Customized production and boxing collaborative scheduling method and system, storage medium and product

Abstract

The invention provides a customized production and boxing cooperative scheduling method and system, a storage medium and a product. The method comprises the steps that S1, an information matrix and the delivery time of workpieces of all delivery addresses are acquired; s2, generating strategy parameters and a plurality of groups of cooperative scheduling schemes according to the information matrix and the delivery time of the workpieces of the delivery addresses by using a problem specific knowledge method; s3, based on a plurality of target values obtained by inputting each alternative scheduling scheme into the production and boxing cooperative scheduling model, performing non-dominated sorting on each cooperative scheduling scheme group, and establishing an Archive population for hierarchical storage; and S4, strategy parameters are updated, a collaborative scheduling representative scheme is selected in the hierarchically stored Archive population through a roulette method, other alternative collaborative scheduling schemes are adjusted according to fitness values corresponding to the collaborative scheduling representative scheme, and high production efficiency and high boxing efficiency of customized orders can be considered.

Description

Technical Field

[0001] The present application relates to the field of intelligent logistics technology, and in particular to a method, system, storage medium and product for coordinated scheduling of customized production and packaging. Background Art

[0002] In today's fiercely competitive market, customized production has become a key strategy for many manufacturers to meet individual customer needs and enhance their market competitiveness. In the apparel industry, tailoring clothing to customer needs has become the industry's development trend. As customers' demands for faster response times and more flexible delivery for personalized orders continue to increase, traditional, isolated optimization operating models are no longer able to meet the efficiency demands of the entire supply chain. There is an urgent need for efficient, collaborative production across the entire supply chain.

[0003] The customized production process includes multiple links of production, quality inspection, warehousing and material packaging. The core links that affect the efficiency of the entire process are production and logistics packaging. The planning and scheduling of the production link determines the procurement, input and output of raw materials, and logistics packaging affects the coordinated scheduling of multiple different products. The manufacturing system needs to achieve full-link optimization from order processing to product delivery by integrating the collaborative decision-making of production scheduling and material packaging.

[0004] Currently, independent scheduling methods for production and product packaging result in a low level of production coordination. In the production scheduling field, methods such as genetic algorithms and mixed integer programming are used to optimize equipment load and order scheduling based on production process requirements. For example, Rui Siyu and Han Wenmin, in "Research on Integrated Scheduling of AGVs and Production Considering Order Batching" (Operation and Management, 2025), comprehensively considered order batching and workpiece transportation time factors to establish an integrated scheduling model for AGVs and production. However, this model only optimizes the production scheduling phase. Meanwhile, other methods for material packaging only consider objectives such as packaging efficiency and box volume ratio.

[0005] The optimization strategies for production and product packaging are independent of each other. Although good optimization results have been achieved in each link, the lack of system coordination caused by isolated optimization has seriously restricted overall efficiency. For example, in the scenario of installment delivery, the existing model has difficulty coordinating the timing relationship between production rhythm and packaging batches. This often leads to delayed shipment of completed orders due to waiting for consolidation, or forced early production to meet the shipping batch size, resulting in increased inventory costs. In addition, the need to quickly ship to meet the needs of some high-quality customers often disrupts production plans and causes production chaos. This conflicting goal between links not only wastes resources but also directly affects the company's order fulfillment capabilities and customer satisfaction.

[0006] In view of this, the existing optimization methods cannot meet the market demand of modern manufacturing enterprises to take into account both high production efficiency and high packaging efficiency for customized orders. There is an urgent need for a collaborative scheduling method that can comprehensively consider multiple factors to improve the current situation and maintain customer satisfaction. Summary of the Invention

[0007] The present application provides a method, system, storage medium and product for coordinated scheduling of customized production and packing, which can take into account both high production efficiency and high packing efficiency of customized orders.

[0008] A first aspect of the present application provides a method for coordinated scheduling of customized production and packaging, the method comprising: Step S1: Obtain an information matrix and the delivery date of the workpieces of each delivery address. The information matrix includes an order demand matrix, a box capacity matrix, a processing time matrix, and a workpiece occupied capacity matrix; Step S2: Using the problem-specific knowledge method, generate strategy parameters and multiple sets of collaborative scheduling solutions based on the information matrix and the delivery dates of the workpieces at each delivery address, where each set of collaborative scheduling solutions includes multiple alternative scheduling solutions for the same number of boxes; Step S3: Based on the multiple target values obtained by inputting each alternative scheduling plan into the production and packaging collaborative scheduling model, each group of collaborative scheduling plans is non-dominated sorted to establish a hierarchically stored Archive population; Step S4: Update the strategy parameters, and select the collaborative scheduling representative scheme from the hierarchically stored Archive population through the roulette wheel method, and adjust other alternative collaborative scheduling schemes according to the fitness value corresponding to the collaborative scheduling representative scheme; Step S5: adjusting each candidate coordinated scheduling solution based on a neighborhood search method; Step S6: Determine whether the adjusted alternative coordinated scheduling scheme meets the preset constraint conditions. If not, execute step S5; if yes, execute step S7. Step S7: performing non-dominated sorting on each group of collaborative scheduling solutions based on the fitness value of each alternative collaborative scheduling solution to update the hierarchically stored Archive population; Step S8: Determine whether the number of Archive populations reaches a preset upper limit. If yes, remove the alternative coordination scheduling schemes whose congestion is greater than the preset congestion. Otherwise, execute step S9. Step S9: Determine whether the maximum number of iterations has been reached. Otherwise, re-execute step S4. If so, output a collaborative scheduling solution group. Based on multiple target values of the current iteration, determine a target collaborative scheduling solution from multiple collaborative scheduling solution groups.

[0009] In some embodiments of the first aspect, the production and packaging collaborative scheduling model is constructed in combination with multi-objective optimization theory with the optimization objectives of maximizing the average packaging volume utilization, minimizing the maximum completion time of multiple order information, and minimizing the lead / delay cost.

