Production scheduling method, device, medium and program based on differential evolution and taboo search

Abstract

The embodiment of the invention discloses a production scheduling method and device based on differential evolution and tabu search, a medium and a program. The method comprises the steps of randomly generating an initial production plan population of a target production task according to a production scheduling optimization model of the target production task; optimizing the production sequence of each production plan individual in the initial production plan population; optimizing the production quantity of the production plan individuals to obtain a current production plan population; optimizing the production sequence of newly added production plan individuals in the current production plan population; screening a current optimal production plan individual from the optimized current production plan population; returning to execute the operation of optimizing the production quantity of the production plan individuals until it is determined that the current optimal production plan individual remains unchanged; and taking the production plan corresponding to the current optimal production plan individual as a target production plan of the target production task. The technical scheme provided by the embodiment of the invention is suitable for large-scale discrete production scheduling optimization.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of production scheduling, and in particular, to a production scheduling method, device, electronic device, storage medium, and program based on differential evolution and tabu search. Background Art

[0002] In recent years, with the gradual development of the manufacturing industry towards intelligence and unmanned operation, production scheduling plays an increasingly important role in enterprise management. Optimizing production scheduling can not only improve the production efficiency and profits of enterprises, but also play a key role in ensuring the smooth implementation of production plans, optimizing resource allocation, and reducing uncertainties and resource waste in the production process. Discrete manufacturing is one of the mainstream types of modern manufacturing, characterized by products usually consisting of multiple independent components, and the production process has obvious divisibility and stages. Discrete manufacturing is applied in many industries such as cigarette production, lithium battery manufacturing, food production, vehicle assembly, electronic product manufacturing, and mechanical equipment manufacturing.

[0003] Existing production scheduling methods are mainly divided into manual-based production scheduling, traditional algorithm-based production scheduling, and heuristic algorithm-based production scheduling.

[0004] In the process of implementing the present invention, the inventors found that the prior art has the following defects: The manual-based production scheduling method has problems such as waste of manpower, unstable solution quality, and inability to handle large-scale complex scenarios; the traditional algorithm-based production scheduling method has problems such as poor algorithm generality, complex modeling process, high computational requirements, and easy to fall into local optima; the heuristic algorithm-based production scheduling method has problems such as slow algorithm convergence speed and easy to ignore potential optimal solutions. Summary of the Invention

[0005] The embodiments of the present invention provide a production scheduling method, device, electronic device, storage medium, and program based on differential evolution and tabu search, which can improve the global search ability and convergence speed of the production scheduling algorithm, and improve the scientificity and accuracy of production scheduling, with strong generality, meeting the computational requirements of large-scale complex scenarios, low computational requirements, and being particularly suitable for large-scale discrete production scheduling optimization.

[0006] According to one aspect of the present invention, there is provided a production scheduling method based on differential evolution and tabu search, including:

[0007] Randomly generating an initial production plan population of the target production task according to the production scheduling optimization model of the target production task;

[0008] Optimizing the production order of each production plan individual in the initial production plan population;

[0009] Optimize the production quantity of the production plan individuals to obtain the current production plan population;

[0010] Optimize the production order of the newly added production plan individuals in the current production plan population;

[0011] Screen the current optimal production plan individual from the optimized current production plan population;

[0012] Return to perform the operation of optimizing the production quantity of the production plan individuals until it is determined that the current optimal production plan individual remains unchanged;

[0013] Take the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

[0014] According to another aspect of the present invention, there is provided a production scheduling device based on differential evolution and tabu search, including:

[0015] An initial production plan population generation module, configured to randomly generate an initial production plan population of the target production task according to the production scheduling optimization model of the target production task;

[0016] A first production order optimization module, configured to optimize the production order of each production plan individual in the initial production plan population;

[0017] A production quantity optimization module, configured to optimize the production quantity of the production plan individuals to obtain the current production plan population;

[0018] A second production order optimization module, configured to optimize the production order of the newly added production plan individuals in the current production plan population;

[0019] A current optimal production plan individual screening module, configured to screen the current optimal production plan individual from the optimized current production plan population;

[0020] An iterative calculation module, configured to return to perform the operation of optimizing the production quantity of the production plan individuals until it is determined that the current optimal production plan individual remains unchanged;

[0021] A target production plan determination module, configured to take the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

[0022] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0023] At least one processor; and

[0024] A memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores a computer program executable by the at least one processor. When executed by the at least one processor, the computer program enables the at least one processor to execute the production scheduling method based on differential evolution and tabu search according to any embodiment of the present invention.

[0026] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the production scheduling method based on differential evolution and tabu search according to any embodiment of the present invention when executed.

[0027] According to another aspect of the present invention, there is also provided a computer program product including a computer program which implements the production scheduling method based on differential evolution and tabu search according to any embodiment of the present invention when executed by a processor.

[0028] In an embodiment of the present invention, after randomly generating an initial production plan population of a target production task according to a production scheduling optimization model of the target production task, the production order of each production plan individual in the initial production plan population is optimized, and the production quantity of the production plan individual is optimized to obtain the current production plan population. After the optimization of the production quantity is completed, the production order of the newly added production plan individuals in the current production plan population can be optimized, and then the current optimal production plan individual is screened from the optimized current production plan population to complete the production scheduling optimization of the current round. Further, the operation of optimizing the production quantity of the production plan individual is returned to be executed until it is determined that the current optimal production plan individual remains unchanged, the iterative process is completed, and the production plan corresponding to the current optimal production plan individual obtained by iterative calculation is used as the target production plan of the target production task. The above technical solution can solve problems existing in the existing production scheduling methods, such as wasting manpower, unstable solution quality, poor algorithm generality, complex modeling process, high computing requirements, being easily trapped in local optima, slow algorithm convergence speed, and easily ignoring potential optimal solutions. It can improve the global search ability and convergence speed of the production scheduling algorithm, and improve the scientificity and accuracy of production scheduling. It has strong generality, meets the computing requirements of large-scale complex scenarios, has low computing requirements, and is particularly suitable for large-scale discrete production scheduling optimization.

