Battery pack multi-production line collaborative scheduling method, storage medium and battery production system

Abstract

The present application discloses a method for collaborative scheduling of multiple production lines of battery packs, a storage medium, and a battery production system. First, a multi-objective production optimization model is constructed according to preset target constraints and preset mutually exclusive constraints, and an initial scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual scheduling plan, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

Description

Technical Field

[0001] The present application relates to the field of circuit technology, and in particular to a method for coordinated production scheduling of multiple production lines of battery packs, a storage medium, and a battery production system. Background Art

[0002] In the battery production system, the Technical Office (PTO department) has long been responsible for the group's R&D and shipment delivery tasks, and for this purpose it is necessary to frequently formulate production plans. Because the production plan belongs to the early sample or test sample stage, the production orders have a wide variety of categories and different batch sizes.

[0003] In the related art, the scheduling of production orders is usually carried out based on the experience of process production personnel, which results in low production efficiency. Summary of the invention

[0004] In view of the above problems, the present application provides a method for collaborative scheduling of multiple production lines of a battery pack, a storage medium, and a battery production system, aiming to provide a production line scheduling solution for improving production efficiency.

[0005] A first aspect of an embodiment of the present application provides a method for collaborative production scheduling of multiple production lines of a battery pack, the method comprising: Construct a multi-objective production optimization model according to preset objective constraints and preset mutually exclusive constraints; An initial production schedule is determined according to the multi-objective production optimization model, and according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to optimize the production scheduling of the initial production schedule to obtain an actual production schedule.

[0006] In the technical solution of the embodiment of the present application, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined based on the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0007] In some embodiments, the battery pack multi-production line collaborative production scheduling method further includes: The actual production schedule is dynamically adjusted according to the abnormal event, and the adjusted production schedule is output.

[0008] In the technical solution of the embodiment of the present application, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0009] In some embodiments, a multi-objective production optimization model is constructed according to preset objective constraints and preset mutually exclusive constraints, including: The multi-objective production optimization model is established according to the preset mutually exclusive constraints with minimizing product delay time and minimizing process switching time as preset target constraints.

[0010] In the technical solution of the embodiment of the present application, the product delay time is the difference between the actual completion time and the planned deadline. The larger the difference, the more serious the product delay, and the production delay will occur. If the difference is 0 or less than 0, it means that the product can be delivered in time. The process switching time refers to the switching time between different production processes during the product production process. The longer the switching time, the lower the production efficiency, which has a greater impact on the production cost of the product. Therefore, in this embodiment, minimizing the product delay time and minimizing the process switching time are preset target constraints. Under the condition of meeting the preset mutually exclusive constraints, a multi-objective production optimization model is constructed, and the actual production scheduling plan is obtained by optimizing the production scheduling relationship. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0011] In some embodiments, the preset mutually exclusive constraint conditions include at least one of a production capacity constraint, a material synchronization constraint, and a production line equipment occupancy mutually exclusive constraint.

[0012] In the technical solution of the embodiment of the present application, the production capacity constraint indicates that the production volume of each process cannot exceed its maximum production capacity, and the production capacity of each device is also limited. The material synchronization constraint indicates that the material demand of each production process cannot exceed the total inventory of the materials required for the process. The production line equipment occupancy mutual exclusion constraint indicates that the same equipment cannot be occupied by multiple processes at the same time, and the start time of each process needs to meet the production sequence of the product. By optimizing the initial production schedule according to the multi-objective production optimization model within reasonable constraints and minimizing the product delay time and the process switching time as preset target constraints, the intermediate production schedule is obtained, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0013] In some embodiments, the production capacity constraints include: Set the processing time of product k in process i to be less than or equal to the maximum available working time of process i; where k represents the category of the product and i represents the category of the process.

[0014] In the technical solution of the embodiment of the present application, the production capacity constraint indicates that the production volume of each process cannot exceed its maximum production capacity, and the production capacity of each device is also limited. In each process i, the processing time of the corresponding product k in process i is less than or equal to the maximum available working hours of process i. Product k represents the kth product, process i represents the i-th process, and Ti is the maximum available working hours of the i-th process. Within reasonable production capacity constraints, the initial production schedule is optimized according to the multi-objective production optimization model to obtain an intermediate production schedule, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0015] In some embodiments, it is set that the material consumed by product k in process i is less than the real-time material inventory of process i; wherein k represents the category of the product, and i represents the category of the process.

[0016] In the technical solution of the embodiment of the present application, the material synchronization constraint indicates that the material demand of each production process cannot exceed the total inventory of materials required for the process, and the total materials consumed by the kth product in process i need to be less than or equal to the real-time material inventory of process i, so as to avoid the production process of the kth product from being stopped due to lack of materials and affecting production efficiency through the material synchronization constraint. Under the condition of satisfying the material synchronization constraint, the initial production schedule is optimized according to the multi-objective production optimization model to obtain the intermediate production schedule, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration, and finally the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0017] In some embodiments, the production line equipment occupancy mutual exclusion constraint includes: Set the same production line to only be assigned to one target product process within the same time period; and / or The same equipment can only be assigned to one target product process in the same time period.

[0018] In the technical solution of the embodiment of the present application, the mutually exclusive constraint of production line equipment occupancy indicates that the same equipment cannot be occupied by multiple processes at the same time, and the start time of each process needs to meet the production order of the product, and, in each product, the start time of the i+1th process is greater than or equal to the sum of the end time of the ith process and the process switching time. By setting the mutually exclusive constraint of production line equipment occupancy, the utilization efficiency of the equipment can be improved, and conflicts in the use of equipment can be avoided. Under the production order specified for each product, the process time of each product is optimized, with minimizing the product delay time and minimizing the process switching time as preset target constraints. The initial production scheduling plan is optimized according to the multi-objective production optimization model to obtain an intermediate production scheduling plan, and the intermediate production scheduling plan is used as the input of the multi-objective production optimization model to continue iterating. Finally, the production scheduling relationship is optimized to obtain the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0019] In some embodiments, determining an initial production scheduling plan according to the multi-objective production optimization model includes: The multi-objective production optimization model is solved according to historical production scheduling data and a linear programming solver to determine the initial production scheduling plan.

