Production scheduling method and device and computer readable storage medium

Abstract

The invention discloses a production scheduling method and device and a computer readable storage medium, and the method comprises the steps: obtaining the production order data of a target product, and initializing a population based on the production order data; crossover and variation: calculating the fitness of each individual in the population according to the objective function and the constraint function, determining high-quality individuals and low-quality individuals according to the fitness, retaining the high-quality individuals, and randomly selecting a preset number of low-quality individuals for crossover and variation to obtain a next-generation population; iteratively executing crossover mutation operation on the next-generation population until an iteration stopping condition is met, and obtaining an optimal population; and determining the production scheduling scheme corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling scheme. According to the multi-objective function and the constraint condition corresponding to the multi-constraint function, the fitness of each individual in the population is calculated, the method can adapt to the complex and changeable production scheduling problem, and therefore the production scheduling efficiency of products can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production line scheduling, and in particular to a production scheduling method, device and computer-readable storage medium. Background Art

[0002] At present, in the fierce market competition environment, production scheduling is an important part of production management of manufacturing enterprises. Its efficiency and quality directly affect the production cost, delivery time and customer satisfaction of enterprises. However, there are many constraints in the actual production process, such as machine capacity, material supply, personnel allocation, etc., which makes the production scheduling problem complex and challenging.

[0003] Existing production scheduling methods are difficult to adapt to complex and changeable production scheduling problems, resulting in low efficiency of product production scheduling. Summary of the invention

[0004] In view of the above problems, the present invention provides a production scheduling method, device and computer-readable storage medium to solve the technical problem that the existing production scheduling method is difficult to adapt to complex and changeable production scheduling problems, resulting in low efficiency of product production scheduling.

[0005] In order to solve the above technical problems, the present invention provides a production scheduling method, comprising:

[0006] Obtaining target product production order data, and initializing a population based on the production order data, wherein the population includes a plurality of individuals;

[0007] Crossover and mutation: Calculate the fitness of each individual in the population according to the objective function and constraint function, determine high-quality individuals and low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; wherein, the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function;

[0008] Iteratively performing a crossover and mutation operation on the next generation population until a stop iteration condition is met to obtain an optimal population;

[0009] The production scheduling plan corresponding to the individual with the highest fitness in the optimal population is determined as the optimal production scheduling plan.

[0010] Further, the initializing the population based on the production order data includes:

[0011] Based on the production order data, plan group order data is formed, and the plan group order data is copied and multiplied as an individual to form a new individual; wherein the plan group order is a group of production orders belonging to the same category;

[0012] When the number of individuals reaches the preset condition, the replication and reproduction of the individuals will stop, and all individuals will form a population.

[0013] Furthermore, the production scheduling method further includes:

[0014] Constructing the capacity constraint function based on product production data, wherein the product production data includes production order data, BOM data and process route data;

[0015] Constructing the resource conflict function based on product manufacturing data, wherein the product manufacturing data includes work calendar data, maintenance plan data, production line rate data, pipeline constraint configuration data and production line conflict configuration data;

[0016] Constructing the material set function based on material supply data, wherein the material supply data includes material inventory data, material lead time data and supplier supply plan data;

[0017] Constructing the product delivery satisfaction function based on product demand data, wherein the product demand data includes product delivery data, product monthly forecast data and product daily shipment forecast data;

[0018] The warehouse capacity satisfaction function is constructed based on warehouse data, wherein the warehouse data includes warehouse capacity data and box / pallet conversion relationship data.

[0019] Furthermore, the calculating the fitness of each individual in the population according to the objective function and the constraint function includes:

[0020] The start time and the end time of the production order are used as decision variables, and the fitness of each individual in the population is calculated according to the objective function and the constraint function.

[0021] Furthermore, after calculating the fitness of each individual in the population according to the objective function and the constraint function, the method further includes:

[0022] Individual sub-adjustment: determine the individuals whose fitness is lower than the preset threshold as the individuals to be adjusted. When the individuals to be adjusted do not meet the constraints corresponding to all constraint functions, adjust the production order start time and production line data in the individuals to be adjusted to form new individuals, and recalculate the fitness of the new individuals.