[0010] In some embodiments of the first aspect, the multiple target values include average container volume utilization, maximum completion time, and lead / delay cost. Step S3: inputting the multiple target values obtained by the production and container collaborative scheduling model based on each alternative scheduling solution includes: Determine the total number of addresses for each delivery address in the alternative scheduling plan, the total capacity of the boxes corresponding to each delivery address, and the actual capacity occupied by the workpieces in the boxes; Determine the packing volume utilization rate for each delivery address based on the total capacity of the box and the actual capacity occupied by the workpieces in the box; Obtain the average packing volume utilization rate based on the packing volume utilization rate of each delivery address and the total number of addresses; and Determine the completion time of the last workpiece in each delivery address in the alternative scheduling plan; Accumulate the latest completion times to get the maximum completion time; and According to the delivery date of the workpieces required by each delivery address in the alternative scheduling plan and the completion time of the last workpiece in each delivery address, the early completion time and the delayed completion time of each delivery address are obtained; The early completion time and late completion time, as well as the preset early completion unit cost and late completion unit cost, are used to obtain the early completion / latest cost.

[0011] In some embodiments of the first aspect, step S2: using a problem-specific knowledge method to generate multiple sets of collaborative scheduling solutions based on the information matrix and the delivery dates of workpieces at each delivery address includes: According to the information matrix and the delivery date of the workpieces at each delivery address, the greedy and random strategies are combined to divide the types of boxes corresponding to each type of box and the number of different workpieces at each delivery address into the corresponding box type codes and packing codes in the four coding layers. Combining the random strategy and the maximum processing time optimization strategy, the corresponding processes of different workpieces in various boxes and the corresponding production machines of each process are divided into corresponding process codes and workstation codes in four coding layers. Decode box type codes, packing codes, process codes, and workstation codes to generate multiple sets of collaborative scheduling plans.

[0012] In some embodiments of the first aspect, greedy and random strategies are combined to sequentially divide the types of boxes corresponding to each category and the number of different workpieces at each delivery address corresponding to each category into corresponding box type codes and packing codes in four coding layers, including: For each delivery address, a greedy strategy is used to generate the initial packing code and box type code based on the principle that large capacity is prioritized over small capacity, and the boxes used are the ones with the most workpiece types. Randomly adjust the packing order between each delivery address, and convert the initial packing code and box type code into a packing code and box type code; Combining the random strategy and the maximum processing time optimization strategy, the corresponding processes of different workpieces in various boxes and the corresponding production machines of each process are divided into corresponding process codes and workstation codes in four coding layers, including: First, different production machines are randomly adapted to each process, and then the process code and workstation code are generated based on the principle that the machine with the shortest processing time is adapted to each process respectively, and the corresponding production machine selected in the previous process will no longer be adapted by the subsequent process.

[0013] In some embodiments of the first aspect, the policy parameters include control parameters, and step S4: updating the policy parameters includes: If the population dispersion of multiple alternative coordinated scheduling schemes is higher than the preset dispersion index, the attenuation step size of the control parameter is increased. If it is lower than the preset dispersion index, the attenuation step size of the control parameter is decreased. If it is equal to the preset dispersion index, the attenuation step size of the control parameter is kept unchanged. The dynamic adjustment index of the control parameter is linearly positively correlated with the preset dispersion index. Step S5: Adjusting each candidate coordinated scheduling solution based on the neighborhood search method, including: In process coding, two processes are randomly selected for swapping; in machine coding, a production machine is randomly replaced with the one with the shortest processing time within the adaptable range; and / or in box coding, a workpiece swap operation is performed. The workpiece swap operation includes swapping all workpieces in two boxes of different types or swapping target workpieces in two boxes with the same delivery address. The hierarchically stored Archive population includes a core layer and a balance layer. Step S7: performing non-dominated sorting on each group of collaborative scheduling solutions based on the fitness value of each alternative collaborative scheduling solution to update the hierarchically stored Archive population, including: Determine a new alternative coordinated scheduling scheme to be exchanged among different coordinated scheduling scheme groups according to the fitness value of each alternative coordinated scheduling scheme; If the new candidate co-scheduling scheme has the same solution dominance relationship as the candidate co-scheduling scheme in the core layer, then the similarity between the new candidate co-scheduling scheme and the candidate co-scheduling scheme in the balancing layer is determined based on the coding distance calculation; Replace the alternative collaborative scheduling scheme with the highest similarity to the new alternative collaborative scheduling scheme in the balancing layer to update the archive population of the hierarchical storage.

[0014] In some embodiments of the first aspect, before obtaining the information matrix and the delivery date of the workpiece for each delivery address, the method further includes: Get multiple order information, box type information and production workshop information; Construct an order demand matrix based on the workpiece type, workpiece quantity and delivery address information in multiple order information; Determine the delivery date of workpieces for each delivery address in multiple order information; Construct a box capacity matrix based on box type information; According to the production workshop information, obtain the process processing schedule and build the processing time matrix; According to the production workshop information, obtain the workpiece occupancy capacity table and build the workpiece occupancy capacity matrix; The preset constraints include: 1) Each process of any workpiece among different workpieces is completed on one and only one production machine; 4) For each process, the start time of the next process is greater than or equal to the start time of the previous process plus the processing time of the previous process; For all types of boxes, the total capacity of workpieces in each box shall not exceed the rated capacity of the box.

[0015] The second aspect of the present application provides a customized production and packaging collaborative scheduling system, including: a processor and a memory; the memory is coupled to the processor, the memory is used to store computer program code, and the processor calls the computer program code to enable the system to execute the method of the first aspect.

[0016] A third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method of the first aspect is implemented.

[0017] A fourth aspect of the present application provides a computer product, comprising a computer program, which implements the method of the first aspect when executed by a processor. It is understood that the customized production and packaging collaborative scheduling method, system, storage medium, and product of this application, through the production and packaging collaborative scheduling model and the use of problem-specific knowledge methods, generates strategy parameters and multiple sets of collaborative scheduling schemes based on information matrices including order demand matrix, box capacity matrix, processing time matrix, workpiece occupancy capacity matrix, and the delivery period of workpieces at each delivery address. It also takes into account multiple collaborative factors in both customized production and workpiece packaging, and can accurately calculate processing time, reasonably allocate production machines and arrange process sequences, reduce waiting time for production machines and packaging machines, and process connection time, thereby effectively shortening the production cycle, improving production efficiency, and enabling phased delivery, thereby improving customer satisfaction.