[0020] In the technical solution of the embodiment of the present application, historical production scheduling data is obtained, and a linear programming solver is used to solve a multi-objective production optimization model based on the historical production scheduling data to obtain an initial production scheduling plan. In the initial production scheduling plan, the production arrangement of each product meets the preset mutually exclusive constraints, and when the preset mutually exclusive constraints are met, the production scheduling relationship is optimized according to the multi-objective production optimization model to obtain an intermediate production scheduling plan, and the intermediate production scheduling plan is continued to be iterated as the input of the multi-objective production optimization model, and finally the production scheduling relationship is optimized to obtain the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0021] In some embodiments, according to the preset mutually exclusive constraint conditions, an alternating iterative optimization mathematical model and a heuristic algorithm are used to optimize the production scheduling of the initial production scheduling plan to obtain an actual production scheduling plan, including: The initial production scheduling plan is locally optimized by using a genetic algorithm or a simulated annealing algorithm, and a multi-objective Pareto front solution set is used to screen the optimal solution.

[0022] In the technical solution of the embodiment of the present application, the production line allocation sequence can be encoded by a genetic algorithm, and its fitness function includes capacity constraints and switching costs. For example, the NSGA-II framework is used to process multiple objectives (multiple objectives include minimizing product delay time and minimizing process switching time) in combination with the constraints. The simulated annealing algorithm can explore neighborhood solutions at a fixed temperature, dynamically adjust the production sequence to reduce switching time, and overcome problems such as equipment failure response and material changes. The initial production schedule is locally optimized by a genetic algorithm or a simulated annealing algorithm, and in the case of meeting the preset mutually exclusive constraints, the optimal solution is screened using a multi-objective Pareto front solution set with minimizing product delay time and minimizing process switching time as preset target constraints to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted in real time based on abnormal events based on a multi-objective optimization model, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0023] In some embodiments, according to the preset mutually exclusive constraint conditions, an alternating iterative optimization mathematical model and a heuristic algorithm are used to optimize the production scheduling of the initial production scheduling plan to obtain an actual production scheduling plan, including: By adopting the method of alternating iterative optimization mathematical model and heuristic algorithm, multiple actual production scheduling plans are generated with minimizing product delay time and minimizing process switching time as preset target constraints.

[0024] In the technical solution of the embodiment of the present application, the initial production scheduling plan is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted using a heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain an intermediate production scheduling plan, and the intermediate production scheduling plan is repeatedly iterated as the input of the multi-objective production optimization model. In the case of meeting the preset mutually exclusive constraints, minimizing the product delay time and minimizing the process switching time are used as the preset target constraints, and a suitable plan is selected from multiple production scheduling plans by using a multi-objective Pareto front solution set to screen the optimal solution as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time based on abnormal events based on the multi-objective optimization model, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0025] In some embodiments, according to the preset mutually exclusive constraint conditions, the production scheduling of the initial production scheduling scheme is optimized by using an alternating iterative optimization mathematical model and a heuristic algorithm to obtain an actual production scheduling scheme, further comprising: When the number of alternating iterations reaches the set number of iterations, or the difference between the minimized product delay time and the minimized process switching time in the consecutive preset number of iterations is less than the preset threshold, the iteration is stopped.

[0026] In the technical solution of the embodiment of the present application, the initial production scheduling plan is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted by the heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain the intermediate production scheduling plan, and the intermediate production scheduling plan is repeatedly iterated as the input of the multi-objective production optimization model. When the number of alternating iterations reaches the set number of iterations, or the difference between the minimized product delay time and the minimized process switching time in the continuous preset number of iterations is less than the preset threshold, the iteration is stopped. For example, the solution with an improvement of more than 5% in the product delay time is preferentially retained, and the solution with a reduction of more than 10% in the process switching time is secondarily selected. The multi-objective Pareto front solution set is used to screen the optimal solution to select a suitable solution from multiple production scheduling plans as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0027] In some embodiments, dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: The production schedule is re-planned based on material inventory warnings and abnormal equipment shutdown events, and the process priority queue is updated.

[0028] In the technical solution of the embodiment of the present application, abnormal events include material inventory warnings and abnormal equipment downtime events. During the implementation of the actual production scheduling plan, the production scheduling plan can be dynamically re-planned according to events such as material shortages, equipment failures, and order changes, and the input parameters of the multi-objective production optimization model can be adjusted. The process priority queue is updated, and then repeated iterations are performed again. The multi-objective Pareto front solution set is used to screen the optimal solution and select a suitable solution from multiple production scheduling plans as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time based on the multi-objective optimization model according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0029] In some embodiments, the step of constructing a multi-objective production optimization model according to preset objective constraints and preset mutually exclusive constraints includes: The multi-objective production optimization model is adjusted according to the weight coefficient of the project delay time and the weight coefficient of the process switching time.

[0030] In the technical solution of the embodiment of the present application, the initial production scheduling plan is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted by the heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain the intermediate production scheduling plan, and the intermediate production scheduling plan is repeatedly iterated as the input of the multi-objective production optimization model. After each iteration, the constraint conditions, the weight coefficient of the project delay time or the weight coefficient of the process switching time are updated, so that the genetic algorithm or the simulated annealing algorithm can complement each other. In the case of meeting the preset mutually exclusive constraint conditions, the product delay time and the process switching time are minimized as the preset target constraints, and the multi-objective Pareto front solution set is used to select the optimal solution from multiple production scheduling plans. The appropriate plan is selected as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0031] In some embodiments, the constructing of a multi-objective production optimization model according to preset objective constraints and preset mutually exclusive constraints further includes: The multi-objective production optimization model is adjusted according to the priority weight coefficients of the products.

[0032] In the technical solution of the embodiment of the present application, the product delay time is the difference between the actual completion time and the planned deadline. The larger the difference, the more serious the product delay, and the production delay will occur. If the difference is 0 or less than 0, it means that the product can be delivered in time. The process switching time represents the switching time between different production processes during the production process of the product. The longer the switching time, the lower the production efficiency, which has a greater impact on the production cost of the product. In the multi-objective production optimization model, the difference between the actual completion time and the planned deadline is compared with 0, and its maximum value is selected as a parameter, and the product delay time is calculated in combination with the priority weight coefficient Wi of the corresponding product i. Finally, the weight coefficient w1 of the product delay time and the weight coefficient w2 of the process switching time are combined. Under the condition of meeting the preset mutually exclusive constraints, the objective function of the multi-objective production optimization model is solved, and the actual production scheduling plan is obtained by optimizing the production scheduling relationship. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0033] In some embodiments, the method of optimizing the production scheduling of the initial production schedule by using an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraint conditions to obtain an actual production schedule includes: A heuristic algorithm is used to locally adjust the process sequence of multiple objectives to reduce the process switching time.

