + 1 and go to S202; otherwise, execute S204;

[0070] S204. Output the workpiece processing sequence of factory M i .

[0071] Preferably, the dynamic programming algorithm includes:

[0072] S100. Let F(r) be the minimum fitness value of the scheduling scheme for the first r orders in the factory; set F(0) to 0; let r = 1;

[0073] S200. If then go to S600; otherwise, set l = max{r - c, 0}, where l represents the position of the last order in the previous batch, and go to S300;

[0074] S300. According to obtain Fi(r l ); where the current fitness value Fi(r l ) is equal to the fitness value Fi(l) when the previous batch is completed plus the fitness value generated by the current batch

[0075] S400. Assign l + 1 to l, and judge whether l ≤ r - 1 holds. If it holds, execute S300; otherwise, go to S500;

[0076] S500. Assign the minimum fitness value in Fi(r l ) to Fi(r); assign r + 1 to r, and go to S200;

[0077] S600. Output the batch grouping result of the factory and end the algorithm.

[0078] Preferably, design the following five neighborhood structures:

[0079] (1) Neighborhood structure 1; it means changing a random element in V2 to an element that has not been used within the element range;

[0080] (2) Neighborhood structure 2; it means selecting an order with the largest delay time from the factory with the largest total fitness value and transferring it to any factory;

[0081] (3) Neighborhood structure 3; it means randomly selecting an order from the factory with the largest fitness value and transferring it to any factory;

[0082] (4) Neighborhood structure 4; it means randomly swapping two V1 points and selecting a random order and reassigning it to any factory;

[0083] (5) Neighborhood structure 5: randomly selecting a part of V1 and reversing it.

[0084] A production and transportation collaborative scheduling system based on VNS-H algorithm considering order delivery time, including:

[0085] The acquisition module is used to obtain order sets and production and manufacturing resources;

[0086] The correction module is used to introduce the loading deterioration rate and the production deterioration rate, and combine the basic processing time of each order and the transportation completion time of the factory assigned to the order to obtain the total production and transportation time of all orders in each factory;

[0087] A construction module, for constructing a mathematical model based on the order set and the manufacturing resources with the goal of minimizing the sum of the total production and transportation time and the delay cost;

[0088] A solution module is used to solve the mathematical model using a VNS-H algorithm to obtain an optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

[0089] A storage medium stores a computer program for production and transportation collaborative scheduling based on a VNS-H algorithm taking into account order delivery dates, wherein the computer program enables a computer to execute the production and transportation collaborative scheduling method as described above.

[0090] An electronic device, comprising:

[0091] 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 methods for executing the production and transportation collaborative scheduling method as described above.

[0092] (III) Beneficial effects

[0093] The present invention provides a production and transportation collaborative scheduling method, system, storage medium and electronic device based on the VNS-H algorithm considering order delivery period. Compared with the prior art, it has the following beneficial effects:

[0094] In the present invention, firstly, the order set and manufacturing resources are obtained; secondly, the loading deterioration rate and the production deterioration rate are introduced, and the total production and transportation time of all orders in each factory is obtained by combining the basic processing time of each order and the transportation completion time of the factory assigned to the order; again, based on the order set and manufacturing resources, a mathematical model is constructed with the goal of minimizing the sum of the total production and transportation time plus the delay cost; finally, the VNS-H algorithm is used to solve the mathematical model to obtain the optimal production and transportation collaborative scheduling solution. Considering the influence of the deterioration effect on the total production and transportation time, the modeling process is closer to actual production; at the same time, the VNS-H algorithm is designed to solve the model, so as to make reasonable production and transportation collaborative scheduling decisions for enterprises within a limited time. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] 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.

[0096] Figure 1 A block diagram of a production and transportation collaborative scheduling method considering order delivery date based on a VNS-H algorithm provided in an embodiment of the present invention;

[0097] Figure 2 A flowchart of a VNS-H algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. 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.

[0099] The embodiments of the present application solve the technical problem of parallel machine continuous batch production scheduling and transportation coordination considering the deterioration effect by providing a production and transportation coordination scheduling method, system, storage medium and electronic device based on the VNS-H algorithm considering the order delivery period.