[0029] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0031] Figure 1 It is a flowchart of a production scheduling method based on differential evolution and tabu search provided in the first embodiment of the present invention;

[0032] Figure 2 It is a schematic flowchart of applying the differential evolution algorithm and the tabu search algorithm in a production scheduling task provided in the first embodiment of the present invention;

[0033] Figure 3 It is a flowchart of a production scheduling method based on differential evolution and tabu search provided in the second embodiment of the present invention;

[0034] Figure 4 It is a flowchart of a production scheduling method based on differential evolution and tabu search provided in the third embodiment of the present invention;

[0035] Figure 5 It is a schematic diagram of a production scheduling device based on differential evolution and tabu search provided in the fourth embodiment of the present invention;

[0036] Figure 6 It is a schematic structural diagram of an electronic device provided in the fifth embodiment of the present invention. Detailed implementation manners

[0037] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first", "second", "third", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0039] Embodiment 1

[0040] Figure 1 It is a flowchart of a production scheduling method based on differential evolution and tabu search provided by Embodiment 1 of the present invention. Figure 2 It is a schematic flow diagram of applying a differential evolution algorithm and a tabu search algorithm in a production scheduling task provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of automatically scheduling production tasks by combining a differential evolution algorithm and a tabu search algorithm at the same time. This method can be executed by a production scheduling device based on differential evolution and tabu search. This device can be implemented in a software and / or hardware manner and is generally integrated in an electronic device. The electronic device can be a terminal device or a server device, as long as it can execute the production scheduling method based on differential evolution and tabu search. The embodiments of the present invention do not limit the specific device type of the electronic device. Correspondingly, as Figure 1 shown, the method includes the following operations:

[0041] S110. Randomly generate an initial production plan population of the target production task according to the production scheduling optimization model of the target production task.

[0042] Among them, the target production task can be any type of task that requires the production line to produce products. Exemplarily, the types of target production tasks can include, but are not limited to, cigarette production tasks, lithium battery manufacturing production tasks, food production tasks, vehicle assembly production tasks, electronic product production tasks, and mechanical equipment production tasks, etc. The embodiments of the present invention do not limit the task types of the target production tasks. The production scheduling optimization model can be constructed for the target production task and is used to characterize the production scheduling optimization requirements and production scheduling constraint factors of the target production task. The initial production plan population can be initially generated for the target production task and includes a population composed of multiple production plans for realizing the target production task. Each individual in the initial production plan population can be called a production plan individual, and each production plan individual represents a production plan configured for the target production task. The so-called production plan can be understood as a plan for overall arrangement and resource allocation of the production activities of the target production task, including information such as the production lines configured for the target production task and the specific production processes of each production line for the products.

[0043] The production scheduling problem refers to the problem of determining the execution order of production tasks and the resource allocation plan according to production requirements and the constraint conditions of production resources during the production process. The production scheduling problem has complexity and uncertainty, so efficient optimization algorithms need to be adopted to solve it. It can be understood that the target production task can be completed through at least one production line, and each production line can produce at least one product. When a production line produces different products, there is a switching time between different products for production. When optimizing the production scheduling of the target production task, optimization can be carried out respectively from two aspects: the production order of products in the production line and the production quantity of products in each production line.

[0044] In the embodiments of the present invention, during the entire process of optimizing the production scheduling of the target production task, as Figure 2 shown, the Tabu Search Algorithm (TS) can be used to optimize the production order of each production line of the target production task, and the Differential Evolution Algorithm (DE) can be used to optimize the production quantity of products in all production lines.

[0045] Among them, the tabu search algorithm is a meta-heuristic random search algorithm. It starts from an initial feasible solution, selects a series of specific search directions (moves) as trials, and selects the move that can achieve the most change in the specific objective function value. To avoid falling into local optimal solutions, a flexible "memory" technique is adopted in the tabu search algorithm to record and select the optimized process that has been carried out to guide the next search direction. The differential evolution algorithm is an efficient global optimization algorithm. It is also a population-based heuristic search algorithm. Each individual in the population corresponds to a solution vector. Its core idea is to gradually find the optimal solution in the search space by simulating the mutation, crossover, and selection operations in the biological evolution process.

[0046] Since the differential evolution algorithm performs data analysis and processing based on population data. Therefore, before using the differential evolution algorithm for production scheduling of the target production task, the production scheduling optimization model adapted to the target production task can be configured first according to the specific production requirements and specific production scenarios of the target production task, so as to characterize the specific production scheduling optimization requirements and production scheduling constraint factors of the target production task through the production scheduling optimization model, thereby abstracting the entity production task into an information data object. After establishing the production scheduling optimization model adapted to the target production task, based on the production scheduling optimization model of the target production task, a certain number of production plan individuals can be randomly selected from multiple production plan individuals that can complete the target production task in the initial state to form an initial production plan population.

[0047] S120. Optimize the production order of each production plan individual in the initial production plan population.

[0048] It can be understood that the initial production plan population is composed of multiple production plan individuals. Each production plan individual corresponds to a specific production plan. Each production plan can be completed by at least one production line for the corresponding production task. Therefore, as Figure 2 shown, after generating the initial production plan population of the target production task, the tabu search algorithm can be first used to initially optimize the production order of each production plan individual in the initial production plan population. The tabu search algorithm can avoid the arrangement of adjacent production tasks by setting a tabu list, thereby avoiding bad production task conflicts and improving the scientificity and rationality of the production order in each production plan individual in the initial production plan population.

[0049] S130. Optimize the production quantity of the production plan individuals to obtain the current production plan population.

[0050] Among them, the current production plan population can be an optimized production plan population obtained by using the differential evolution algorithm to optimize the production quantity of the production plan individuals in the population. The production plan population includes multiple production plan individuals.

[0051] After optimizing the production sequence of each production plan individual in the initial production plan population, optionally, the differential evolution algorithm can be further used to optimize the production quantity of each production plan individual in the population. The production plan individuals with optimized production quantities form a new current production plan population.

[0052] Specifically, the differential evolution algorithm can perform a mutation operation on the initial production plan population, that is, perform differential and recombination on the production plan individuals in the initial production plan population to generate new individuals, and then perform a selection operation, that is, select beneficial mutations by comparing the production plan individuals in the population. Furthermore, check the termination condition of the algorithm. If the optimal production plan individual selected from the generated production plan individuals remains stable, the termination condition is met and the algorithm stops; otherwise, execute the iterative calculation process and continue with the mutation and selection operations until it is determined that the algorithm meets the termination condition, completing the optimization process of the production quantity of the production plan individuals.

[0053] It can be seen that the differential evolution algorithm can gradually find the optimal production scheduling plan by performing mutation and selection operations on the production plan individuals in the production plan population, and has the advantages of strong global search ability, strong adaptability, few parameters, simple implementation, and efficient handling of multi-objective optimization problems. It can effectively handle the complex constraint conditions in the production process of the target production task, thereby improving the production efficiency and resource utilization rate of the target production task.