[0034] In the technical solution of the embodiment of the present application, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model to reduce the process switching time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0035] In some embodiments, the method of optimizing the production scheduling of the initial production schedule by using an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraint conditions to obtain an actual production schedule includes: The switching time of adjacent processes is calculated by adopting a matrix storage method, and the process sequence of the initial production scheduling plan is adjusted according to the basic switching time of adjacent processes, the process difference coefficient, and the equipment cleaning time.

[0036] In the technical solution of the embodiment of the present application, product k, product k+1, product K+2, etc. are scheduled for production on different production lines, which may share some processes. A production line-product association matrix is ​​established using a matrix storage method, that is, the mapping relationship between products and processes is described through a process route matrix. For example, product k needs to go through process i→process i+3→process i+4 to complete production. Check whether all preset mutually exclusive constraints are covered. The preset mutually exclusive constraints may include equipment occupancy time, material inventory, and whether the complex scheduling requirements of multiple products and multiple production lines are met. According to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, reduce the switching time tij from process i to j, and thus optimize the production scheduling relationship to obtain an actual production scheduling plan, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0037] In some embodiments, dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: The production schedule is re-planned based on material inventory warnings and abnormal equipment shutdown events, and the process priority queue is updated.

[0038] In some embodiments, dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When it is detected that the material inventory is lower than the safety threshold, the process schedule that depends on the material will be automatically frozen.

[0039] In the technical solution of the embodiment of the present application, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production schedule. In the actual production schedule, when it is monitored that the material inventory is lower than the safety threshold, the process schedule that depends on the material is automatically frozen, and a feasible solution can be quickly generated based on the multi-objective optimization model and the heuristic algorithm, and the actual production schedule is dynamically adjusted to improve the real-time response capability of the dynamic adjustment, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0040] In some embodiments, dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When the duration of a production line equipment failure exceeds the preset tolerance, the pending processes of the production line are reallocated to an alternative production line.

[0041] In the technical solution of the embodiment of the present application, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, when the duration of the production line equipment failure exceeds the preset tolerance, the key bottleneck process (i.e., the process where the duration of the production line equipment failure exceeds the preset tolerance) is identified based on the multi-objective optimization model and the heuristic algorithm, and then the neighborhood operation is applied to make adjustments, evaluate the reduction of the process switching time, use dynamic weights and target priorities for multi-objective optimization, and dynamically adjust the actual production scheduling plan to obtain an adjusted production scheduling plan, thereby improving the real-time response capability of the dynamic adjustment, so that the project completion time is as close as possible to its planned deadline, and reducing production delays.

[0042] In some embodiments, dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When a new product order is inserted, the order is inserted in the current production scheduling gap based on a multi-objective optimization model and a heuristic algorithm to obtain the adjusted production scheduling plan.

[0043] In the technical solution of the embodiment of the present application, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, when a new product order is inserted, an order is inserted in the current production scheduling gap based on a multi-objective optimization model and a heuristic algorithm to obtain the adjusted production scheduling plan, thereby improving the real-time response capability of dynamic adjustment, making the project completion time as close to its planned deadline as possible, and reducing production delays.

[0044] In some embodiments, the method for collaborative scheduling of battery pack multi-production lines further includes: The key nodes and progress of the actual production scheduling plan are visually displayed.

[0045] In the technical solution of the embodiment of the present application, the product processes of multiple target products in the multi-objective production optimization model are adjusted by means of an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and the product delay time, thereby optimizing the production scheduling relationship and obtaining the actual production schedule. During the execution of the actual production schedule, the actual production schedule is visualized, and the capacity utilization rate of each production line in the next 72 hours is displayed through a color gradient. The consumption rate and replenishment nodes for the next 5 days are displayed by material category, and the bottleneck processes that affect the overall delivery are prompted with a flashing red prompt, reminding the user of the current production schedule progress in real time, and issuing early warnings for related nodes.

[0046] A second aspect of the embodiments of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0047] A third aspect of an embodiment of the present application further provides a battery production scheduling system, comprising a memory, a processor, and a computer program stored in the memory, wherein when the processor executes the program, the steps of the method described in any one of the above embodiments are implemented.

[0048] In the technical solution of the embodiment of the present application, relevant computer programs and historical production scheduling data are stored on the memory. When the program is executed by the processor, the steps of the method described in any of the above embodiments are implemented. First, a multi-objective production optimization model is constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0049] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings: Figure 1 A first schematic diagram of a method for collaborative production scheduling of multiple production lines of battery packs provided in an embodiment of the present application; Figure 2 A second schematic diagram of a method for collaborative production scheduling of multiple production lines of battery packs provided in an embodiment of the present application; Figure 3 A third schematic diagram of a method for collaborative production scheduling of multiple production lines of battery packs provided in an embodiment of the present application; Figure 4 A fourth schematic diagram of the method for coordinated production scheduling of multiple production lines of battery packs provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following embodiments of the technical solution of the present application will be described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0053] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0054] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The phrase "second connection port" at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] In the battery production system, the technical office (PTO department) department has long been responsible for the group's R&D and shipment delivery tasks. For this reason, it is necessary to frequently formulate production plans, because most of them belong to the sample A and B stages. Among them, sample A represents the samples in the early stage of product development, which are usually used for internal testing and verification of design concepts to check whether the basic functions of the product meet the design requirements. Sample B is closer to the form of the final product and will undergo more extensive testing, including durability, performance, user experience, etc., to ensure that the product can meet the needs of the market and users. In the production of batteries, samples need to be iterated many times to ensure that key indicators such as battery safety, consistency, and energy density meet the predetermined standards. Therefore, the production of samples is abnormally frequent, and these pull lines have certain flexibility. Faced with a wide range of orders with different batch sizes, PTO technicians often encounter problems such as frequent cutting and pulling, order overdue, and low efficiency when manually scheduling production. It is urgent to develop an intelligent scheduling optimization model to achieve the goal of reducing the overall cutting and pulling time and alleviating the phenomenon of order overdue.

[0057] In order to solve the above technical problems, the present application embodiment provides a method for coordinated production scheduling of multiple production lines of battery packs, see Figure 1 As shown, the method for coordinated production scheduling of multiple production lines of battery packs in this embodiment includes step S100 and step S200.

[0058] In step S100, a multi-objective production optimization model is constructed according to preset objective constraints and preset mutually exclusive constraints.

[0059] In this embodiment, the product delay time of battery production may involve constraints on production cycle and delivery period, while the process switching time may involve equipment adjustment, cleaning time, etc. The product delay time and process switching time greatly affect the production efficiency and production cost of the product. The preset target constraints in this embodiment may include minimizing the product delay time and process switching time.