[0023] When the fitness of the new individual is higher than a preset threshold, or when the upper limit of the individual sub-adjustment times is reached, the individual sub-adjustment operation is stopped.

[0024] Furthermore, after determining the production scheduling scheme corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling scheme, the method further includes:

[0025] When the optimal production scheduling solution satisfies the constraint conditions corresponding to all constraint functions and the value of the objective function is within a preset threshold range, the optimal production scheduling solution is applied to the production of the target product.

[0026] The present invention also provides a production scheduling device, comprising:

[0027] An initialization population module is used to obtain target product production order data and initialize a population based on the production order data, wherein the population includes a plurality of individuals;

[0028] A crossover and mutation module is used to calculate the fitness of each individual in the population according to the objective function and the constraint function, determine the high-quality individuals and the low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; wherein the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the production capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function;

[0029] An iteration module, used for iteratively performing a crossover and mutation operation on the next generation population until a stop iteration condition is met to obtain an optimal population;

[0030] The optimal production scheduling scheme determination module is used to determine the production scheduling scheme corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling scheme.

[0031] The present invention also provides a production scheduling device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0032] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operation of the production scheduling method as described above.

[0033] The present invention also provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction is executed on a production scheduling device / apparatus, the production scheduling device / apparatus performs the operations of the production scheduling method as described above.

[0034] The embodiment of the present invention calculates the fitness of each individual in the population according to the constraints corresponding to the multi-objective function and the multi-constraint function, which is conducive to balancing different objectives and constraints in the case of multiple objectives and multiple constraints, can adapt to complex and changeable production scheduling problems, and determine the optimal production scheduling plan while optimizing the objective function and satisfying all business constraints, thereby effectively improving the production scheduling efficiency of the product.

[0035] Furthermore, the embodiments of the present invention can improve the fitness of individuals that do not meet the constraints, making them closer to the optimization goal, thereby increasing the chance of being selected, and sub-adjusting the individuals by adjusting the start time of the production order and the production line data can effectively improve the rationality of production resource allocation, thereby further improving the production efficiency of the product.

[0036] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0038] Figure 1 A schematic diagram of a production scheduling method provided by an embodiment of the present invention is shown;

[0039] Figure 2 A schematic diagram of a process of initializing a population provided by an embodiment of the present invention is shown;

[0040] Figure 3 Another schematic diagram showing the process of initializing a population provided by an embodiment of the present invention is shown

[0041] Figure 4 The flowchart of constructing a constraint function provided by an embodiment of the present invention is shown;

[0042] Figure 5 Another schematic diagram of the production scheduling method provided by an embodiment of the present invention is shown;

[0043] Figure 6 A schematic diagram showing the structure of a production scheduling device provided by an embodiment of the present invention is shown;

[0044] Figure 7 A schematic diagram of the structure of a production scheduling device provided in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0046] See also Figure 1 , an embodiment of the present invention provides a production scheduling method, comprising:

[0047] S1. Obtain the production order data of the target product, and initialize the population based on the production order data, where the population includes multiple individuals;

[0048] In an embodiment of the present invention, a plan group order data can be determined based on the production order data, and the plan group order data includes multiple production orders. The plan group order data is copied and reproduced as an individual to form a population, that is, the population is initialized.

[0049] S2, crossover and mutation: Calculate the fitness of each individual in the population according to the objective function and constraint function, determine the high-quality individuals and low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; Among them, the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function;

[0050] In an embodiment of the present invention, determining high-quality individuals and low-quality individuals according to fitness can be that individuals corresponding to fitness higher than a first preset threshold are regarded as high-quality individuals; and individuals corresponding to fitness lower than a second preset threshold are regarded as low-quality individuals. The embodiment of the present invention can help the algorithm explore new possible solution spaces by involving low-quality individuals in crossover and mutation, while retaining high-quality individuals helps develop known excellent solution spaces, thereby improving the reliability of determining the optimal production scheduling solution.