[0054] S140. Optimize the production sequence of the newly added production plan individuals in the current production plan population.

[0055] Among them, the newly added production plan individuals can be the production plan individuals newly generated by the differential evolution algorithm.

[0056] To further ensure the scientificity and rationality of the production sequence, as Figure 2 shown, after optimizing the production quantity of the production plan individuals in the production plan population, for each production line with a fixed production quantity, optionally, the tabu search algorithm can be used again to optimize the production sequence of each production line for each product in each production plan individual. Specifically, the tabu search algorithm can be used to optimize the production sequence of the newly added production plan individuals in the current production plan population.

[0057] S150. Screen the current optimal production plan individual from the optimized current production plan population.

[0058] Among them, the current optimal production plan individual can be the optimal production plan individual selected from the current production plan population in the current round, and this optimal production plan individual is the optimal production scheduling plan determined for the target production task in the current round.

[0059] S160. Determine whether the currently optimal production plan individual remains unchanged. If so, execute S170; otherwise, return to execute S130.

[0060] It can be understood that the currently optimal production plan individuals selected in each round may be different, that is, during the algorithm iteration process, the determined optimal production plan individual will change. As the algorithm is continuously iterated and updated, the determined currently optimal production plan individual will gradually tend to be stable. When the currently optimal production plan individual remains unchanged, it can be determined that the algorithm has determined the optimal production plan individual applicable to the target production task after multiple rounds of calculation.

[0061] Optionally, determining that the currently optimal production plan individual remains unchanged can mean that the currently optimal production plan individuals selected in multiple rounds are the same, that is, the currently optimal production plan individual remains unchanged for multiple rounds.

[0062] S170. Use the production plan corresponding to the currently optimal production plan individual as the target production plan for the target production task.

[0063] Among them, the target production plan can be the production plan finally determined for the target production task and is the optimal production scheduling scheme applicable to the target production task.

[0064] Correspondingly, when the algorithm completes the iteration process and the currently optimal production plan individuals selected in multiple consecutive rounds remain unchanged, the production plan corresponding to the currently optimal production plan individual that remains unchanged in multiple consecutive rounds can be used as the target production plan for the target production task, and the production scheduling optimization of the target production task is completed.

[0065] The embodiment of the present invention randomly generates an initial production plan population of the target production task according to the production scheduling optimization model of the target production task, optimizes the production sequence of each production plan individual in the initial production plan population, and optimizes the production quantity of the production plan individual to obtain the current production plan population. After the production quantity optimization is completed, the production sequence of the newly added production plan individuals in the current production plan population can be optimized, and then the current optimal production plan individual can be selected from the optimized current production plan population to complete the current round of production scheduling optimization. Further, return to perform the operation of optimizing the production quantity of the production plan individual until it is determined that the current optimal production plan individual remains unchanged, complete the iterative process, and use the production plan corresponding to the iteratively calculated current optimal production plan individual as the target production plan of the target production task. The above technical solution can solve the problems existing in the existing production scheduling methods, such as waste of manpower, unstable solution quality, poor algorithm versatility, complex modeling process, high computing demand, easy to fall into local optimality, slow algorithm convergence speed and easy to ignore potential optimal solutions. It can improve the global search capability and convergence speed of the production scheduling algorithm, and improve the scientificity and accuracy of production scheduling. It has strong versatility, meets the computing needs of large-scale complex scenarios, has low computing demand, and is particularly suitable for large-scale discrete production scheduling optimization.

[0066] Embodiment 2

[0067] Figure 3 is a flowchart of a production scheduling method based on differential evolution and taboo search provided by the second embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, multiple specific optional implementation methods for establishing a production scheduling optimization model and optimizing the production sequence of individual production plans are provided. Figure 3 As shown, the method of this embodiment may include:

[0068] S210: Generate a target production plan for the target production task according to the production line associated data of the target production task.

[0069] The production line associated data may be data related to the product production line, for example, including but not limited to the production time of the production line, the production date, and the production sequence of the product, etc. As long as it is data related to the production line, the embodiment of the present invention does not limit the data type and data content of the production line associated data. The target production plan is also a production plan applicable to the target production task.

[0070] Before generating a target production plan for a target production task, relevant data of each production line of the target production task can be collated and collected as production line-related data of the target production task. Further, multiple target production plans of the target production task can be generated based on the production line-related data of the target production task.

[0071] In an alternative embodiment of the present invention, the production line related data of the target production task may include production duration, product quantity, and production sequence. Correspondingly, when generating the target production plan of the target production task based on the production line related data of the target production task, the production plans of all production lines within the target production task scheduling period can be designed as an n days ×n lines matrix X, where n days is the number of days included in the target production task scheduling period, and n lines is the number of production lines to be scheduled.

[0072] Optionally, the target production plan of the target production task can be generated based on the following formula:

[0073]

[0074] where X represents the target production plan, represents the production sequence of production line j on date i; represents the product number; represents the production duration of the product numbered on date i; n prd represents the quantity of all products.

[0075] In the above formula, is represented by a list, and the elements in the list represent the production duration of the product numbered on date i. can use integer coding, can use floating-point or integer coding according to different scenarios, can also take the value of 0, indicating not produced. In the application scenario of discrete manufacturing, for the sake of production efficiency, the number of times any production line produces the same product within a day is less than or equal to 1. Therefore the range of the number of elements in the list is always n prd , and n prd is the quantity of all producible products. It should be noted that the order of the elements in the list represents the production sequence, and the product code in the element has no association with its position in the list, that is the list can also be constituted by the following formula:

[0076]

[0077] The above list represents that production line j first produces the product numbered p0 on date i, and the production duration of its product is Next, produce the product with product number , and the production duration of the product is Finally, produce the product with product number p1, and the production duration of the product is

[0078] S220. Establish the target constraint conditions of the target production plan according to the production-related constraint factors.

[0079] Among them, the production-related constraint factors can be the relevant factors used to generate the restrictive constraint conditions in the target production task. For example, they can include but are not limited to factors such as production line efficiency, working hours calendar, and production targets. The embodiments of the present invention do not limit the types and quantities of the production-related constraint factors. The target constraint conditions can be the constraint conditions configured according to the actual production situation of the target production task.