[0018] At the same time, by fully considering factors such as the workpiece's occupied capacity, the box type and capacity restrictions, and the various delivery addresses corresponding to the order information, a scientific packing plan is adopted to maximize the use of box space, reduce box waste, and significantly improve the packing rate. This enables the target collaborative scheduling solution derived from this production and packing collaborative scheduling model to meet the market demand for both high production efficiency and high packing efficiency for customized orders. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] Figure 1 A schematic diagram of a process flow of a method for coordinated scheduling of customized production and packaging provided in an embodiment of the present application; Figure 2 A schematic diagram of an application scenario of the customized production and packaging collaborative scheduling method provided in an embodiment of the present application; Figure 3 A schematic diagram of another application scenario of the customized production and packaging collaborative scheduling method provided in an embodiment of the present application; Figure 4 A schematic diagram of another application scenario of the customized production and packaging collaborative scheduling method provided in an embodiment of the present application; Figure 5 A schematic diagram of another application scenario of the method for coordinated scheduling of customized production and packaging provided in an embodiment of the present application; Figure 6 A scheduling Gantt chart for Solution 1 of the customized production and packaging collaborative scheduling method of this application; Figure 7 This is the scheduling Gantt chart of Solution 2 which does not consider the collaborative scheduling method of customized production and packaging; Figure 8 A structural diagram of the customized production and packaging collaborative scheduling system provided in an embodiment of the present application.

[0021] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0022] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0023] The terms "first", "second", etc. involved in this application are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0024] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the technical problem. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0025] See also Figure 1 , Figure 1 This is a flow chart of the customized production and packaging collaborative scheduling method provided by this application. The execution subject of this scheduling method can be a customized production and packaging collaborative scheduling system, such as Figure 1 As shown, the scheduling method may include the following steps: Step S1: Obtain the information matrix and the delivery date of the workpieces of each delivery address.

[0026] The information matrix includes the order demand matrix, the box capacity matrix, the processing time matrix and the workpiece occupancy capacity matrix.

[0027] Specifically, before executing step S110, multiple order information, box type information and production workshop information are obtained. Then, an order demand matrix is constructed based on the workpiece type, workpiece quantity and delivery address information in the multiple order information. , the order demand matrix is used to characterize the demand quantity of different workpieces for each delivery address, where p is the workpiece type, q is the delivery address, and m ij Indicates the number of workpieces i shipped to address j.

[0028] And determine the delivery date of the workpieces for each delivery address in multiple order information , where q represents the delivery address, and d j Represents the delivery date of delivery address j.

[0029] Construct box capacity matrix based on box type information , the box capacity matrix is used to characterize the capacity of various boxes, where, n Indicates the box type, b i Represents the capacity of the i-th type box. According to the production workshop information, obtain the process processing schedule and construct the processing time matrix ,The processing time matrix is used to characterize the processing time of each process of different workpieces on different machines, where, It represents the processing time of the jth process of workpiece i on production machine k. According to the production workshop information, obtain the workpiece occupancy capacity table and build the workpiece occupancy capacity matrix :The workpiece occupied capacity matrix is used to characterize the capacity occupied by different workpieces. p Indicates the artifact type, where c i Indicates workpiece i Occupied capacity.

[0030] In an application scenario, multiple order information is shown in Table 1: Table 1 Order information table

[0031] In this application scenario, the order demand matrix is:

[0032] According to the order table information, determine the delivery date of each delivery address. The expression is:

[0033] The capacity table of box types is shown in Table 2.

[0034] Table 2 Box type capacity table

[0035] In this application scenario, the box capacity matrix is:

[0036] The workpiece occupied capacity table is shown in Table 3.

[0037] Table 3 Workpiece Occupancy Capacity

[0038] Then in the application scenario, the workpiece capacity matrix is:

[0039] The process processing schedule is shown in Table 4: Table 4 Processing schedule

[0040] In this application scenario, the processing time matrix is:

[0041] Step S2: Using the problem-specific knowledge method, generate strategy parameters and multiple groups of collaborative scheduling solutions based on the information matrix and the delivery dates of the workpieces at each delivery address. Each group of collaborative scheduling solutions includes multiple alternative scheduling solutions for the same number of boxes.

[0042] For example, Figures 2 to 4 The individuals shown are coded A and C. Individuals A and B with the same number of boxes (both 3 boxes) are divided into one group, and C into another group.

[0043] As you can understand, since the length of the process and machine codes depends on the number of boxes, individuals with the same number of boxes are grouped to facilitate subsequent population evolution. This grouped evolution mechanism addresses the issue of varying code lengths for different packing solutions. Other algorithms, such as genetic algorithms, require complex repair mechanisms to handle variable chromosome lengths, which can easily lead to invalid solutions.

[0044] The strategy parameters include control parameter a, direction weight A and disturbance factor C.

[0045] Problem-specific knowledge methods include greedy strategies, random strategies, and maximum processing time optimization strategies.

[0046] In one embodiment, when using the problem-specific knowledge method, preset assumptions must be met. The preset assumptions include: (1) The process flow and processing time of different workpieces are known and determined.

[0047] (2) The processing time required for each process does not change with the production sequence or other factors.

[0048] (3) For multiple workpieces of the same type, the processing time calculation follows the simple accumulation principle. For example, if there are The time required for each workpiece 1 to complete process j is (j represents the process number), the number of processes for workpiece 1 is ,So The total processing time of workpiece 1 is This is based on the assumption that each workpiece 1 must go through all of its respective processes in sequence, and the processing time of each piece is accumulated to obtain the total processing time of all workpieces of this type.