[0060] In step S200, an initial production schedule is determined according to the multi-objective production optimization model, and according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to optimize the production scheduling of the initial production schedule to obtain an actual production schedule.

[0061] In an embodiment of the present application, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the alternating iterative optimization mathematical model and heuristic algorithm, an initial production scheduling plan is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted using a heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain an intermediate production scheduling plan, and the intermediate production scheduling plan is repeatedly iterated as the input of the multi-objective production optimization model, so that the process switching time in the actual production scheduling plan is minimized, the product delay time is minimized, and the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0062] In an application embodiment, in a battery production system, a batch of sample orders in a certain area need to be produced. There are multiple production lines in the area. The production lines can use different equipment to complete the orders. Each order has n processes by default. You can choose any wire with corresponding production capacity to complete the production task. However, the production efficiency of different wires is different. Therefore, it is necessary to assign each order to each device and reasonably adjust the production sequence of the orders on each wire to minimize the overall cutting time. At the same time, since the sample order has a large process risk, the sample needs to adjust the machine test paper and wait for the interface process (a part of the sample flows through the subsequent process first) and other special requirements to meet the production constraints of the anode and cathode balance. In order to improve the equipment utilization and capacity utilization while meeting the delivery time of each order, in this embodiment, by setting the corresponding preset mutually exclusive constraints, the equipment level can be scheduled, the front and rear processes are uniformly scheduled, and the front process anode and cathode are scheduled simultaneously to construct a multi-objective production optimization model. Based on the multi-objective production optimization model, multiple production lines can produce multiple batches of battery samples at the same time, and the heuristic algorithm is used to optimize the production scheduling under the preset mutually exclusive constraints to obtain the actual scheduling plan to realize the customized process of multiple batches of battery samples.

[0063] In some embodiments, see Figure 2 As shown, the method for coordinated production scheduling of multiple production lines of battery packs in this embodiment also includes step S300.

[0064] In step S300, the actual production scheduling plan is dynamically adjusted according to the abnormal event, and the adjusted production scheduling plan is output.

[0065] In an embodiment of the present application, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. During the implementation of the actual production scheduling plan, production line failures, defective materials, temporary staff shortages and other production abnormalities may be encountered. The actual production scheduling plan can be dynamically adjusted based on the multi-objective optimization model according to the abnormal events, such as adjusting the order of related processes, and comprehensively considering the process switching time and product extension time, so that the project completion time is as close to its planned deadline as possible to reduce production delays.

[0066] In some embodiments, in the process of constructing a multi-objective production optimization model according to preset target constraints and preset mutually exclusive constraints, the multi-objective production optimization model can be obtained by using a mathematical model language to obtain a basic workshop scheduling model. For example, the preset mutually exclusive constraints include: a process of a batch of samples can be allocated to n (user-specified batch number) equipment, the total amount of n equipment allocated needs to be equal to the demand for the batch of samples, the time constraints of different processes (process route constraints), etc. can be solved through rapid modeling to obtain the required decision variable values.

[0067] In this embodiment, combined with Figure 3 As shown, in step S200, the heuristic algorithm includes an evolutionary optimization algorithm and a multi-objective optimization algorithm, and the best solution value is obtained by performing algorithm optimization through the evolutionary optimization algorithm and the multi-objective optimization algorithm. The evolutionary optimization algorithm can be a differential evolution algorithm, and the corresponding production scheduling plan is obtained by the evolutionary optimization algorithm, and a constraint violation matrix is ​​constructed under the corresponding preset mutually exclusive constraint conditions according to the mathematical constraint method and the heuristic deduction algorithm, and then the solution is obtained based on the multi-objective optimization algorithm, and the solution result is substituted into the multi-objective production optimization model, and then optimized again by the evolutionary optimization algorithm, and it is iterated repeatedly.

[0068] In some embodiments, the heuristic deduction algorithm is subject to constraints such as continuous production constraints of materials and tooling, equal interface process constraints, and equal interface quantity constraints, while the mathematical constraint method is subject to constraints such as basic workshop scheduling constraints, adjacent process time constraints, process restriction flow time constraints, capacity ramping constraints, continuous production constraints of previous and subsequent processes, and production resource release constraints.

[0069] In some embodiments, in step S200, the multi-objective production optimization model is first encoded according to the evolutionary optimization algorithm to generate an initial solution, and the decision variable values ​​of two batch samples-process-equipment allocation and batch samples-process-equipment-start time are obtained by decoding the initial solution, and then the remaining decision values ​​are derived by the heuristic rule method, such as the material continuous production constraint, and the project start time obtained by solving it is used to determine whether the continuous production of the material can be met. After all the decision values ​​are obtained, they are used to determine whether the preset mutually exclusive constraints are met, calculate the objective function penalty function, and according to the encoding and decoding method, the judgment constraint and the fitness calculation method, the evolutionary algorithm framework geatpy (genetic and evolutionary algorithm toolbox) is used to perform the optimal transformation of the population. In this way, different evolutionary algorithms and evolutionary methods can be used to test and optimize, obtain the final solution value, and output the corresponding actual production scheduling plan.

[0070] In some embodiments, in step S100, a multi-objective production optimization model is constructed according to preset target constraints and preset mutually exclusive constraints, including: according to the preset mutually exclusive constraints, the multi-objective production optimization model is established with minimizing product delay time and minimizing process switching time as preset target constraints.

[0071] In this embodiment, the product delay time is the difference between the actual completion time and the planned deadline. The larger the difference, the more serious the product delay, which will cause production delays. If the difference is 0 or less than 0, it means that the product can be delivered in time. The process switching time refers to the switching time between different production processes during the product production process. The longer the switching time, the lower the production efficiency, which has a greater impact on the production cost of the product. Therefore, in this embodiment, minimizing the product delay time and minimizing the process switching time are used as preset target constraints. Under the condition of meeting the preset mutually exclusive constraints, a multi-objective production optimization model is constructed, and the actual production scheduling plan is obtained by optimizing the production scheduling relationship. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0072] In some embodiments, the preset mutually exclusive constraint conditions include at least one of a production capacity constraint, a material synchronization constraint, and a production line equipment occupancy mutually exclusive constraint.