[0080] Different types of target production tasks have different production restriction conditions. Therefore, the corresponding production-related constraint factors can be determined according to the type of the target production task, and then the target constraint conditions adapted to the target production plan can be established according to the production-related constraint factors. The target production task needs to execute the production scheduling process on the premise of meeting the target constraint conditions.

[0081] In an alternative embodiment of the present invention, the target constraint conditions include at least one of the production target constraint conditions and the working hours calendar constraint conditions; the establishing the target constraint conditions of the target production plan according to the production-related constraint factors may include: establishing the production target constraint conditions based on the following formula:

[0082]

[0083] Among them, represents the production quantity of product k in all production lines on the i-th day in the target production plan; t prd k represents the production duration of product k on date i; represents the quantity of product k produced by production line j per unit time; n days represents the number of days in the scheduling period of the target production task; n prd represents the quantity of all products; represents the production target of product k on the i-th day; TAR k represents the total production target of product k within the scheduling period; represents the production sequence of production line j on date i.

[0084] In the expression of the above production target constraint conditions, represents the production target constraint condition that the daily production quantity of each product should meet the standard; It represents the production target constraint condition that the total production quantity of each product should reach the standard within the scheduling period of the target production task.

[0085] Optionally, the working hour calendar constraint condition can be established based on the following formula:

[0086]

[0087] Wherein, represents the production duration of production line j on the i-th day in the target production plan X, represents the upper limit of the production duration of production line j on the i-th day; n lines represents the number of production lines for which the target production task accepts scheduling.

[0088] Optionally, the value of can be obtained by sorting out the working hour calendar provided by the corresponding manufacturer. The above working hour calendar constraint condition indicates that the production duration of any production line in the target production plan X on any day shall not exceed the specified production duration of the working hour calendar.

[0089] It should be noted that in addition to the above types of target constraint conditions, other types of target constraint conditions can also be configured according to the type of the target production task and the actual production requirements. The embodiments of the present invention do not limit the types and quantities of the target constraint conditions corresponding to the target production task.

[0090] S230. Establish the production scheduling optimization model according to the target constraint condition of the target production plan and the production scheduling objective function.

[0091] Wherein, the production scheduling objective function can be an objective function configured for the entire target production task, and is used to evaluate the production plan of the target production task.

[0092] Optionally, the production scheduling objective function can be established with the goal of minimizing the production time and the product changeover time, so as to evaluate the target production plan X through the production scheduling objective function. At the same time, based on the production scheduling objective function, combined with the target constraint conditions established for the target production plan based on relevant production information such as production line efficiency, working hour calendar and production target, a production scheduling optimization model for the target production task is formed.

[0093] In an optional embodiment of the present invention, the production scheduling objective function can be established based on the following formula:

[0094] min Obj(X) = T prd (X) + T ch g (X)

[0095]

[0096]

[0097] Wherein, min Obj(X) represents the production scheduling objective function, T prd (X) represents the sum of the production hours of all production lines on all dates and for all products in the target production plan X; represents the sum of the production time of all products in the production sequence of production line j on date i; t prd k represents the production time of product k on date i; T ch g (X) represents the product switching time of all production lines on all dates in the target production plan X; n days Indicates the number of days of the target production task scheduling cycle; n lines Indicates the number of production lines that accept scheduling for the target production task; Represents the production sequence of production line j on date i.

[0098] Among them, t ch g j It represents the switching time between two adjacent products in the production sequence of production line j on all dates. The switching time between two adjacent products can span days, that is, the last product produced by the production line on day i is adjacent to the first product produced on day i+n (n≤1), and each product involved in the calculation should be produced in the target production plan X being evaluated, that is, the production time should be greater than 0. When the production time of a product is equal to 0, the current product is adjacent to the next product whose production time is greater than 0. The switching time of each product on each production line can be obtained based on the information provided by the manufacturer.

[0099] Optionally, various formulas of the target constraint conditions of the above target production plan and the production scheduling objective function are combined to obtain a production scheduling optimization model for the target production task.

[0100] S240. Randomly generate an initial production plan population for the target production task according to a production scheduling optimization model for the target production task.

[0101] After establishing the production scheduling optimization model of the target production task, the initial production plan population of the target production task can be randomly generated on the premise of satisfying the constraints and production scheduling objectives based on the various target constraints and production scheduling objective functions in the production scheduling optimization model of the target production task. The initial production plan population of the target production task may include multiple production plan individuals. Optionally, the number of production plan individuals in the initial production plan population may be at least 4, and the embodiment of the present invention does not limit the specific number of production plan individuals in the initial production plan population. Each production plan individual is a complete target production plan X.

[0102] S250. Optimize the production sequence of each production plan individual in the initial production plan population by using a tabu search algorithm.

[0103] Optionally, a tabu search algorithm can be used to optimize the production sequence of each production plan individual in the initial production plan population without considering production time, that is, to achieve the goal of minimizing the switching time between different products in the target production plan.

[0104] In an optional embodiment of the present invention, optimizing the production sequence of each production plan individual in the initial production plan population by using a tabu search algorithm may include: optimizing the production sequence of the production plan individual by using the tabu search algorithm based on the following production sequence objective function:

[0105] min Obj ts (X) = T ch g (X)

[0106] where min Obj ts (X) represents the production sequence objective function, and T ch g (X) represents the product switching duration of all production lines on all dates in the target production plan X. The production sequence objective function is also a function for optimizing the product production sequence.

[0107] The above production sequence objective function can optimize the production switching time of products caused by the production sequence.

[0108] S260. Optimize the production quantity of the production plan individual by using a differential evolution algorithm to obtain the current production plan population.

[0109] S270. Optimize the production sequence of the newly added production plan individuals in the current production plan population by using the tabu search algorithm.

[0110] In an optional embodiment of the present invention, optimizing the production sequence of the newly added production plan individuals in the current production plan population may include: optimizing the production sequence of the newly added production plan individuals in the current production plan population by using the tabu search algorithm based on the following production sequence objective function:

[0111] min Obj ts (X) = T ch g (X)

[0112] where min Obj ts (X) represents the production sequence objective function, and T ch g (X) represents the product switching duration of all production lines on all dates in the target production plan X.

[0113] S280. Screen the current optimal production plan individual from the optimized current production plan population.