[0049] (4) The conversion time between processes is negligible. When a production machine completes a process for a workpiece and switches to the next process, or switches from processing one workpiece to processing another, the preparation time, equipment adjustment time, etc. are all considered zero.

[0050] Specifically, the method for generating alternative coordinated scheduling schemes includes: under the preset assumptions, according to the information matrix and the delivery period of the workpieces at each delivery address, combining greedy and random strategies, respectively dividing the types of boxes corresponding to each type of box and the number of different workpieces at each delivery address corresponding to each type of box into the corresponding box type codes and packing codes in the four coding layers.

[0051] Then, by combining the random strategy and the maximum processing time optimization strategy, the corresponding processes of different workpieces in various types of boxes and the corresponding situations of different production machines are divided into corresponding process codes in four coding layers (the process code length is la (la = the number of all processes * the number of boxes, indicating the execution order of each process)) and workstation codes (workstation codes lb (lb = the number of all processes * the number of boxes), indicating the allocation of processes to production machines.

[0052] Finally, the box type code, packing code, process code, and workstation code are decoded to generate multiple sets of collaborative scheduling plans. Each alternative scheduling plan in each set of collaborative scheduling plans includes information such as the total number of addresses at each delivery address, the total capacity of the boxes corresponding to each delivery address and the actual capacity occupied by the workpieces in the boxes, the delivery date of the workpieces required at each delivery address when the last workpiece at each delivery address is completed, the demand for different workpieces for each order in different time periods, the time required for different workpieces to complete each process, and whether each process of different workpieces is processed on different production machines.

[0053] For example, the first layer is the box type code, and the digital code 1, 2, 3 represents the box type. The second layer is the packing code. For example, 2.11, 2.12, 2.13, each three digits of the code constitutes a box. The first digit before the decimal point represents the address, and the digit after the decimal point represents the number of workpieces. 2.11, 2.12, 2.13 means that 11 workpieces 1, 12 workpieces 2, and 13 workpieces 3 at address 2 constitute a box. The third layer is the process code with a length of l a (l a = total number of processes * number of boxes), the tens digit of the digital code represents the box number, the ones digit represents the workpiece number, and the number of times it appears repeatedly represents the process of the workpiece in the box. In this example, the first occurrence of the first digit 11 in the digital code indicates the first process of workpiece 1 in the first box, and the second occurrence of the fourth digit 11 indicates the second process of workpiece 1 in the first box; the fourth layer is the station code with a length of l b (lb = total process quantity * box quantity), digital codes 1, 2, 3, indicating that the process is assigned to machines 1, 2, 3.

[0054] like Figures 2 to 4 As shown, each alternative collaborative scheduling scheme (code A, code B or code C) includes the first-level box type code, the second-level packing code, the third-level process code and the fourth-level workstation code.

[0055] It can be understood that this embodiment adopts a dedicated coding structure of box type-packing-process-station designed specifically for the production-packing collaborative problem, directly mapping the actual decision variables to break the traditional production and packing separation decision-making model and achieve collaborative optimization of the two.

[0056] Step S3: Based on the multiple target values obtained by inputting each alternative scheduling scheme into the production and packaging collaborative scheduling model, each group of collaborative scheduling schemes is non-dominated sorted to establish a hierarchically stored Archive population.

[0057] Specifically, the solution to the production and packaging collaborative scheduling model includes a gray wolf optimization algorithm. In the gray wolf optimization algorithm, a population of gray wolves is considered to be one of multiple collaborative scheduling alternatives, and each individual gray wolf in the population is considered to be one of the alternative collaborative scheduling alternatives. Initial optimization parameters are set for each of the multiple collaborative scheduling alternatives. These initial optimization parameters include the number of alternative collaborative scheduling alternatives, the maximum number of iterations, the unit cost of early completion, and the unit cost of late completion.

[0058] The production and packing collaborative scheduling model, built using multi-objective optimization theory, aims to maximize average packing volume utilization, minimize the maximum completion time for multiple orders, and minimize lead / tardiness costs. The target-based collaborative scheduling scheme instructs different production machines to process different workpieces in different types of boxes, and for the packing machines to pack different workpieces into the corresponding boxes.

[0059] Step S4: Update the strategy parameters, and select the collaborative scheduling representative scheme from the hierarchically stored Archive population through the roulette method, and adjust other alternative collaborative scheduling schemes according to the fitness value corresponding to the collaborative scheduling representative scheme.

[0060] Specifically, updating strategy parameters includes dynamically adjusting control parameters, updating direction weights and disturbance factors. The collaborative scheduling representative scheme is regarded as the alpha wolf of the gray wolf population. 、 、 The fitness value of the production and packing collaborative scheduling model based on the collaborative scheduling representative scheme is obtained. The influence weight of the collaborative scheduling representative scheme on the update of other alternative collaborative scheduling schemes in the collaborative scheduling scheme group is dynamically adjusted according to the fitness value, and other alternative collaborative scheduling schemes are adjusted.

[0061] It can be understood that the production and packaging collaborative scheduling model is the objective function, and the corresponding fitness value can be obtained according to the objective function. According to the fitness value, the influence weight of the collaborative scheduling representative scheme on the update of other alternative collaborative scheduling schemes in the collaborative scheduling scheme group is dynamically adjusted to adjust other alternative collaborative scheduling schemes. The adjustment formula is ,in, Indicates other alternative collaborative scheduling schemes, Represents a collaborative scheduling representative scheme Represents a collaborative scheduling representative scheme 、 、 The influence weight of .in: , is the initial coefficient, T max is the maximum number of iterations, is the decay exponent, Represents the collaborative scheduling representative scheme under the i-th objective function (i.e., under the i-th production and packaging collaborative scheduling model) 、 、 The fitness value of .

[0062] Step S5: Adjust each candidate collaborative scheduling solution based on the neighborhood search method.

[0063] Step S6: Determine whether the adjusted alternative collaborative scheduling scheme meets the preset constraint conditions. If not, execute step S5; if so, execute step S7.