[0073] In this embodiment, the production capacity constraint indicates that the production volume of each process cannot exceed its maximum production capacity, and the production capacity of each device is also limited. The material synchronization constraint indicates that the material demand of each production process cannot exceed the total inventory of the materials required by the process. The production line equipment occupancy mutual exclusion constraint indicates that the same equipment cannot be occupied by multiple processes at the same time, and the start time of each process needs to meet the production sequence of the product. Within reasonable constraints, the initial production schedule is optimized according to the multi-objective production optimization model to obtain an intermediate production schedule, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0074] In some embodiments, the production capacity constraint includes: setting the processing time of product k in process i to be less than or equal to the maximum available working hours of process i; wherein k represents the category of the product, and i represents the category of the process.

[0075] In this embodiment, the production capacity constraint indicates that the production volume of each process cannot exceed its maximum production capacity, and the production capacity of each device is also limited. In each process i, the processing time of the corresponding product k in process i is less than or equal to the maximum available working time of process i. Product k represents the kth product, process i represents the i-th process, and Ti is the maximum available working time of the i-th process. The processing time of product k in process i can be defined as Σ(Tki)≤Ti, Tki is the processing time of product k in process i, and Ti is the maximum available working time of process i. Within reasonable production capacity constraints, the initial production schedule is optimized according to the multi-objective production optimization model to obtain an intermediate production schedule, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0076] In some embodiments, the material synchronization constraint includes: setting the material consumed by product k in process i to be less than the real-time material inventory of process i; wherein k represents the category of the product, and i represents the category of the process.

[0077] In this embodiment, the material synchronization constraint indicates that the material demand of each production process cannot exceed the total inventory of materials required by the process, and the total materials consumed by the kth product in process i need to be less than or equal to the real-time material inventory of process i, so as to avoid the production process of the kth product from being stopped due to lack of materials and affecting production efficiency through the material synchronization constraint. Under the condition of satisfying the material synchronization constraint, the initial production schedule is optimized according to the multi-objective production optimization model to obtain the intermediate production schedule, and the intermediate production schedule is used as the input of the multi-objective production optimization model for further iteration, and finally the production scheduling relationship is optimized to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0078] In some embodiments, in the material synchronization constraint, Σ(Qki×Pk)≤Qi can be set; Among them, Qki is the unit consumption of materials of process i by product k, Pk is the production quantity of product k, Qki×Pk is the total materials consumed by the kth product in process i, and Qi is the real-time material inventory of process i.

[0079] In some embodiments, the mutually exclusive constraint on production line equipment occupancy includes: setting the same production line to only be allocated to one target product process within the same time period.

[0080] In this embodiment, the mutually exclusive constraint of production line equipment occupancy indicates that the same production line cannot be occupied by multiple processes at the same time, and the start time of each process needs to meet the production order of the product, and for each product, the start time of the i+1th process is greater than or equal to the sum of the end time of the ith process and the process switching time. By setting the mutually exclusive constraint of production line equipment occupancy, the utilization efficiency of the production line can be improved, and conflicts in the use of the production line can be avoided. Under the production order specified for each product, the process time of each product is optimized, with minimizing the product delay time and minimizing the process switching time as preset target constraints. The initial production scheduling plan is optimized according to the multi-objective production optimization model to obtain the intermediate production scheduling plan, and the intermediate production scheduling plan is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0081] In some embodiments, the mutually exclusive constraint on production line equipment occupancy includes: setting the same equipment to be only allowed to be allocated to one target product process within the same time period.

[0082] In this embodiment, the mutually exclusive constraint of production line equipment occupancy means that the same equipment cannot be occupied by multiple processes at the same time, the start time of each process needs to meet the production order of the product, and, for each product, the start time of the i+1th process is greater than or equal to the sum of the end time of the ith process and the process switching time. By setting the mutually exclusive constraint of production line equipment occupancy, the utilization efficiency of the equipment can be improved and conflicts in the use of equipment can be avoided. Under the production order specified for each product, the process time of each product is optimized, with minimizing the product delay time and minimizing the process switching time as preset target constraints. The initial production scheduling plan is optimized according to the multi-objective production optimization model to obtain the intermediate production scheduling plan, and the intermediate production scheduling plan is used as the input of the multi-objective production optimization model for further iteration. Finally, the production scheduling relationship is optimized to obtain the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0083] In some embodiments, in step S200, determining an initial production scheduling plan according to the multi-objective production optimization model includes: solving the multi-objective production optimization model according to historical production scheduling data and a linear programming solver to determine the initial production scheduling plan.

[0084] In this embodiment, historical production scheduling data is obtained, and a linear programming solver is used to solve a multi-objective production optimization model based on the historical production scheduling data to obtain an initial production scheduling plan. In the initial production scheduling plan, the production arrangement of each product meets the preset mutually exclusive constraints, and when the preset mutually exclusive constraints are met, the production scheduling relationship is optimized according to the multi-objective production optimization model to obtain an intermediate production scheduling plan, and the intermediate production scheduling plan is continuously iterated as the input of the multi-objective production optimization model, and finally the production scheduling relationship is optimized to obtain the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model according to abnormal events in real time, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0085] In some embodiments, in step S200, the production scheduling of the initial production schedule is optimized by alternating iterative optimization mathematical models and heuristic algorithms according to the preset mutually exclusive constraints to obtain the actual production schedule, including: locally optimizing the initial production schedule through a genetic algorithm or a simulated annealing algorithm, and using a multi-objective Pareto front solution set to screen the optimal solution.

[0086] In this embodiment, the production line allocation sequence can be encoded by a genetic algorithm, and its fitness function includes capacity constraints and switching costs. For example, the NSGA-II framework is used to process multiple objectives (multiple objectives include minimizing product delay time and minimizing process switching time) in combination with constraint conditions. The simulated annealing algorithm can explore neighborhood solutions at a fixed temperature, dynamically adjust the production sequence to reduce switching time, and overcome problems such as equipment failure response and material changes. The initial production schedule is locally optimized by a genetic algorithm or a simulated annealing algorithm, and in the case of meeting the preset mutually exclusive constraints, the optimal solution is screened by using a multi-objective Pareto frontier solution set with minimizing product delay time and minimizing process switching time as preset target constraints to obtain the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted in real time based on abnormal events based on a multi-objective optimization model, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0087] In some embodiments, in step S200, the production scheduling of the initial production schedule is optimized by using an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraints to obtain an actual production schedule, including: using an alternating iterative optimization mathematical model and a heuristic algorithm to generate multiple actual production schedules with minimizing product delay time and minimizing process switching time as preset target constraints.