[0100] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0101] The embodiment of the present invention is based on the actual manufacturing background of a battery manufacturing enterprise, and models the battery PACK production and transportation coordination problem as a parallel machine continuous batch production scheduling and transportation coordination problem considering the deterioration effect. First of all, the actual situation of order production and delivery in reality is considered. In order to minimize the sum of the total production and transportation time and the delay cost, the orders with different delivery dates are assigned to factories located in different locations, and the batch order of production orders of each factory is determined by the dynamic programming method; then a heuristic algorithm is designed to optimize the existing job processing sequence. On the basis of the heuristic algorithm, combined with the variable neighborhood search algorithm, a VNS-H algorithm is formed to find better results for order selection of production plants and factory selection of transportation methods. The present invention makes reasonable scheduling decisions for the main enterprise within a limited time, improves the overall production capacity of the manufacturing system and reduces production costs.

[0102] 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.

[0103] Embodiment 1:

[0104] like Figure 1 As shown, the embodiment of the present invention provides a production and transportation collaborative scheduling method considering order delivery time based on the VNS-H algorithm, including:

[0105] S1. Obtain order collection and production resources;

[0106] S2. Introducing the loading deterioration rate and the production deterioration rate, combined with the basic processing time of each order and the transportation completion time of the factory assigned to the order, the total production and transportation time of all orders in each factory is obtained;

[0107] S3, based on the order set and manufacturing resources, construct a mathematical model with the goal of minimizing the sum of the total production and transportation time and the delay cost;

[0108] S4. Use the VNS-H algorithm to solve the mathematical model and obtain the optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

[0109] The embodiment of the present invention takes into account the influence of the deterioration effect on the total time consumption of production and transportation, and the modeling process is closer to actual production. At the same time, the VNS-H algorithm is designed to solve the model, so as to make reasonable production and transportation collaborative scheduling decisions for enterprises within a limited time.

[0110] Next, we will introduce the steps of the above scheme in detail:

[0111] In step S1, an order set and production resources are obtained.

[0112] The production and transportation coordination problem considering order delivery time studied in the embodiment of the present invention is described as follows: For example, for battery manufacturing, there are n independent orders J = {J [1] ,J [2] ,…,J [n]} and n identical parallel machines m = {M1,M2,…,M m All the workpieces and machines of the manufacturer are available at time 0. In the manufacturing phase, n orders are first assigned to the serial batch machines of the manufacturer, and then grouped into batches. The orders in the same batch are processed serially one by one, and the work needs to be loaded before each batch starts. The batch completion time is defined as the completion time of the last order in the batch.

[0113] It should be noted that the serial batch machine in the embodiment of the present invention is actually a factory. The assumption that a factory is a machine for production is only for the convenience of describing the problem. The term factory will still be used uniformly in the following text.

[0114] This step obtains the order set and manufacturing resources. The variables and descriptions used for subsequent model construction are shown in Table 1:

[0115] Table 1

[0116]

[0117]

[0118] In step S2, the loading deterioration rate and the production deterioration rate are introduced, and the basic processing time of each order and the transportation completion time of the factory assigned to the order are combined to obtain the total production and transportation time of all orders in each factory.

[0119] The embodiment of the present invention takes into account that all machines have a production deterioration rate b due to the deterioration effect when processing orders, so that the actual processing time of the order is added with a deterioration time that is linearly related to the total processing time of the machine on the basis of the basic processing time of the order. At the same time, the loading work before the start of the batch also has a loading deterioration rate θ, so that the actual loading time of the batch is linearly related to the total processing time of the machine.

[0120] On this basis, this step corrects the total production and transportation time of all orders in each factory as follows:

[0121] S21. Assume each factory as a serial batch machine participating in order production, introduce loading deterioration rate and production deterioration rate, and correct the processing start time of any order in the factory.

[0122] S22. Combine the basic processing time of each order and correct the processing completion time of any order in the factory

[0123] S23. Combined with the transportation completion time of the factory where the order is located, the total production and transportation time of all orders in each factory is obtained; expressed as:

[0124]

[0125] Among them, a represents any one of the first r-1 processing locations of the order in the factory; g represents any one of the ath to r-1th processing locations of the order in the factory.

[0126] In step S3, based on the order set and manufacturing resources, a mathematical model is constructed with the goal of minimizing the sum of the total production and transportation time and the delay cost.