[0064] The preset constraints include: 1) Unique machine allocation constraint: each process of any workpiece among different workpieces can be completed on one and only one production machine; that is, , Indicates whether the process j of workpiece i is processed on the production machine k. If it is processed, it is 1, otherwise it is 0.

[0065] 2) Process sequence constraint: for each process, the start time of the next process is greater than or equal to the start time of the previous process plus the processing time of the previous process; that is, ,in, represents the start time of the j+1th process of workpiece i, represents the start time of the jth process of workpiece i, represents the processing time of the jth process of job i.

[0066] 3) Box capacity constraint: For each type of box, the total capacity of the workpieces in each box does not exceed the rated capacity of the box. , where p i Represents the capacity occupied by workpiece i, N yi represents the number of workpiece i in the yth box, C n Represents the rated capacity of the nth box.

[0067] Step S7: performing non-dominated sorting on each group of collaborative scheduling solutions based on the fitness value of each alternative collaborative scheduling solution to update the hierarchically stored Archive population.

[0068] In one embodiment, a new candidate co-scheduling scheme to be exchanged among different co-scheduling scheme groups is determined according to the fitness value of each candidate co-scheduling scheme.

[0069] Then, if the new candidate co-scheduling scheme has the same de-dominance relationship as the candidate co-scheduling scheme in the core layer, the similarity between the new candidate co-scheduling scheme and the candidate co-scheduling scheme in the balancing layer is determined based on the coding distance calculation.

[0070] As you can understand, the hierarchical storage archive population is stored in layers according to the degree of contribution to the target: 1) Core layer: stores the optimal solution for each target (such as the optimal solution for average packing volume utilization, the solution with the shortest maximum completion time), 2) Balance layer: stores the balanced solution for multiple targets. The steps for calculating similarity using encoding distance include: The coding distance calculation formula is: ,in For each layer weight (box type encoding layer =0.2, binning encoding layer =0.3, process coding layer =0.3, station coding layer =0.2), is the distance function of each layer.

[0071] Box type encoding distance function ,in Used to determine the encoding vectors of two boxes X and Y In the k Are they different in position? If so, =1, otherwise 0, Bin Encoding Distance Function , m is the number of workpieces with the same address, The process coding distance calculation method is edit distance, and the specific function is , Machine-encoded distance function .

[0072] Similarity: . D=D1+D2+D3+D4.

[0073] Finally, the alternative collaborative scheduling scheme with the highest similarity to the new alternative collaborative scheduling scheme in the balancing layer is replaced to update the archive population of the hierarchical storage.

[0074] That is, in the balancing layer, the Sim of the new solution and all old solutions is calculated, and the old solution with the largest Sim (i.e., the smallest distance) is selected for replacement. It can be understood that the elimination mechanism is that when a new solution (i.e., a new alternative co-scheduling solution) is added, if it has the same dominance relationship with the core layer solution, it will replace the solution with the highest similarity in the balancing layer (i.e., the alternative co-scheduling solution with the highest similarity).

[0075] It can be understood that for the four-layer coding structure, the update mechanism of this application can better maintain the effectiveness of the coding than other operations such as the crossover mutation operation of the genetic algorithm, has better convergence effect when optimizing the volume utilization target, and can find a more compact packing solution.

[0076] Step S8: Determine whether the number of Archive populations reaches a preset upper limit. If so, eliminate the alternative coordination scheduling schemes with a congestion greater than the preset congestion. Otherwise, execute step S9.

[0077] Step S9: Determine whether the maximum number of iterations has been reached. Otherwise, re-execute step S4. If so, output a collaborative scheduling solution group. Based on multiple target values of the current iteration, determine a target collaborative scheduling solution from multiple collaborative scheduling solution groups.

[0078] In one embodiment, a comprehensive analysis is performed on the three target values of average packing volume utilization, maximum completion time, and advance / delay cost. Among multiple groups of collaborative scheduling schemes, the alternative collaborative scheduling scheme with the best maximum average packing volume utilization, maximum completion time of multiple order information, and advance / delay cost is selected as the target collaborative scheduling scheme.

[0079] It can be understood that in the above technical solution, by taking the maximization of the average packing volume utilization, minimization of the maximum completion time of multiple order information and minimization of the early / delay cost as the optimization goals, a production and packing collaborative scheduling model is constructed in combination with multi-objective optimization theory, and using problem-specific knowledge methods and preset assumptions, multiple alternative collaborative scheduling schemes for customized production and material packing collaborative scheduling are generated based on the information matrix and the delivery date of the workpieces of each delivery address, as well as alternative scheduling schemes corresponding to the alternative collaborative scheduling schemes. After that, the packing collaborative scheduling model of the alternative scheduling schemes is input for iterative calculation, and finally, based on the multiple target values obtained in the current iteration, the target collaborative scheduling scheme is determined from the multiple alternative collaborative scheduling schemes. The target collaborative scheduling scheme can take into account the market demand for high production efficiency and high packing efficiency of customized orders.

[0080] Specifically, through the production and packing collaborative scheduling model and the use of problem-specific knowledge methods, strategy parameters and multiple groups of collaborative scheduling schemes are generated based on information matrices including order demand matrix, box capacity matrix, processing time matrix and workpiece occupancy capacity matrix, and the delivery period of workpieces at each delivery address. At the same time, multiple collaborative factors of the two links of customized production and workpiece packing are taken into account. It can accurately calculate the processing time, reasonably allocate production machines and arrange the process sequence, reduce the waiting time of production machines and packing machines and the process connection time, thereby effectively shortening the production cycle, improving production efficiency, and enabling phased delivery, thereby improving customer satisfaction.

[0081] At the same time, by fully considering factors such as the workpiece's occupied capacity, the box type and capacity restrictions, and the various delivery addresses corresponding to the order information, a scientific packing plan is adopted to maximize the use of box space, reduce box waste, and significantly improve the packing rate. This enables the target collaborative scheduling solution derived from this production and packing collaborative scheduling model to meet the market demand for both high production efficiency and high packing efficiency for customized orders.