[0088] In this embodiment, the initial production schedule is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted using a heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain an intermediate production schedule, and the intermediate production schedule is repeatedly iterated as the input of the multi-objective production optimization model. In the case of meeting the preset mutually exclusive constraints, the preset objective constraints are minimized, the multi-objective Pareto front solution set is used to screen the optimal solution to select a suitable solution from multiple production schedules as the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0089] In some embodiments, in step S200, the production scheduling of the initial production schedule is optimized by adopting an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraints to obtain an actual production schedule, and also includes: when the number of alternating iterations reaches a set number of iterations, or the difference between the minimized product delay time and the minimized process switching time in a consecutive preset number of iterations is less than a preset threshold, the iteration is stopped.

[0090] In this embodiment, the initial production scheduling plan is first determined according to the multi-objective production optimization model, and then the heuristic algorithm is used to adjust the process sequence to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain the intermediate production scheduling plan, and the intermediate production scheduling plan is repeatedly iterated as the input of the multi-objective production optimization model. When the number of alternating iterations reaches the set number of iterations, or the difference between the minimized product delay time and the minimized process switching time in the consecutive preset number of iterations is less than the preset threshold, the iteration is stopped.

[0091] For example, solutions that improve product delay time by more than 5% are prioritized, and solutions that reduce process switching time by more than 10% are secondarily selected. The multi-objective Pareto frontier solution set is used to screen the optimal solution and select a suitable solution from multiple production scheduling plans as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time according to abnormal events based on the multi-objective optimization model, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0092] In some embodiments, the number of iterations can be set to 5, 10 or 20 times, and can also be determined by the cumulative number of processes of multiple target products. For example, the number of iterations can be set to 2, 4 or 10 times the cumulative number of processes of multiple target products.

[0093] In some embodiments, the preset threshold may be 1% of the product delay time and process switching time in the initial production scheduling plan.

[0094] In some embodiments, in step S300, the actual production schedule is dynamically adjusted according to abnormal events, and the adjusted production schedule is output, including: triggering production schedule re-planning according to material inventory warnings and abnormal equipment shutdown events, and updating process priority queues.

[0095] In this embodiment, abnormal events include material inventory warnings and abnormal equipment downtime events. During the implementation of the actual production scheduling plan, the production scheduling plan can be dynamically re-planned according to events such as material shortages, equipment failures, and order changes, and the input parameters of the multi-objective production optimization model can be adjusted. The process priority queue is updated, and then repeated iterations are performed again. The multi-objective Pareto front solution set is used to screen the optimal solution and select a suitable solution from multiple production scheduling plans as the final actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time based on the multi-objective optimization model according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0096] In some embodiments, in step 100, the multi-objective production optimization model can be adjusted according to the weight coefficient of the project delay time and the weight coefficient of the process switching time.

[0097] In this embodiment, the initial production schedule is first determined according to the multi-objective production optimization model, and then the process sequence is adjusted by the heuristic algorithm to reduce the switching time, and then the multi-objective production optimization model is returned to process the optimization of the product delay time, so as to obtain the intermediate production schedule, and the intermediate production schedule is repeatedly iterated as the input of the multi-objective production optimization model. After each iteration, the constraint conditions, the weight coefficient of the project delay time or the weight coefficient of the process switching time are updated, so that the genetic algorithm or the simulated annealing algorithm can complement each other, and in the case of meeting the preset mutually exclusive constraint conditions, the product delay time and the process switching time are minimized as the preset target constraints, and the multi-objective Pareto front solution set is used to select the optimal solution from multiple production schedules to select a suitable solution as the final actual production schedule. In the actual production schedule, the actual production schedule is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close as possible to its planned deadline, reducing production delays.

[0098] In some embodiments, in step 100, the objective function of the multi-objective production optimization model is defined as: ; Among them, Z is the minimum product delay time and the minimum process switching time, is the weight coefficient of project delay time, is the weight coefficient of process switching time.

[0099] In some embodiments, in step 100, the multi-objective production optimization model is adjusted according to the priority weight coefficient of the product.

[0100] In this embodiment, the product delay time is the difference between the actual completion time and the planned deadline. The larger the difference, the more serious the product delay, which will cause production delays. If the difference is 0 or less than 0, it means that the product can be delivered in time. The process switching time represents the switching time between different production processes during the production process of the product. The longer the switching time, the lower the production efficiency, which has a greater impact on the production cost of the product. In the multi-objective production optimization model, the difference between the actual completion time and the planned deadline is compared with 0, and its maximum value is selected as a parameter. The product delay time is calculated in combination with the priority weight coefficient of the corresponding product. Finally, the weight coefficient of the product delay time and the weight coefficient of the process switching time are combined. Under the condition of meeting the preset mutually exclusive constraints, the objective function of the multi-objective production optimization model is solved, and the actual production scheduling plan is obtained by optimizing the production scheduling relationship. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, reducing production delays.

[0101] In some embodiments, the objective function of the multi-objective production optimization model is defined as: min Z =w1·Σ(Wi·max(0, Ci - Di)) + w2·Σ(Tij·Xij); Among them, Wi represents the priority weight of product i, Ci is the actual completion time, Di is the delivery deadline, Tij represents the time it takes for process i to switch to production line j, and Xij is the statistical variable of the number of switching times.

[0102] In this embodiment, in the multi-objective production optimization model, the difference between the actual completion time and the planned deadline is compared with 0, and its maximum value is selected as a parameter. The product delay time is calculated in combination with the priority weight coefficient Wi of the corresponding product i. Finally, the weight coefficient w1 of the product delay time and the weight coefficient w2 of the process switching time are combined. Under the condition of meeting the preset mutually exclusive constraints, the objective function of the multi-objective production optimization model is solved, and the actual production scheduling plan is obtained by optimizing the production scheduling relationship. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time based on abnormal events based on the multi-objective optimization model, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0103] In some embodiments, in step 200, the production scheduling of the initial production schedule is optimized by alternating iterative optimization of mathematical models and heuristic algorithms according to the preset mutually exclusive constraints to obtain the actual production schedule, including: using a heuristic algorithm to locally adjust the process sequence of multiple objectives to reduce the process switching time.

[0104] In this embodiment, a multi-objective production optimization model is first constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model to reduce the process switching time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted in real time based on abnormal events based on the multi-objective optimization model, so that the project completion time is as close as possible to its planned deadline, thereby reducing production delays.

[0105] In some embodiments, in step 200, the production scheduling of the initial production schedule is optimized by alternating iterative optimization mathematical models and heuristic algorithms according to the preset mutually exclusive constraints to obtain the actual production schedule, including: using matrix storage to calculate the switching time of adjacent processes, and adjusting the process sequence of the initial production schedule according to the basic switching time of adjacent processes, process difference coefficient, and equipment cleaning time.