[0114] In an alternative embodiment of the present invention, screening the current optimal production plan individual from the optimized current production plan population may include: screening the current optimal production plan individual from the optimized current production plan population based on the following formula:

[0115] min Obj(X) = T prd (X) + T ch g (X)

[0116]

[0117] Wherein, min Obj(X) represents the production scheduling objective function, and T prd (X) represents the sum of the production durations of all production lines for all dates and all products in the target production plan X; represents the sum of the production times of all products in the production order of production line j on date i; t prd k represents the production duration of product k on date i; T ch g (X) represents the product changeover duration of all production lines in the target production plan X for all dates; t ch g j represents the changeover time between two adjacent products among the products in the production order of production line j for all dates; n days represents the number of days in the target production task scheduling period; n lines represents the number of production lines for which the target production task is scheduled; represents the production order of production line j on date i.

[0118] That is, all production plan individuals in the current round of iteration can be evaluated through the production scheduling objective function, and the optimal production plan individual in the current round can be screened and stored according to the evaluation results.

[0119] S290. Determine whether the current optimal production plan individual remains unchanged. If so, execute S2110; otherwise, return to execute S260.

[0120] S2110. Use the production plan corresponding to the current optimal production plan individual as the target production plan for the target production task.

[0121] In the above technical solution, by generating a target production plan for the target production task according to the production line association data of the target production task, and establishing target constraint conditions for the target production plan according to production association constraint factors, and then establishing a production scheduling optimization model according to the target constraint conditions of the target production plan and the production scheduling objective function, the scientificity, accuracy, and rationality of the production scheduling optimization model are improved. By evaluating all production plan individuals in each round of iteration through the production scheduling objective function, and screening the optimal production plan individuals in each round according to the evaluation results, the accuracy of screening the optimal production plan individuals can be improved.

[0122] Embodiment III

[0123] Figure 4 FIG. is a flowchart of a production scheduling method based on differential evolution and tabu search provided in Embodiment III of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, various specific optional implementation manners of optimizing the production quantity of production plan individuals by using the differential evolution algorithm are given. Correspondingly, as Figure 4 shown, the method of this embodiment may include:

[0124] S310. Randomly generate an initial production plan population for the target production task according to the production scheduling optimization model of the target production task.

[0125] S320. Use the tabu search algorithm to optimize the production order of each production plan individual in the initial production plan population.

[0126] S330. Randomly select a first set number of production plan individuals to be processed from the current production plan population.

[0127] Among them, the first set number can be set according to actual needs. For example, it can be n. Usually, the value of n can be greater than 3. The production plan individuals to be processed can be randomly selected from the current production plan population and need to be evolved.

[0128] S340. Screen a second set number of target associated individuals for the production plan individuals to be processed, and establish a group of individuals to be processed according to the production plan individuals to be processed and the target associated individuals matched with the production plan individuals to be processed.

[0129] Among them, the second set number can also be set according to actual needs. Usually, it can be 3. The present invention embodiment does not limit the specific value of the second set number. The target associated individuals can be relevant production plan individuals screened for the production plan individuals to be processed. A production plan individual to be processed can correspond to multiple screened target associated individuals. The group of individuals to be processed can be a group of production plan individuals composed of the production plan individuals to be processed and the target associated individuals matched with the production plan individuals to be processed.

[0130] When optimizing the production quantity of production plan individuals using the differential evolution algorithm, first, a first set number of production plan individuals to be processed can be randomly selected from the current production plan population. For example, first, n production plan individuals to be processed are randomly selected from the current production plan population. Further, a second set number of target associated individuals are screened for each production plan individual to be processed. For example, 3 corresponding target associated individuals can be screened for each production plan individual to be processed from the current production plan population. Correspondingly, each production plan individual to be processed and the 3 target associated individuals screened correspondingly can form a group of individuals to be processed. That is, a group of individuals to be processed can include 4 production plan individuals.

[0131] In an alternative embodiment of the present invention, screening the second set number of target associated individuals for the production plan individuals to be processed may include: randomly extracting a third set number of production plan individuals from the current production plan population; performing multiple rounds of selection on the third set number of production plan individuals according to the binary tournament operator with an adaptive probability to obtain a current target associated individual; and returning to perform the operation of randomly extracting a third set number of production plan individuals from the current production plan population until it is determined that the current number of the target associated individuals reaches the second set number.

[0132] Among them, the third set number can also be set according to actual needs, such as being set to m, which can generally be a value greater than 3. The present invention embodiment does not limit the specific value of the third set number. The current target associated individual can be the target associated individual screened by the binary tournament operator currently.

[0133] In a specific example, assume that the number of target associated individuals required is 3. Then, for each production plan individual to be processed, the process of selecting target associated individuals can be performed three times without replacement from the current production plan population. Specifically, in the first process of selecting target associated individuals, m production plan individuals (the third set quantity m is a settable parameter, and its value is an integer greater than 3) can be randomly selected from the current production plan population. Further, in the m production plan individuals, a binary tournament operator with an adaptive probability is used for multiple rounds of selection to determine the first target associated individual from the m production plan individuals. Similarly, in the second process of selecting target associated individuals, m production plan individuals can be randomly selected from the current production plan population. Further, in the m production plan individuals, a binary tournament operator with an adaptive probability is used for multiple rounds of selection to determine the second target associated individual from the m production plan individuals. Similarly, in the third process of selecting target associated individuals, m production plan individuals can be randomly selected from the current production plan population. Further, in the m production plan individuals, a binary tournament operator with an adaptive probability is used for multiple rounds of selection to determine the third target associated individual from the m production plan individuals. Finally, 3 target associated individuals are screened out and combined with the current 1 production plan individual to be processed to form a group of individuals to be processed. That is, a total of n groups of individuals to be processed can be obtained. Each group of individuals to be processed can include 1 production plan individual to be processed and 3 target associated individuals, a total of 4 non-repeated production plan individuals. It should be noted that the production plan individuals included in different groups of individuals to be processed can be repeated.

[0134] In an alternative embodiment of the present invention, the multiple rounds of selection of the third set quantity of production plan individuals according to the binary tournament operator with an adaptive probability may include: performing multiple rounds of selection on the third set quantity of production plan individuals based on the following formula:

[0135]

[0136] Wherein, represents the probability that a production plan individual is screened; rand(0,1) is a random number greater than 0 and less than 1; and are production plan individuals participating in the m-th round of binary tournament; is the production plan individual participating in the (m + 1)-th round of binary tournament, that is, and the selected production plan individuals in, m, n, and h represent natural numbers. Individuals not selected in each round of binary tournament are eliminated and do not participate in the next round of selection.

[0137] S350. Perform crossover and mutation processing on the to-be-processed individual group to obtain new production plan individuals.