[0082] In some embodiments, in step S2, greedy and random strategies are combined to divide the types of boxes corresponding to each type of box and the number of different workpieces at each delivery address corresponding to each type of box into corresponding box type codes and packing codes in four coding layers, including: First, for each delivery address, a greedy strategy is used. Based on the principle that large-capacity boxes are prioritized over small-capacity boxes, and that the boxes used are the ones with the most artifact types, initial packing codes and box type codes are generated. For example, for address 1, the largest-capacity box is first used to pack as many combinations of artifacts 1, 2, and 3 as possible, and the remaining items are then packed in smaller boxes. The same process is repeated for address 2, resulting in initial packing codes and box type codes corresponding to multiple different collaborative scheduling alternatives.

[0083] Next, the packing order between each delivery address is randomly adjusted, and the initial packing code and box type code are converted into a packing code and box type code.

[0084] For example, instead of loading address 1 first and then address 2, we can adjust it to loading address 2 first and then address 1. We can get the corresponding packing codes and box type codes for multiple alternative coordinated scheduling solutions. It can be understood that in this embodiment, a greedy strategy is first used to generate a better initial packing code and box type code, and then a random strategy is used to adjust them, so that the alternative collaborative scheduling schemes have diversity.

[0085] In some embodiments, in step S2, a random strategy and a maximum processing time optimization strategy are combined to divide the corresponding processes of different workpieces in various types of boxes and the corresponding situations of different production machines corresponding to each process into four layers of coding layers, including: first randomly adapting different production machines to each process, and then adapting the machine with the shortest processing time among different production machines to each process, and generating process codes and work station codes according to the principle that the corresponding production machine selected in the previous process will no longer be adapted by the subsequent process.

[0086] As you can understand, in this embodiment, based on the machine type required for a process, all machines suitable for that process are screened from the machine types. Then, based on the maximum processing time optimization, machines with shorter processing times are screened to perform the processing task. When a process is subsequently encountered, machines that have not been selected are selected, and the above principle of selecting machines based on the shortest processing time is continuously followed until all machines suitable for that process have been used. This ensures that each machine can participate in the processing flow of the corresponding process at the appropriate time.

[0087] In some embodiments, the multiple target values include average packing volume utilization, maximum completion time, and lead / delay cost. The production and packing collaborative scheduling model includes a packing average volume utilization determination model, a completion time determination model, and a lead / delay cost determination model. That is, in step S3, the multiple target values obtained by inputting each alternative scheduling solution into the production and packing collaborative scheduling model include the following steps: Step a.1: Determine the total number of addresses for each delivery address in the alternative scheduling scheme, the total capacity of the boxes corresponding to each delivery address, and the actual capacity occupied by the workpieces in the boxes; Step a.2: Determine the packing volume utilization rate for each delivery address based on the total capacity of the box and the actual capacity occupied by the workpieces in the box; Step a.3: Calculate the average packing volume utilization rate based on the packing volume utilization rate of each delivery address and the total number of addresses; It can be understood that after inputting the alternative scheduling scheme into the average container volume utilization determination model, the calculation process of the average container volume utilization can be obtained.

[0088] For example, the expression of the average volume utilization rate determination model of the packing is: (1). Among them, represents the average packing volume utilization rate, n represents the total number of addresses for each delivery address, r i Indicates the packing volume utilization rate of the i-th delivery address. The total capacity of all boxes sent to the i-th delivery address is the sum of the box capacities (i.e. the total capacity of the boxes sent to each delivery address). For example, if m boxes are used to send to the delivery address, the box capacities are . It represents the actual capacity occupied by the workpieces in the box corresponding to the i-th delivery address.

[0089] Step a.4: Determine the completion time of the last workpiece in each delivery address in the alternative scheduling plan; Step a.5: Accumulate the final completion time to obtain the maximum completion time; It can be understood that after inputting the alternative scheduling scheme into the completion time determination model, the calculation process of minimizing the maximum completion time can be obtained.

[0090] For example, the expression of the completion time determination model is: ,in, represents minimizing the maximum completion time, Indicates the completion time of the last workpiece in the delivery address r.

[0091] Step a.6: Based on the delivery date of the workpieces required by each delivery address in the alternative scheduling plan and the completion time of the last workpiece in each delivery address, obtain the early completion time and delay time of each delivery address Step a.7: Obtain the lead / delay cost based on the early completion time and the delayed time, as well as the preset early completion unit cost and delayed unit cost.

[0092] It can be understood that inputting the alternative scheduling scheme into the advance / delay cost determination model can obtain the calculation process of the advance / delay cost.

[0093] For example, the expression of the advance / delay cost determination model is: in, represents the lead time / delay cost, and They are the preset unit cost of early completion and the unit cost of delay, For delivery address r The required delivery time of the workpiece, For delivery address r The early completion time, For delivery address r The delay time. Represents the completion time of the last workpiece in the delivery address r.

[0094] In one application scenario of this embodiment, it is possible to generate Figures 2 to 4The three target values of average packing volume utilization, maximum completion time and early / delay cost corresponding to the three alternative scheduling schemes coded A, B and C.

[0095] Specifically, individual code A has an average container volume utilization of 74%, a maximum completion time of 1760, and an early / delay cost of 255. Individual code B has an average container volume utilization of 81.8%, a maximum completion time of 1800, and an early / delay cost of 567. Individual code C has an average container volume utilization of 79.5%, a minimum completion time of 1790, and an early / delay cost of 426. It can be seen that code A has a greater advantage in maximum completion time and early / delay cost, while code B has a greater advantage in average container volume utilization.

[0096] In some embodiments, dynamically adjusting the control parameter a in step S4 includes: If the population dispersion of multiple alternative coordinated scheduling schemes is higher than the preset dispersion index, the attenuation step size of the control parameter is increased to accelerate the contraction.

[0097] If it is less than the preset dispersion index, the attenuation step size of the control parameter is reduced to extend the exploration; if it is equal to the preset dispersion index, the attenuation step size of the control parameter is kept unchanged.