[0127] It should be noted that in order to solve the problem of parallel machine continuous batch production scheduling and transportation coordination considering the deterioration effect, the mathematical model proposed in the embodiment of the present invention is based on the following assumptions:

[0128] (1) The arrival time of all orders is 0;

[0129] (2) The basic processing time and delivery date of the production order are known in advance;

[0130] (3) The processing time for the same order assigned to different factories may not be the same;

[0131] (4) The transportation time of the same transportation plan applied to different factories may not be the same;

[0132] (5) The penalty cost parameters for all factory production orders are the same;

[0133] (6) The capacity of a batch of machines in all factories is the same;

[0134] (7) A factory is assumed to be a machine that produces;

[0135] (8) Each factory can only process one order at any time and the processing should be continuous and cannot be interrupted by other orders;

[0136] (9) Orders in the same batch are shipped together and have the same delivery time.

[0137] In addition, it should be noted that, in addition to the deterioration effect, the embodiment of the present invention also specifically considers the delivery stage after the production of a batch of orders is completed. Each order has its own delivery date. If the production exceeds the delivery date, a penalty cost will be submitted according to the delay time. The orders are shipped to the customers by the third-party logistics in batches. Different third-party logistics services have different service fees and transportation times.

[0138] Therefore, the mathematical model constructed in this step includes:

[0139] MinF

[0140]

[0141]

[0142]

[0143] C(B [k-1][i] )≤S(B [k][i] ) (5)

[0144]

[0145]

[0146] Among them, formula (2) is the objective function, Min is the minimization function, and F is the total production and transportation time C i Plus the delay cost P i sum;

[0147] Formula (3) represents the delay cost P i is the sum of penalty costs for all orders in factory i; α represents the delay penalty parameter, max is the maximization function, d [r][i] represents the delivery time of the order at the rth location of factory i;

[0148] Constraint (4) indicates that the number of orders in any batch in the factory must not be greater than the batch capacity; k represents the location of the production batch in the factory, represents the number of orders contained in the kth batch of factory i, and c represents the capacity of one batch of the factory;

[0149] Constraint (5) means that each factory can only process one batch of orders at any time; B [k][i] represents the kth batch of factory i, S(B [k][i] ) represents the processing start time of the kth batch in factory i, C(B [k][i] ) represents the processing completion time of the kth batch of factory i;

[0150] Constraint (6) indicates that each factory can only process one order at any time; represents the processing completion time of the order at the r-1th position of factory i;

[0151] Constraint (7) indicates that an order can only be produced at one location in one factory; x [j][i][r] is a decision variable. When order j is assigned to the rth position of factory i, x [j][i][r] =1, otherwise 0.

[0152] In step S4, the VNS-H algorithm is used to solve the mathematical model to obtain the optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

[0153] In related technologies, in the scheduling decision-making process for resource constraints such as delivery time constraints, more reliance is placed on intelligent algorithms to directly find approximate optimal solutions. This method has good effects when dealing with some simple or small-scale problems, but it is often inefficient and ineffective when facing large-scale complex problems. The heuristic algorithm proposed in combination with actual problems can make up for this shortcoming.

[0154] In addition, even though traditional intelligent algorithms such as genetic algorithms have advantages such as strong global search capabilities and fast convergence speed when facing NP-hard problems, they have the defect of easily falling into local optimality when dealing with large-scale scheduling problem optimization.

[0155] Therefore, the embodiment of the present invention combines the heuristic algorithm with the variable neighborhood algorithm and designs a VNS-H algorithm, which can effectively improve the convergence of the algorithm and avoid the algorithm from falling into the local optimum as much as possible.

[0156] like Figure 2 As shown, Figure 2 The complete process of the VNS-H algorithm provided by the embodiment of the present invention is given. Figure 2 This step is described in detail, including:

[0157] S41. Initialize the input parameters of the algorithm, including the order quantity, the delivery date of each order, the basic production time of each order, the deterioration rate of production and batch setting time, the penalty rate of delayed production, the transportation time and transportation cost of the third-party logistics solution, and encode the factory allocation of all orders using integer coding; wherein the integer coding method refers to:

[0158] Solution vector 1 is denoted as V s =(v1,v2,v3,...,v N ), v1,v2,v3,...,v N is any integer from 1 to m, v iThe value of represents the corresponding factory to which the i-th order is assigned; the solution vector 2 is represented by V 2s =(v1,v2,v3,…,v e ), v1,v2,v3,...,v e Different types of third-party logistics transportation solutions from 1 to e.