[0138] Among them, the new production plan individuals can be the new production plan individuals obtained by performing crossover and mutation processing on the to-be-processed individual group.

[0139] According to the basic principle of the differential evolution algorithm, after determining the to-be-processed individual group, crossover processing and mutation processing can be performed on the to-be-processed individual group. Each processing process can generate new production plan individuals, and the new production plan individuals are added to the current production plan population to realize the update of the production plan population.

[0140] In an optional embodiment of the present invention, the performing crossover and mutation processing on the to-be-processed individual group to obtain new production plan individuals may include: performing mutation processing on the to-be-processed individual group based on the following formula to obtain the first new production plan individual:

[0141]

[0142] where t prd k represents the production duration of product k at date i; R1, R2, and R3 represent three target associated individuals corresponding to the current to-be-processed production plan individual, X mut represents the first new production plan individual; n prd represents the number of all products; n days represents the number of days in the scheduling period of the target production task; n lines represents the number of production lines for which the target production task is scheduled; K represents the target product selected using the roulette wheel operator.

[0143] Among them, the first new production plan individual is also the new production plan individual obtained by performing mutation processing on the to-be-processed individual group.

[0144] Optionally, the roulette wheel operator can be used to randomly select at least one product K according to the total manufacturing duration of each product within the scheduling period of the target production task, with the probability increasing as the duration becomes longer, and without changing the production order in the to-be-processed production plan individual, use the above formula to complete the mutation operation of the product production quantity for the to-be-processed individual group.

[0145] Optionally, the to-be-processed individual group can be cross-processed based on the following formula to obtain the second new production plan individual:

[0146]

[0147] where X crs represents the second new production plan individual; Rbest Represents the optimal target - associated individual; rand(0,1) is a random number greater than 0 and less than 1 and equal within the same product; CR represents a crossover probability setting value greater than 0 and less than 1; n prd Represents the number of products of all products; Represents the production sequence of production line j on date i.

[0148] Among them, the second newly - added production plan individual is also the newly - added production plan individual obtained by performing crossover processing on the individual group to be processed. The optimal target - associated individual is also the target - associated individual with the best production scheduling plan effect among the target - associated individuals.

[0149] Optionally, the roulette - wheel operator can be used to randomly select at least one product K according to the total manufacturing duration of each product within the production cycle of the target production task, with the probability being greater as the duration is longer. At the same time, the optimal target - associated individual R can be selected from each target - associated individual best , and perform a crossover operation with the production plan individual to be processed. Optionally, the following production scheduling objective function can be used to select the optimal target - associated individual R from each target - associated individual best :

[0150] min Obj(X) = T prd (X)+T ch g (X)

[0151]

[0152] S360. Generate the current production plan population according to the original production plan individual and the newly - added production plan individual.

[0153] After obtaining the newly - added production plan individual, add the newly - added production plan individual to the current production plan population, so that the population update operation of the current production plan population is completed by the original production plan individuals and the newly - added production plan individuals in the current production plan population.

[0154] S370. Optimize the production sequence of the newly - added production plan individuals in the current production plan population using the tabu search algorithm.

[0155] Optionally, the tabu search algorithm can be used to optimize the production sequence of the newly - added production plan individuals X mut and X crs obtained by mutation and crossover without considering the production time, that is, complete the minimization objective of the switching time between different products in the newly - added production plan individuals X mut and X crs .

[0156] S380. Screen the current optimal production plan individual from the optimized current production plan population.

[0157] S390. Determine whether the current optimal production plan individual remains unchanged. If so, execute S3110; otherwise, return to execute S330.

[0158] S3110. Take the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

[0159] In the above technical solution, by using the binary tournament operator with adaptive probability to improve the traditional differential evolution algorithm, and controlling the randomly generated direction through the roulette wheel operator in the mutation and crossover steps of the differential evolution algorithm, the global search ability and convergence speed of the differential evolution algorithm can be improved, and the scientificity and accuracy of production scheduling can be enhanced. At the same time, by combining the improved differential evolution algorithm with the tabu search algorithm, potential optimal solutions can be explored. The production scheduling method based on differential evolution and tabu search provided by the embodiments of the present invention can, while retaining the advantages of strong versatility of heuristic algorithms, the ability to handle large-scale complex scenarios, and low computational requirements, solve the problems of slow convergence speed and easy neglect of potential solutions of traditional heuristic algorithms, and is more suitable for large-scale discrete production scheduling optimization.

[0160] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the present disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of the relevant data comply with the relevant laws, regulations, and standards in the relevant regions.

[0161] It should be noted that any permutation and combination of the technical features in the above embodiments also fall within the protection scope of the present invention.

[0162] Embodiment 4

[0163] Figure 5 is a schematic diagram of a production scheduling device based on differential evolution and tabu search provided by Embodiment 4 of the present invention. As Figure 5 shown, the device includes: an initial production plan population generation module 410, a first production order optimization module 420, a production quantity optimization module 430, a second production order optimization module 440, a current optimal production plan individual screening module 450, an iterative calculation module 460, and a target production plan determination module 470, where:

[0164] The initial production plan population generation module 410 is configured to randomly generate an initial production plan population of the target production task according to the production scheduling optimization model of the target production task;

[0165] The first production sequence optimization module 420 is used to optimize the production sequence of each production plan individual in the initial production plan population;

[0166] The production quantity optimization module 430 is used to optimize the production quantity of the production plan individual to obtain the current production plan population;

[0167] The second production sequence optimization module 440 is used to optimize the production sequence of the newly added production plan individuals in the current production plan population;

[0168] The current optimal production plan individual screening module 450 is used to screen the current optimal production plan individual from the optimized current production plan population;

[0169] The iterative calculation module 460 is used to return and execute the operation of optimizing the production quantity of the production plan individual until it is determined that the current optimal production plan individual remains unchanged;

[0170] The target production plan determination module 470 is used to use the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

[0171] In the embodiment of the present invention, after randomly generating the initial production plan population of the target production task according to the production scheduling optimization model of the target production task, the production sequence of each production plan individual in the initial production plan population is optimized, and the production quantity of the production plan individual is optimized to obtain the current production plan population. After the production quantity optimization is completed, the production sequence of the newly added production plan individuals in the current production plan population can be optimized, and then the current optimal production plan individual is screened from the optimized current production plan population to complete the production scheduling optimization of the current round. Further, return and execute the operation of optimizing the production quantity of the production plan individual until it is determined that the current optimal production plan individual remains unchanged, complete the iterative process, and use the production plan corresponding to the current optimal production plan individual obtained by iterative calculation as the target production plan of the target production task. The above technical solution can solve the problems existing in the existing production scheduling methods, such as wasting manpower, unstable solution quality, poor algorithm generality, complex modeling process, high computing requirements, easy to fall into local optimum, slow algorithm convergence speed, and easy to ignore potential optimal solutions, etc. It can improve the global search ability and convergence speed of the production scheduling algorithm, and improve the scientificity and accuracy of production scheduling. It has strong generality, meets the computing requirements of large-scale complex scenarios, has low computing requirements, and is particularly suitable for large-scale discrete production scheduling optimization.