[0098] Among them, the dynamic adjustment index of the control parameter is linearly positively correlated with the preset dispersion index.

[0099] For example, the adjustment formula of the control parameter a is: .

[0100] in It is a dynamic adjustment index, which is adaptively calculated based on the preset dispersion index, such as , α is the preset weight factor, is the preset dispersion indicator.

[0101] Understandably, in traditional gray wolf optimization algorithms, the control parameter a (which determines the speed at which the encirclement around prey shrinks or expands) typically decays according to a fixed formula. This fixed strategy can lead to premature convergence in the early stages: if a decays too quickly, the algorithm prematurely falls into a local optimum; and stagnation in the later stages: if a decays too slowly, the algorithm struggles to perform a refined search. In this embodiment, the decay rate of a is adjusted in real time based on population diversity or convergence status, improving search refinement.

[0102] In some embodiments, the neighborhood search method includes a local search, and step S5: adjusting each candidate collaborative scheduling solution based on the neighborhood search method includes: In process coding, two processes are randomly selected for interchange; in machine coding, a production machine is randomly replaced with the machine with the shortest processing time within the adaptable range and / or workpiece interchange operations are performed in packaging coding.

[0103] The workpiece swapping operation includes swapping all workpieces in two boxes from various boxes or swapping target workpieces in two boxes with the same delivery address. Figure 5 As shown, all artifacts in the box coded as 3 are swapped with all artifacts in the box coded as 2.

[0104] The following is a comparative example compared with the method of the present application to discuss the superiority of the method of the present application.

[0105] Specifically, the target collaborative scheduling scheme generated by applying the customized production and packaging collaborative scheduling method of this application is Scheme 1.

[0106] Scheme 2 is a scheduling scheme generated without considering the coordinated scheduling of customized production and packing (i.e., using other algorithms).

[0107] Table 5 compares the content of Scheme 1 and Scheme 2, including the processing sequence of the processes, the corresponding production machines, and the packaging plan. Table 6 shows the corresponding target values of the two schemes.

[0108] Table 5 Comparison of the contents of Scheme 1 and Scheme 2

[0109] In solution 1 The jth process of workpiece i in the nth box is processed on machine k. In the packing plan, (11-12-13)->3 means that 11 pieces of workpiece 1, 12 pieces of workpiece 2, and 13 pieces of workpiece 3 are packed in box type 3.

[0110] In solution 2 It means that the j-th process of workpiece i is processed on machine k, and the packing plan is the same as above.

[0111] Table 6 Target value comparison

[0112] Combined with Table 6, Figure 6 Scheme 1 Scheduling Gantt Chart and Figure 7 The Gantt chart of Scheme 2 shows that Scheme 1 has more advantages in terms of maximum completion time and minimum advance / delay functions. In the phased delivery scenario, Scheme 1 is the best and preferred option.

[0113] It can be understood that the customized production and packing collaborative scheduling method of the present application, with its dedicated coding structure of box type-packing-process-station, is designed specifically for the production-packing collaborative problem, and directly maps the actual decision variables to break the traditional production and packing separation decision-making model, and realize the collaborative optimization of the two. For the four-layer coding structure, the update mechanism of this method can better maintain the effectiveness of the coding than other operations such as the crossover and mutation operations of the genetic algorithm, and has better convergence effect when optimizing the volume utilization target, and can find a more compact packing solution. This method introduces a group evolution mechanism to solve the problem of different coding lengths for different packing solutions. Other algorithms such as genetic algorithms that handle variable-length chromosomes require additional design of complex repair mechanisms, which are prone to invalid solutions.

[0114] Figure 8 This is a schematic diagram of the structure of the customized production and packaging collaborative scheduling system provided in this application. Figure 8 As shown, the customized production and packaging collaborative scheduling system 10 includes: Processor 11, memory 12 and bus 13; The memory 12 is used to store computer program codes of the processor 11; The processor 11 is configured to execute the technical solution of the method for coordinated scheduling of customized production and packaging in any of the aforementioned method embodiments by executing computer program code.

[0115] The customized production and packaging collaborative scheduling system 10 is used to execute the technical solution provided in any of the aforementioned method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0116] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for coordinated scheduling of customized production and packaging as described above is implemented.

[0117] The present application also provides a computer product, including a computer program, which, when executed by a processor, implements the above-mentioned customized production and packaging coordinated scheduling method. Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for coordinated scheduling of customized production and packaging, characterized in that: Methods include: Step S1: Obtaining an information matrix and the delivery date of workpieces of each delivery address, wherein the information matrix includes an order demand matrix, a box capacity matrix, a processing time matrix, and a workpiece occupied capacity matrix; Step S2: Using the problem-specific knowledge method, generate strategy parameters and multiple sets of collaborative scheduling solutions based on the information matrix and the delivery dates of the workpieces at each delivery address, where each set of collaborative scheduling solutions includes multiple alternative scheduling solutions for the same number of boxes; Step S3: Based on the multiple target values obtained by inputting each alternative scheduling plan into the production and packaging collaborative scheduling model, each group of collaborative scheduling plans is non-dominated sorted to establish a hierarchically stored Archive population; Step S4: Update the strategy parameters, and select the collaborative scheduling representative scheme from the hierarchically stored Archive population through the roulette wheel method, and adjust other alternative collaborative scheduling schemes according to the fitness value corresponding to the collaborative scheduling representative scheme; Step S5: adjusting each candidate coordinated scheduling solution based on a neighborhood search method; Step S6: Determine whether the adjusted alternative coordinated scheduling scheme meets the preset constraint conditions. If not, execute step S5; if yes, execute step S7. Step S7: performing non-dominated sorting on each group of collaborative scheduling solutions based on the fitness value of each alternative collaborative scheduling solution to update the hierarchically stored Archive population; Step S8: Determine whether the number of Archive populations reaches a preset upper limit. If yes, remove the alternative coordination scheduling schemes whose congestion is greater than the preset congestion. Otherwise, execute step S9. Step S9: Determine whether the maximum number of iterations has been reached. Otherwise, re-execute step S4. If so, output a collaborative scheduling solution group. Based on multiple target values of the current iteration, determine a target collaborative scheduling solution from multiple collaborative scheduling solution groups.