[0106] In this embodiment, product k, product k+1, product K+2, etc. are scheduled for production on different production lines, which may share some processes. A production line-product association matrix is ​​established using a matrix storage method, that is, the mapping relationship between products and processes is described through a process route matrix, such as product k needs to go through process i→process i+3→process i+4 to complete production. Check whether all preset mutually exclusive constraints are covered. The preset mutually exclusive constraints may include equipment occupancy time, material inventory, and whether the complex scheduling requirements of multiple products and multiple production lines are met. The process sequence of the initial production scheduling plan is adjusted according to the basic switching time of adjacent processes, process difference coefficient, and equipment cleaning time. The product processes of multiple target products in the multi-objective production optimization model are adjusted to reduce the switching time tij from process i to j, thereby optimizing the production scheduling relationship to obtain the actual production scheduling plan, so that the project completion time is as close as possible to its planned deadline, and production delays are reduced.

[0107] In some embodiments, the switching time tij from process i to j is defined in a matrix storage manner to satisfy: tij =ti1×xi+ti2; wherein ti1 is the basic switching time, xi is the process difference coefficient, ti2 is the cleaning time of process i, and xi is determined based on the compatibility of the electrolyte types of the previous and subsequent processes.

[0108] In this embodiment, a production line-product association matrix is ​​established by using a matrix storage method, that is, the mapping relationship between products and processes is described by a process route matrix, such as product k needs to go through process i→process i+3→process i+4 to complete production. During the switching process from process i to process j, the switching time tij from process i to process j is calculated based on the basic switching time ti1 from process i to process j, combined with the process difference coefficient xi from process i to process j, and the cleaning time ti2 of process i, and it is checked whether all preset mutually exclusive constraints are covered. According to the preset mutually exclusive constraints, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm, and the switching time tij from process i to j is reduced, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan, so that the project completion time is as close as possible to its planned deadline, and production delays are reduced.

[0109] In some embodiments, in step 300, the actual production schedule is dynamically adjusted according to abnormal events, and the adjusted production schedule is output, including: triggering the production schedule to be re-planned according to material inventory warnings and abnormal equipment shutdown events, and updating the process priority queue.

[0110] In this embodiment, during the implementation of the actual production schedule, abnormal production events such as production line failures, defective materials, and temporary staff shortages may be encountered. The actual production schedule can be dynamically adjusted based on the multi-objective optimization model according to the abnormal events. For example, the production schedule can be re-planned based on material inventory warnings and abnormal equipment shutdown events, and the process priority queue can be updated to generate a corresponding adjusted production schedule. The corresponding adjusted production schedule of the embodiment can be flexibly selected to make the project completion time as close to its planned deadline as possible to reduce production delays.

[0111] In some embodiments, in step 300, the actual production schedule is dynamically adjusted according to the abnormal event, and the adjusted production schedule is output, including: when it is monitored that the material inventory is lower than the safety threshold, the process schedule that depends on the material is automatically frozen.

[0112] In this embodiment, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production schedule. In the actual production schedule, when it is monitored that the material inventory is lower than the safety threshold, the process schedule that depends on the material is automatically frozen, and a feasible solution can be quickly generated based on the multi-objective optimization model and the heuristic algorithm, so that the actual production schedule is dynamically adjusted, and the real-time response capability of the dynamic adjustment is improved, so that the project completion time is as close as possible to its planned deadline, and production delays are reduced.

[0113] In some embodiments, in step 300, the actual production schedule is dynamically adjusted according to the abnormal event, and the adjusted production schedule is output, including: when the duration of the production line equipment failure exceeds the preset tolerance, the pending processes of the production line are reallocated to an alternative production line.

[0114] In this embodiment, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, when the duration of the production line equipment failure exceeds the preset tolerance, the key bottleneck process (i.e., the process whose duration of the production line equipment failure exceeds the preset tolerance) is identified based on the multi-objective optimization model and the heuristic algorithm, and then the neighborhood operation is applied to make adjustments, evaluate the reduction of the process switching time, use dynamic weights and target priorities for multi-objective optimization, reallocate the pending processes of the production line to the alternative production line, and dynamically adjust the actual production scheduling plan to obtain an adjusted production scheduling plan, improve the real-time response capability of the dynamic adjustment, and make the project completion time as close as possible to its planned deadline, thereby reducing production delays.

[0115] In some embodiments, in step 300, the actual production schedule is dynamically adjusted according to abnormal events, and an adjusted production schedule is output, including: when a new product order is inserted, an order is inserted in the current production schedule gap based on a multi-objective optimization model and a heuristic algorithm to obtain the adjusted production schedule.

[0116] In this embodiment, the product processes of multiple target products in the multi-objective production optimization model are adjusted by using an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and product delay time, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, when a new product order is inserted, an order is inserted in the current production scheduling gap based on a multi-objective optimization model and a heuristic algorithm to obtain the adjusted production scheduling plan, thereby improving the real-time response capability of dynamic adjustment, making the project completion time as close as possible to its planned deadline, and reducing production delays.

[0117] In some embodiments, see Figure 2 As shown, the method for coordinated production scheduling of multiple production lines of battery packs in this embodiment also includes step S400.

[0118] In step S400, the key nodes and progress of the actual production scheduling plan are visualized.

[0119] In this embodiment, the product processes of multiple target products in the multi-objective production optimization model are adjusted by means of an alternating iterative optimization mathematical model and a heuristic algorithm according to preset mutually exclusive constraints, so as to reduce the process switching time and the product delay time, thereby optimizing the production scheduling relationship and obtaining the actual production schedule. During the execution of the actual production schedule, the key nodes and progress of the actual production schedule are visualized, and the capacity utilization rate of each production line in the next 72 hours is displayed through a color gradient. The consumption rate and replenishment nodes for the next 5 days are displayed by material category, and the bottleneck processes that affect the overall delivery are prompted with a flashing red prompt, which reminds the user of the current production schedule progress in real time and issues early warnings for related nodes.

[0120] An embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0121] An embodiment of the present application also provides a battery production scheduling system, including a memory, a processor, and a computer program stored in the memory, and when the processor executes the program, the steps of the method described in any of the above embodiments are implemented.