[0172] Optionally, the above device further includes a production scheduling optimization model establishment module, configured to generate a target production plan for the target production task according to the production line association data of the target production task; establish target constraint conditions for the target production plan according to production association constraint factors; and establish the production scheduling optimization model according to the target constraint conditions of the target production plan and the production scheduling objective function.

[0173] Optionally, the production line association data of the target production task includes production duration, quantity of products, and production sequence; the production scheduling optimization model establishment module is further configured to: generate the target production plan for the target production task based on the following formula:

[0174]

[0175] where X represents the target production plan; represents the production sequence of production line j on date i; represents the product number; represents numbered of the product on date i; n prd represents the quantity of all products; n days represents the number of days in the scheduling period of the target production task; n lines represents the number of production lines for which the target production task is scheduled.

[0176] Optionally, the target constraint conditions include at least one of production target constraint conditions and working hour calendar constraint conditions; the production scheduling optimization model establishment module is further configured to: establish the production target constraint conditions based on the following formula:

[0177]

[0178] where represents the production quantity of product k among all production lines on the i-th day in the target production plan; t prd k represents the production duration of product k on date i; represents the quantity of product k produced by production line j per unit time; n days represents the number of days in the scheduling period of the target production task; n prd represents the quantity of all products; represents the production target of product k on the i-th day; TAR k represents the total production target of product k within the scheduling period; represents the production sequence of production line j on date i;

[0179] Establish the working hour calendar constraint conditions based on the following formula:

[0180]

[0181] Among them, represents the production duration of production line j on the i-th day in the target production plan X, represents the upper limit of the production duration of production line j on the i-th day; n lines represents the number of production lines for which the target production task is scheduled.

[0182] Optionally, the production scheduling objective function can also be established based on the following formula:

[0183] min Obj(X) = T prd (X) + T ch g (X)

[0184]

[0185] Among them, min Obj(X) represents the production scheduling objective function, T prd (X) represents the sum of the production durations of all production lines in the target production plan X for all dates and all products; represents the sum of the production times of all products in the production order of production line j on date i; t prd k represents the production duration of product k on date i; T ch g (X) represents the product switching duration of all production lines in the target production plan X for all dates; t ch g j represents the switching time between two adjacent products among the products in the production order of production line j for all dates; n days represents the number of days in the scheduling cycle of the target production task; n lines represents the number of production lines for which the target production task is scheduled; represents the production order of production line j on date i.

[0186] Optionally, the first production order optimization module 420 or the second production order optimization module 440 is further configured to: optimize the production order of the production plan individual or the new production plan individual based on the following production order objective function by using a tabu search algorithm:

[0187] min Obj ts (X) = T ch g (X)

[0188] Among them, minObj ts (X) represents the production order objective function, T ch g (X) represents the product switching duration of all production lines in the target production plan X for all dates.

[0189] Optionally, the production quantity optimization module 430 is further configured to: randomly select a first set number of production plan individuals to be processed from the current production plan population; screen a second set number of target associated individuals from the production plan individuals to be processed, and establish a group of individuals to be processed according to the production plan individuals to be processed and the target associated individuals matched with the production plan individuals to be processed; perform crossover and mutation processing on the group of individuals to be processed to obtain new production plan individuals; generate the current production plan population according to the original production plan individuals and the new production plan individuals.

[0190] Optionally, the production quantity optimization module 430 is further configured to: randomly extract a third set number of production plan individuals from the current production plan population; perform multiple rounds of selection on the third set number of production plan individuals according to the binary tournament operator with an adaptive probability to obtain a current target associated individual; return to execute the operation of randomly extracting a third set number of production plan individuals from the current production plan population until it is determined that the current number of the target associated individuals reaches the second set number.

[0191] Optionally, the production quantity optimization module 430 is further configured to: perform multiple rounds of selection on the third set number of production plan individuals based on the following formula:

[0192]

[0193] Wherein, represents the probability that the production plan individual is screened; rand(0,1) is a random number greater than 0 and less than 1; and are production plan individuals participating in the m-th round of binary tournament; is a production plan individual participating in the (m + 1)-th round of binary tournament, and m, n, and h represent natural numbers.

[0194] Optionally, the production quantity optimization module 430 is further configured to: perform mutation processing on the group of individuals to be processed based on the following formula to obtain the first new production plan individual:

[0195]

[0196] Wherein, t prd k represents the production duration of product k on date i; R1, R2, and R3 represent three target associated individuals corresponding to the current production plan individual to be processed, X mut represents the first new production plan individual; n prd represents the quantity of all products; n days represents the number of days in the target production task scheduling period; n linesThe number of production lines that accept scheduling for the target production task; K represents the target product selected using the roulette wheel operator;

[0197] Cross-process the to-be-processed individual group based on the following formula to obtain a second newly added production plan individual:

[0198]

[0199] where, X crs represents the second newly added production plan individual; R best represents the optimal target-related individual; rand(0,1) is a random number greater than 0 and less than 1 and equal within the same product; CR represents a crossover probability setting value greater than 0 and less than 1; n prd represents the number of products of all products; represents the production sequence of production line j on date i.

[0200] Optionally, the current optimal production plan individual screening module 450 is further configured to: screen the current optimal production plan individual from the optimized current production plan population based on the following formula:

[0201] min Obj(X) = T prd (X) + T ch g (X)

[0202]

[0203] where, min Obj(X) represents the production scheduling objective function, T prd (X) represents the sum of the production durations of all production lines in the target production plan X for all dates and all products; represents the sum of the production times of all products in the production sequence of production line j on date i; t prd k represents the production duration of product k on date i; T ch g (X) represents the product switching duration of all production lines in the target production plan X for all dates; t ch g j represents the switching time between two adjacent products among the products in the production sequence of production line j for all dates; n days represents the number of days in the scheduling period of the target production task; n lines represents the number of production lines that accept scheduling for the target production task; represents the production sequence of production line j on date i.