2. The method according to claim 1, characterized in that The production and packing collaborative scheduling model is constructed by combining multi-objective optimization theory with the optimization objectives of maximizing the average packing volume utilization, minimizing the maximum completion time of multiple order information, and minimizing the lead / delay cost.

3. The method according to claim 1 or 2, characterized in that The multiple target values include average container volume utilization, maximum completion time, and lead / delay cost. Step S3: inputting the multiple target values obtained by the production and container collaborative scheduling model based on each alternative scheduling solution includes: Determine the total number of addresses for each delivery address in the alternative scheduling plan, the total capacity of the boxes corresponding to each delivery address, and the actual capacity occupied by the workpieces in the boxes; Determining the packing volume utilization rate of each delivery address based on the total capacity of the box and the actual capacity occupied by the workpieces in the box; Obtaining an average packing volume utilization rate based on the packing volume utilization rate of each delivery address and the total number of addresses; and Determine the completion time of the last workpiece in each delivery address in the alternative scheduling plan; Accumulating the final completion times to obtain a maximum completion time; and According to the delivery date of the workpieces required by each delivery address in the alternative scheduling plan and the completion time of the last workpiece in each delivery address, the early completion time and the delayed completion time of each delivery address are obtained; The early completion time and the delayed completion time, as well as the preset early completion unit cost and delayed unit cost, are used to obtain the early completion / delay cost.

4. The method according to claim 1, wherein The step S2: using a problem-specific knowledge method to generate multiple sets of collaborative scheduling solutions based on the information matrix and the delivery dates of the workpieces at each delivery address includes: According to the information matrix and the delivery date of the workpieces at each delivery address, the greedy and random strategies are combined to divide the types of boxes corresponding to each type of box and the number of different workpieces at each delivery address into the corresponding box type codes and packing codes in the four coding layers. Combining the random strategy and the maximum processing time optimization strategy, the corresponding processes of different workpieces in various boxes and the corresponding production machines of each process are divided into corresponding process codes and workstation codes in four coding layers. Decode box type codes, packing codes, process codes, and workstation codes to generate multiple sets of collaborative scheduling plans.

5. The method according to claim 4, characterized in that The combination of greedy and random strategies divides the types of boxes and the number of different workpieces at each delivery address into four coding layers, including the corresponding box type codes and packing codes, including: For each delivery address, a greedy strategy is used to generate the initial packing code and box type code based on the principle that large capacity is prioritized over small capacity, and the boxes used are the ones with the most workpiece types. Randomly adjust the packing order between each delivery address, and convert the initial packing code and box type code into a packing code and box type code; The random strategy and the maximum processing time optimization strategy are combined to divide the corresponding processes of different workpieces in various boxes and the corresponding production machines of each process into corresponding process codes and workstation codes in four coding layers, including: First, different production machines are randomly adapted to each process, and then the process code and workstation code are generated based on the principle that the machine with the shortest processing time is adapted to each process respectively, and the corresponding production machine selected in the previous process will no longer be adapted by the subsequent process.

6. The method according to claim 4, characterized in that The strategy parameters include control parameters. Step S4: updating the strategy parameters includes: If the population dispersion of multiple alternative coordinated scheduling schemes is higher than the preset dispersion index, the attenuation step size of the control parameter is increased. If it is lower than the preset dispersion index, the attenuation step size of the control parameter is decreased. If it is equal to the preset dispersion index, the attenuation step size of the control parameter is kept unchanged. The dynamic adjustment index of the control parameter is linearly positively correlated with the preset dispersion index. The step S5: adjusting each candidate coordinated scheduling solution based on the neighborhood search method, includes: In process coding, two processes are randomly selected for swapping; in machine coding, a production machine is randomly replaced with the one with the shortest processing time within the adaptable range; and / or in box coding, a workpiece swap operation is performed. The workpiece swap operation includes swapping all workpieces in two boxes of different types or swapping target workpieces in two boxes with the same delivery address. The hierarchically stored Archive population includes a core layer and a balance layer. Step S7: performing non-dominated sorting on each group of collaborative scheduling schemes based on the fitness value of each alternative collaborative scheduling scheme to update the hierarchically stored Archive population includes: Determine a new alternative coordinated scheduling scheme to be exchanged among different coordinated scheduling scheme groups according to the fitness value of each alternative coordinated scheduling scheme; If the new candidate co-scheduling scheme has the same solution dominance relationship as the candidate co-scheduling scheme in the core layer, then the similarity between the new candidate co-scheduling scheme and the candidate co-scheduling scheme in the balancing layer is determined based on the coding distance calculation; Replace the alternative collaborative scheduling scheme with the highest similarity to the new alternative collaborative scheduling scheme in the balancing layer to update the archive population of the hierarchical storage.

7. The method according to claim 1, characterized in that Before obtaining the information matrix and the delivery date of the workpieces at each delivery address, the method further includes: Get multiple order information, box type information and production workshop information; Building an order demand matrix based on the workpiece type, workpiece quantity and delivery address information in the plurality of order information; Determining the delivery date of the workpiece for each delivery address in the plurality of order information; Construct a box capacity matrix based on box type information; According to the production workshop information, obtain the process processing schedule and build the processing time matrix; According to the production workshop information, obtain the workpiece occupancy capacity table and build the workpiece occupancy capacity matrix; The preset constraints include: 1) Each process of any workpiece among different workpieces is completed on one and only one production machine; 2) For each process, the start time of the next process is greater than or equal to the start time of the previous process plus the processing time of the previous process; 3) For all types of boxes, the total capacity of workpieces in each box shall not exceed the rated capacity of the box.

8. A customized production and packaging collaborative scheduling system, characterized by: include: processor and memory; The memory is coupled to the processor, and the memory is used to store computer program code. The processor calls the computer program code to enable the system to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer product comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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