[0159] S42, based on the heuristic algorithm, the workpiece processing order, batching method and fitness value of each factory are obtained, and the s The corresponding initial solution V1 is obtained 2s The corresponding initial transport mode V2; wherein the fitness function is the objective function of the mathematical model.

[0160] Exemplarily, the heuristic algorithm includes heuristic algorithm 1 and heuristic algorithm 2 for adjusting the order processing sequence.

[0161] First, the heuristic algorithm 1 refers to:

[0162] S101, for factory M i Order collection on Execute the preset dynamic programming algorithm to obtain the batch set And calculate the production completion time of each order and the total fitness value after each batch is generated; let the variable in represents the batch number of factory i.

[0163] In an optional implementation, the dynamic programming algorithm comprises:

[0164] S100, let F(r) be the minimum fitness value of the scheduling plan for the first r orders in the factory; set F(0) to 0; let r = 1;

[0165] S200, if Then go to S600; otherwise, set l = max{rc,0}, where l represents the position of the last order in the previous batch, and go to S300;

[0166] S300, according to Find Fi(r l ); the current fitness value Fi(r l ) is equal to the fitness value Fi(l) when the previous batch was completed plus the fitness value generated by the current batch To find the fitness value of the scheduling scheme when the last order is r and the last order in the second-to-last batch is l;

[0167] S400. Assign l + 1 to l, and determine whether l ≤ r - 1 holds. If it holds, execute S300; otherwise, go to S500;

[0168] S500. Assign the smallest fitness value in Fi(r l ) to Fi(r); assign r + 1 to r, and go to S200;

[0169] S600. Output the grouping result of the factory and end the algorithm.

[0170] S102. If z > 1, go to S103; otherwise, go to S107.

[0171] S103. If Set r1 = 1, and go to S104; otherwise, set z = z - 1, and go to S102.

[0172] S104. If the conditions and are met, then insert behind batch B [z][i] in , and go to S101; otherwise, go to S105.

[0173] S105. If r2 < s, set r2 = r2 + 1, and go to S104; otherwise, let and go to S106.

[0174] S106. If r1 < r2, set r1 = r1 + 1, and go to S104; otherwise, set z = z - 1, and go to S102.

[0175] S107. Output the grouping result of factory M i ;

[0176] Second, the heuristic algorithm 2 refers to:

[0177] S201. For the order set i on factory M and the obtained batch set calculate the production completion time of each order and the total fitness value after each batch is generated; let z = 1,

[0178] S202. Arrange the orders on batch B [z][i] in non - decreasing order of the order - based processing time to form an order production sequence, and go to S203.

[0179] S203. If z < z0, set z = z + 1 and go to S202; otherwise, execute S204;

[0180] S204. Output the workpiece processing sequence of factory M i of.

[0181] S43. Set the execution parameters of the VNS algorithm, where the VNS algorithm part includes the initial solution V s , the maximum number of iterations I max , the minimum neighborhood structure index value o min , the current neighborhood structure index value o, the maximum neighborhood structure index value o max ; the local search algorithm part includes the maximum number of iterations it max , the current number of iterations it = 1; the global optimal solution V best = V s ; the optimal transportation mode V 2best = V 2s .

[0182] S44. Set o = o min .

[0183] S45. Select a random neighborhood structure perturbation V1 to obtain V1'.

[0184] S46. Execute the o-th neighborhood structure of the current V1' to obtain V1'', and execute the first neighborhood structure of V2 to obtain V2'.

[0185] Exemplarily, five neighborhood structures are designed as follows:

[0186] (1) Neighborhood structure 1; it means changing a random element in V2 to an element not used within the element range;

[0187] (2) Neighborhood structure 2; it means selecting an order with the largest delay time from the factory with the largest total fitness value and transferring it to any factory;

[0188] (3) Neighborhood structure 3; it means randomly selecting an order from the factory with the largest fitness and transferring it to any factory;

[0189] (4) Neighborhood structure 4; it means randomly swapping two V1 points and selecting a random order and reassigning it to any factory;

[0190] (5) Neighborhood structure 5; it means randomly selecting a part of V1 and reversing it.

[0191] S47. Update the production sequence based on the heuristic algorithm, and obtain the fitness value of V1'' according to the objective function solution formula; if it is better than the current global optimal solution V best , then assign V1'' to Vbest , assign V2′ to V 2best , and go to S44; otherwise, assign o+1 to o, and determine whether o is not greater than o max If so, go to S45, otherwise go to S48.