[0122] In the technical solution of the embodiment of the present application, relevant computer programs and historical production scheduling data are stored on the memory. When the program is executed by the processor, the steps of the method described in any of the above embodiments are implemented. First, a multi-objective production optimization model is constructed according to preset target constraints and preset mutually exclusive constraints, and an initial production scheduling plan is determined according to the multi-objective production optimization model. Then, according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to adjust the product processes of multiple target products in the multi-objective production optimization model, thereby optimizing the production scheduling relationship to obtain an actual production scheduling plan. In the actual production scheduling plan, the actual production scheduling plan is dynamically adjusted based on the multi-objective optimization model in real time according to abnormal events, so that the project completion time is as close to its planned deadline as possible, thereby reducing production delays.

[0123] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0124] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0125] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0128] 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 make equivalent replacements for some of the technical features therein. 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 application, and should all be included in the protection scope of the present application.

Claims

1. A method for collaborative production scheduling of multiple production lines of battery packs, characterized in that: The battery pack multi-production line collaborative production scheduling method comprises: Construct a multi-objective production optimization model according to preset objective constraints and preset mutually exclusive constraints; An initial production schedule is determined according to the multi-objective production optimization model, and according to the preset mutually exclusive constraints, an alternating iterative optimization mathematical model and a heuristic algorithm are used to optimize the production scheduling of the initial production schedule to obtain an actual production schedule.

2. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 1, characterized in that: The battery pack multi-production line collaborative production scheduling method further includes: The actual production schedule is dynamically adjusted according to the abnormal event, and the adjusted production schedule is output.

3. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 1, characterized in that: Construct a multi-objective production optimization model according to preset objective constraints and preset mutually exclusive constraints, including: The multi-objective production optimization model is established according to the preset mutually exclusive constraints with minimizing product delay time and minimizing process switching time as preset target constraints.

4. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 3, characterized in that: The preset mutually exclusive constraint conditions include at least one of a production capacity constraint, a material synchronization constraint, and a production line equipment occupancy mutually exclusive constraint.

5. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 4, characterized in that: The production capacity constraints include: Set the processing time of product k in process i to be less than or equal to the maximum available working time of process i; where k represents the category of the product and i represents the category of the process.

6. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 4, characterized in that: The material synchronization constraints include: Set the material consumed by product k in process i to be less than the real-time material inventory of process i; where k represents the category of the product and i represents the category of the process.

7. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 4, characterized in that: The mutually exclusive constraints on the production line equipment occupancy include: Set the same production line to only be assigned to one target product process within the same time period; and / or The same equipment can only be assigned to one target product process in the same time period.

8. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 1, characterized in that: Determining an initial production scheduling plan according to the multi-objective production optimization model includes: The multi-objective production optimization model is solved according to historical production scheduling data and a linear programming solver to determine the initial production scheduling plan.

9. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 1, characterized in that: According to the preset mutually exclusive constraint conditions, the production scheduling of the initial production scheduling plan is optimized by using an alternating iterative optimization mathematical model and a heuristic algorithm to obtain an actual production scheduling plan, including: The initial production scheduling plan is locally optimized by using a genetic algorithm or a simulated annealing algorithm, and a multi-objective Pareto front solution set is used to screen the optimal solution.

10. The method for coordinated production scheduling of multiple production lines of battery packs according to claim 1, characterized in that: According to the preset mutually exclusive constraint conditions, the production scheduling of the initial production scheduling plan is optimized by using an alternating iterative optimization mathematical model and a heuristic algorithm to obtain an actual production scheduling plan, including: By adopting the method of alternating iterative optimization mathematical model and heuristic algorithm, multiple actual production scheduling plans are generated with minimizing product delay time and minimizing process switching time as preset target constraints.

11. The method for coordinated production scheduling of multiple production lines of battery packs according to claim 10, characterized in that: According to the preset mutually exclusive constraint conditions, the production scheduling of the initial production scheduling plan is optimized by using an alternating iterative optimization mathematical model and a heuristic algorithm to obtain an actual production scheduling plan, which also includes: When the number of alternating iterations reaches the set number of iterations, or the difference between the minimized product delay time and the minimized process switching time in the consecutive preset number of iterations is less than the preset threshold, the iteration is stopped.

12. The method for coordinated production scheduling of multiple production lines of battery packs according to claim 1, characterized in that: The multi-objective production optimization model is constructed according to the preset objective constraints and preset mutually exclusive constraints, including: The multi-objective production optimization model is adjusted according to the weight coefficient of the project delay time and the weight coefficient of the process switching time.

13. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 12, characterized in that: The multi-objective production optimization model is constructed according to the preset objective constraints and the preset mutually exclusive constraints, and further includes: The multi-objective production optimization model is adjusted according to the priority weight coefficients of the products.

14. The method for coordinated production scheduling of multiple production lines of battery packs according to claim 1, characterized in that: The method of optimizing the production scheduling of the initial production scheduling plan by using an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraint conditions to obtain an actual production scheduling plan includes: A heuristic algorithm is used to locally adjust the process sequence of multiple objectives to reduce the process switching time.

15. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 1, characterized in that: The method of optimizing the production scheduling of the initial production scheduling plan by using an alternating iterative optimization mathematical model and a heuristic algorithm according to the preset mutually exclusive constraint conditions to obtain an actual production scheduling plan includes: The switching time of adjacent processes is calculated by adopting a matrix storage method, and the process sequence of the initial production scheduling plan is adjusted according to the basic switching time of adjacent processes, the process difference coefficient, and the equipment cleaning time.

16. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 2, characterized in that: The actual production scheduling plan is dynamically adjusted according to the abnormal event, and the adjusted production scheduling plan is output, including: The production schedule is re-planned based on material inventory warnings and abnormal equipment shutdown events, and the process priority queue is updated.

17. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 2, characterized in that: The dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When it is detected that the material inventory is lower than the safety threshold, the process schedule that depends on the material will be automatically frozen.

18. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 2, characterized in that: The dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When the duration of a production line equipment failure exceeds the preset tolerance, the pending processes of the production line are reallocated to an alternative production line.

19. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 2, characterized in that: The dynamically adjusting the actual production scheduling plan according to the abnormal event and outputting the adjusted production scheduling plan includes: When a new product order is inserted, the order is inserted in the current production scheduling gap based on a multi-objective optimization model and a heuristic algorithm to obtain the adjusted production scheduling plan.

20. The method for coordinated production scheduling of multiple production lines of battery packs as claimed in claim 2, characterized in that: The collaborative production scheduling method for multiple production lines of battery packs also includes: The key nodes and progress of the actual production scheduling plan are visually displayed.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 20 are implemented.

22. A battery production scheduling system, comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 20 are implemented.

Images