[0204] The above production scheduling device based on differential evolution and tabu search can execute the production scheduling method based on differential evolution and tabu search provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For the technical details not described in detail in this embodiment, reference can be made to the production scheduling method based on differential evolution and tabu search provided in any embodiment of the present invention.

[0205] Since the above-introduced production scheduling device based on differential evolution and tabu search is a device that can execute the production scheduling method based on differential evolution and tabu search in the embodiments of the present invention, based on the production scheduling method based on differential evolution and tabu search introduced in the embodiments of the present invention, those skilled in the art can understand the specific implementation manners and various variations of the production scheduling device based on differential evolution and tabu search in this embodiment. Therefore, the implementation of how the production scheduling device based on differential evolution and tabu search realizes the production scheduling method based on differential evolution and tabu search in the embodiments of the present invention will not be described in detail here. As long as the devices adopted by those skilled in the art to implement the production scheduling method based on differential evolution and tabu search in the embodiments of the present invention belong to the scope to be protected by this application.

[0206] Embodiment 5

[0207] Figure 6 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0208] As Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0209] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0210] The processor 11 can be various general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the production scheduling method based on differential evolution and tabu search.

[0211] Optionally, the production scheduling method based on differential evolution and tabu search may include: randomly generating an initial production plan population of the target production task according to the production scheduling optimization model of the target production task; optimizing the production order of each production plan individual in the initial production plan population; optimizing the production quantity of the production plan individual to obtain the current production plan population; optimizing the production order of the newly added production plan individuals in the current production plan population; screening the current optimal production plan individual from the optimized current production plan population; returning to execute the operation of optimizing the production quantity of the production plan individual until it is determined that the current optimal production plan individual remains unchanged; and taking the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

[0212] In some embodiments, the production scheduling method based on differential evolution and tabu search can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the production scheduling method based on differential evolution and tabu search described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the production scheduling method based on differential evolution and tabu search by any other suitable means (e.g., by means of firmware).

[0213] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0214] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0215] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0216] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0217] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0218] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0219] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0220] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A production scheduling method based on differential evolution and tabu search, characterized in that, including: randomly generating an initial production plan population of the target production task according to a production scheduling optimization model of the target production task; optimizing the production sequence of each production plan individual in the initial production plan population; optimizing the production quantity of the production plan individual to obtain the current production plan population; optimizing the production sequence of the newly added production plan individuals in the current production plan population; screening the current optimal production plan individual from the optimized current production plan population; returning to perform the operation of optimizing the production quantity of the production plan individual until it is determined that the current optimal production plan individual remains unchanged; taking the production plan corresponding to the current optimal production plan individual as the target production plan of the target production task.

2. The method according to claim 1, wherein Before randomly generating the initial production plan population of the target production task according to the production scheduling optimization model of the target production task, it further includes: generating a target production plan of the target production task according to the production line association data of the target production task; establishing target constraint conditions of the target production plan according to production association constraint factors; establishing the production scheduling optimization model according to the target constraint conditions of the target production plan and the production scheduling objective function.

3. The method according to claim 2, wherein The method further includes: establishing the production scheduling objective function based on the following formula: min Obj(X) = T prd (X) + T chg (X) Among them, min Obj(X) represents the production scheduling objective function, and T prd (X) represents the sum of the production durations of all production lines in all dates and for all products in the target production plan X; represents the sum of the production times of all products in the production order of production line j on date i; t prd k represents the production duration of product k on date i; T chg (X) represents the product changeover duration of all production lines in all dates in the target production plan X; t chg j represents the changeover time between two adjacent products among the products in the production order of production line j in all dates; n days represents the number of days in the scheduling period of the target production task; n lines represents the number of production lines that accept scheduling for the target production task; represents the production order of production line j on date i.

4. The method according to claim 1, characterized in that: using a tabu search algorithm to optimize the production sequence of the production plan individual or the newly added production plan individual based on the following production sequence objective function: min Obj ts (X) = T chg (X) Among them, min Obj ts (X) represents the production sequence objective function, T chg (X) represents the product changeover duration of all production lines in the target production plan X on all dates.

5. The method according to claim 1, wherein optimizing the production quantity of the production plan individual to obtain the current production plan population, including: randomly selecting a first set number of production plan individuals to be processed from the current production plan population; screening a second set number of target associated individuals from the production plan individuals to be processed, and establishing a group of individuals to be processed according to the production plan individuals to be processed and the target associated individuals matched with the production plan individuals to be processed; performing crossover and mutation processing on the group of individuals to be processed to obtain newly added production plan individuals; generating the current production plan population according to the original production plan individuals and the newly added production plan individuals.

6. The method according to claim 5, characterized in that The screening of the second set number of target associated individuals from the production plan individuals to be processed includes: randomly extracting a third set number of production plan individuals from the current production plan population; performing multiple rounds of selection on the third set number of production plan individuals according to a binary tournament operator with an adaptive probability to obtain a current target associated individual; returning to perform the operation of randomly extracting a third set number of production plan individuals from the current production plan population until it is determined that the current number of the target associated individuals reaches the second set number.

7. The method according to claim 1, wherein The screening of the current optimal production plan individual from the optimized current production plan population includes: screening the current optimal production plan individual from the optimized current production plan population based on the following formula: min Obj(X) = T prd (X) + T chg (X) Among them, min Obj(X) represents the production scheduling objective function, and T prd (X) represents the sum of the production durations of all production lines for all products on all dates in the target production plan X; represents the sum of the production times of all products in the production sequence of production line j on date i; t prd k represents the production duration of product k on date i; T chg (X) represents the product changeover duration of all production lines in the target production plan X on all dates; t chg j represents the changeover time between two adjacent products among the products in the production sequence of production line j on all dates; n days represents the number of days in the target production task scheduling period; n lines represents the number of production lines for which the target production task is scheduled; represents the production sequence of production line j on date i.

8. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the production scheduling method based on differential evolution and tabu search according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the production scheduling method based on differential evolution and tabu search according to any one of claims 1-7 when the computer instructions are executed by a processor.

10. A computer program product comprising a computer program / instructions, wherein, When the computer program / instructions are executed by a processor, the production scheduling method based on differential evolution and tabu search according to any one of claims 1-7 is implemented.

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