[0192] S48, assign I+1 to I, and judge I≤I max Is it true? If so, execute S44. Otherwise, end the algorithm and decode the global optimal solution V best and the best mode of transportV 2best , obtain the optimal production and transportation collaborative scheduling solution.

[0193] So far, the embodiment of the present invention has completed the entire process of the production and transportation collaborative scheduling method considering the order delivery date based on the VNS-H algorithm.

[0194] Embodiment 2:

[0195] The embodiment of the present invention provides a production and transportation collaborative scheduling system based on the VNS-H algorithm and taking into account order delivery time, including:

[0196] The acquisition module is used to obtain order sets and production and manufacturing resources;

[0197] The correction module is used to introduce the loading deterioration rate and the production deterioration rate, and combine the basic processing time of each order and the transportation completion time of the factory assigned to the order to obtain the total production and transportation time of all orders in each factory;

[0198] A construction module, for constructing a mathematical model based on the order set and the manufacturing resources with the goal of minimizing the sum of the total production and transportation time and the delay cost;

[0199] A solution module is used to solve the mathematical model using a VNS-H algorithm to obtain an optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

[0200] Embodiment 3:

[0201] An embodiment of the present invention provides a storage medium storing a computer program for production and transportation collaborative scheduling taking into account order delivery time based on a VNS-H algorithm, wherein the computer program enables a computer to execute the production and transportation collaborative scheduling method as described in Example 1.

[0202] Embodiment 4:

[0203] An embodiment of the present invention provides an electronic device, including:

[0204] 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 and transportation collaborative scheduling method as described in Example 1.

[0205] It can be understood that the production and transportation collaborative scheduling system, storage medium and electronic device based on the VNS-H algorithm considering the order delivery time provided in the embodiment of the present invention correspond to the production and transportation collaborative scheduling method based on the VNS-H algorithm considering the order delivery time provided in the embodiment of the present invention. The explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the production and transportation collaborative scheduling method, and will not be repeated here.

[0206] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0207] 1. The embodiment of the present invention takes into account the influence of the total time consumption of production and transportation due to the deterioration effect, and the modeling process is closer to actual production. At the same time, the VNS-H algorithm is designed to solve the model, so as to make reasonable production and transportation collaborative scheduling decisions for enterprises within a limited time.

[0208] 2. The embodiment of the present invention designs an optimization algorithm of a variable neighborhood search algorithm and a heuristic algorithm for scheduling the production delivery period and resource allocation of orders. First, the allocation of workpieces to be processed is determined by coding, and then the processing order, batching method and transportation plan of each workshop workpiece are determined according to the heuristic algorithm. Finally, the approximate optimal solution is found through the iteration of the variable neighborhood search algorithm. The variable neighborhood search algorithm is adopted to avoid the defect that the intelligent algorithm is prone to fall into the local optimum, and the convergence of the algorithm is accelerated by the heuristic algorithm.

[0209] 3. The embodiment of the present invention is derived from the actual situation of the battery pack production process of a large-scale new energy power lithium battery manufacturing enterprise. Taking into account the deterioration of the production process, the delivery period of battery pack orders, and the fact that orders can be produced in different factories, it provides a feasible solution for improving the production efficiency of the enterprise and improving customer satisfaction, and has certain practical application value.

[0210] 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.

[0211] 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, a person 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 and transportation collaborative scheduling method based on the VNS-H algorithm considering order delivery time, characterized in that: Including: Obtain an order set and production and manufacturing resources; Introduce a loading deterioration rate and a production deterioration rate, and combine the basic processing time of each order and the transportation completion time of the factory assigned to the order to obtain the total production and transportation time of all orders in each factory; Based on the order set and production and manufacturing resources, construct a mathematical model with the goal of minimizing the sum of the total production and transportation time and the late cost; Use the VNS-H algorithm to solve the mathematical model to obtain an optimal production and transportation collaborative scheduling plan; where the VNS-H algorithm refers to combining a variable neighborhood search algorithm on the basis of a heuristic algorithm.

2. The production and transportation coordinated scheduling method according to claim 1, characterized in that: The introducing of the loading deterioration rate and the production deterioration rate, and combining the basic processing time of each order and the transportation completion time of the factory assigned to the order to obtain the total production and transportation time of all orders in each factory; includes: Assume each factory as a serial batch machine participating in order production, introduce loading deterioration rate and production deterioration rate, and correct the processing start time of any order in the factory Combined with the basic processing time of each order, correct the processing completion time of any order in the factory Combine the transportation completion time of the factory where the order is located to obtain the total production and transportation time of all orders in each factory; expressed as: Where, i represents the factory index; r represents the processing position of the order in the factory; represents the number of orders for factory i, and n represents the total number of orders, M i represents the i-th factory, and m represents the total number of factories; θ represents the loading deterioration rate; b represents the production deterioration rate; a represents any one of the first r - 1 processing positions of the order in the factory; g represents any one of the orders from the a-th to the r - 1-th processing positions in the factory; represents the order of the rth position of factory i, J [j] represents the jth order; represents the processing start time of the order at the rth position of factory i; represents the processing completion time of the order at the rth position of factory i; μ[r][i] represents the basic processing time of the order at the r-th position in factory i; μ[a][i] represents the basic processing time of the order at the a-th position in factory i; C i represents the sum of the delivery times of all orders in factory i; is the decision variable, when and are assigned to different batches, then Otherwise, 0; t i represents the transportation completion time of factory i.

3. The production and transportation coordinated scheduling method according to claim 2, characterized in that: The mathematical model includes: Among them, formula (2) is the objective function, Min is the minimization function, and F is the total production and transportation time C i Plus the delay cost P i sum; Formula (3) represents the delay cost P i is the sum of penalty costs for all orders in factory i; α represents the delay penalty parameter, max is the maximization function, d [r][i] represents the delivery time of the order at the rth location of factory i; Constraint (4) indicates that the number of orders in any batch in the factory must not be greater than the batch capacity; k represents the location of the production batch in the factory, represents the number of orders contained in the kth batch of factory i, and c represents the capacity of one batch of the factory; Constraint (5) means that each factory can only process one batch of orders at any time; B [k][i] represents the kth batch of factory i, S(B [k][i] ) represents the processing start time of the kth batch in factory i, C(B [k][i] ) represents the processing completion time of the kth batch of factory i; Constraint (6) indicates that each factory can only process one order at any time; represents the processing completion time of the order at the r-1th position of factory i; Constraint (7) indicates that an order can only be produced at one location in one factory; x [j][i][r] is a decision variable. When order j is assigned to the rth position of factory i, x [j][i][r] =1, otherwise 0.

4. The production and transportation coordinated scheduling method according to claim 3, characterized in that: The using of the VNS-H algorithm to solve the mathematical model to obtain an optimal production and transportation collaborative scheduling plan; Including: S41. Initialize the input parameters of the algorithm, including the number of orders, the due date of each order, the basic production time of each order, the deterioration rate of production and batch setting time, the penalty rate for late production, the transportation time and transportation cost of the third-party logistics plan, and use integer coding to encode the factory allocation of all orders; where the integer coding method refers to: Solution vector 1 is denoted as V s =(v1,v2,v3,...,v N ), v1,v2,v3,...,v N is any integer from 1 to m, v i The value of represents the corresponding factory to which the i-th order is assigned; the solution vector 2 is represented by V 2s =(v1,v2,v3,…,v e ), v1,v2,v3,...,v e Provide different types of third-party logistics transportation solutions from 1 to e; S42, based on the heuristic algorithm, the workpiece processing order, batching method and fitness value of each factory are obtained, and the s The corresponding initial solution V1 is obtained 2s The corresponding initial transport mode V2; wherein the fitness function is the objective function of the mathematical model; S43, setting the execution parameters of the VNS algorithm, wherein the VNS algorithm part includes the initial solution V s , the maximum number of iterations I max , minimum neighborhood structure index value o min , current neighborhood structure index value o, maximum neighborhood structure index value o max ; The local search algorithm includes the maximum number of iterations it max , the current number of iterations it=1; the global optimal solution V best =V s ; Best transportation method V 2best =V 2s ; S44, set o=o min ; S45. Select a random neighborhood structure to jitter V1 to get V1'; S46. Execute the o-th neighborhood structure of the current V1' to get V1'', and execute the first neighborhood structure of V2 to get V2'; S47, based on the heuristic algorithm, update the production sequence, and obtain the fitness value of V1″ according to the objective function solution formula; if it is better than the current global optimal solution V best , then assign V1″ to V best , assign V2′ to V 2best , and go to S44; otherwise, assign o+1 to o, and determine whether o is not greater than o max , if established, go to S45, otherwise go to S48; S48, assign I+1 to I, and judge I≤I max Is it true? If so, execute S44. Otherwise, end the algorithm and decode the global optimal solution V best and the best mode of transportV 2best , obtain the optimal production and transportation collaborative scheduling solution.

5. The production and transportation coordinated scheduling method according to claim 4, characterized in that: The heuristic algorithm includes heuristic algorithm 1 and heuristic algorithm 2 for adjusting the order processing sequence; The heuristic algorithm 1 refers to: S101, for factory M i Order collection on Execute the preset dynamic programming algorithm to obtain the batch set And calculate the production completion time of each order and the total fitness value after each batch is generated; let the variable in represents the batch number of factory i; S102. If z > 1, then go to S103, otherwise, go to S107; S103, if Set r1 = 1, And go to S104, otherwise, set and go to S102; S104, if satisfied and Then Insert into batch B [z][i] middle After that, go to S101, otherwise, go to S105; S105. If r2 < s, set r2 = r2 + 1 and go to S104; otherwise, go to S106; S106. If r1 < r2, then set r1 = r1 + 1 and go to S104; otherwise, set z = z - 1, and go to S102; S107, output factory M i The batch results; The heuristic algorithm 2 refers to: S201, for factory M i Order collection on And the resulting batch set Calculate the production completion time of each order and the total fitness value after each batch is generated; let z = 1, S202, batch B [z][i] The orders on the system are arranged in non-decreasing order of order basic processing time to form an order production sequence, and then go to S203; S203. If z < z0, then set z = z + 1 and go to S202; otherwise, execute S204; S204, output factory N i The processing order of workpieces.

6. The production and transportation coordinated scheduling method according to claim 5, characterized in that: The dynamic programming algorithm; includes: S100. Let F(r) be the minimum fitness value of the scheduling plan for the first r orders in the factory; set F(0) to 0; let r = 1; S200, if Then go to S600; otherwise, set l = max{rc,0}, where l represents the position of the last order in the previous batch, and go to S300; S300, according to Find Fi(r l );where the current fitness value Fi(r l ) is equal to the fitness value Fi(l) when the previous batch was completed plus the fitness value generated by the current batch S400. Assign l + 1 to l, and judge whether l ≤ r - 1 holds. If it holds, then execute S300; otherwise, go to S500; S500, Fi(r l ) is assigned the smallest fitness value to Fi(r); r+1 is assigned to r, and go to S200; S600. Output the batch result of the factory and end the algorithm.

7. The production and transportation coordinated scheduling method according to claim 4, characterized in that: Design the following five neighborhood structures: (1) Neighborhood structure 1; refers to changing a random element in V2 to an element that has not been used within the element range; (2) Neighborhood structure 2: It means selecting an order with the largest delay time from the factory with the largest total fitness value and transferring it to any factory; (3) Neighborhood structure 3: It means randomly selecting an order from the factory with the largest fitness and transferring it to any factory; (4) Neighborhood structure 4; refers to randomly exchanging two V1 points, selecting a random order and redistributing it to any factory; (5) Neighborhood structure 5: randomly selecting a part of V1 and reversing it.

8. A production and transportation collaborative scheduling system based on the VNS-H algorithm taking into account order delivery time, characterized in that: include: The acquisition module is used to obtain order sets and production and manufacturing resources; The correction module is used to introduce the loading deterioration rate and the production deterioration rate, and combine the basic processing time of each order and the transportation completion time of the factory assigned to the order to obtain the total production and transportation time of all orders in each factory; A construction module, for constructing a mathematical model based on the order set and the manufacturing resources with the goal of minimizing the sum of the total production and transportation time and the delay cost; A solution module is used to solve the mathematical model using a VNS-H algorithm to obtain an optimal production and transportation collaborative scheduling solution; wherein the VNS-H algorithm refers to a variable neighborhood search algorithm combined with a heuristic algorithm.

9. A storage medium, characterized in that: It stores a computer program for production and transportation collaborative scheduling based on the VNS-H algorithm taking into account order delivery time, wherein the computer program enables the computer to execute the production and transportation 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 and transportation collaborative scheduling method as described in any one of claims 1 to 7.

Images