[0051] In an embodiment of the present invention, the objective function includes a minimum total production time objective function, a minimum comprehensive cost objective function and a maximum delivery on-time rate objective function, wherein the minimum total production time objective function can improve production efficiency and reduce production costs, and by optimizing the production process and reducing unnecessary waiting and idle time, the production rhythm can be accelerated and the order delivery cycle can be shortened; the minimum comprehensive cost objective function can improve cost control and resource optimization utilization, and by reducing waste and overproduction, material, labor and energy costs can be reduced, while improving resource utilization and reducing unnecessary inventory and logistics costs; the maximum delivery on-time rate objective function can improve the reliability of timely completion of product delivery.

[0052] In an embodiment of the present invention, the constraint functions include a capacity constraint function, a resource conflict function, a material completeness function, a product delivery satisfaction function and a warehouse capacity satisfaction function, wherein the capacity constraint function can enable the normal production of production orders; the resource conflict function can ensure that the scheduling process of production orders complies with the constraints of the factory's manufacturing capacity, constructs capacity constraints and resource conflicts; the material completeness function can have sufficient material resources, including materials and materials used to produce target products, thereby ensuring the smooth progress of production orders; the product delivery satisfaction function can consider the sequence of different product orders and the supply and demand of product availability during the scheduling process; the warehouse capacity satisfaction function can ensure that production scheduling will not cause warehouse explosions.

[0053] In an embodiment of the present invention, the fitness of each individual in the population is calculated according to the constraints corresponding to the multi-objective function and the multi-constraint function, which is conducive to balancing different objectives and constraints in the case of multiple objectives and multiple constraints, can adapt to complex and changeable production scheduling problems, and determine the optimal production scheduling plan while optimizing the objective function and satisfying all business constraints, thereby effectively improving the production scheduling efficiency.

[0054] S3, iteratively perform crossover and mutation operations on the next generation population until the stop iteration condition is met to obtain the optimal population;

[0055] In the embodiment of the present invention, it can be set that the iteration stopping condition is reached after 10 iterations.

[0056] S4. Determine the production scheduling plan corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling plan.

[0057] In an embodiment of the present invention, the final production scheduling plan is determined through fitness evaluation to ensure that the determined production scheduling plan can maximize production efficiency and minimize production costs to the greatest extent while satisfying all constraints, thereby improving the product production efficiency of the target product.

[0058] See also Figure 2 In one embodiment, step S1, initializing a population based on production order data, comprises:

[0059] S11. forming plan group order data based on the production order data, and duplicating and multiplying the plan group order data as individuals to form new individuals; wherein the plan group order is a group of production orders belonging to the same category;

[0060] S12. When the number of individuals reaches the preset condition, stop copying and reproducing the individuals, and form a population with all the individuals.

[0061] In the embodiment of the present invention, preset conditions can be set according to actual needs. For example, when the number of individuals reaches 100, 150 or 200, the individuals are stopped from being replicated and reproduced, and a population is formed with 100, 150 or 200 individuals, that is, the population is initialized.

[0062] See also Figure 3 , is another flow chart of an initialization population provided by an embodiment of the present invention. In the embodiment of the present invention, after starting to initialize the population, the original work order of the plan group is obtained, and the original work order is copied and multiplied based on the plan to form an initial plan group population n. When n≤150, the original work order of the plan group is continuously obtained and copied and multiplied until the formed initial plan group population n>150, that is, the preset number is reached, and the copying and multiplication operation is stopped to complete the initialization of the population.

[0063] See also Figure 4 In one embodiment, the production scheduling method further includes:

[0064] S21. constructing a capacity constraint function based on product production data, wherein the product production data includes production order data, BOM data (bill of materials data) and process route data;

[0065] In an embodiment of the present invention, product production data can be used as the main body of scheduling in the overall scheduling process and as the source of calculation for other data. For example, BOM data corresponds to the material requirements of a production order. A capacity constraint function can be constructed based on BOM data and other product production data, which can effectively enable the normal production of production orders.

[0066] S22, constructing a resource conflict function based on product manufacturing data, wherein the product manufacturing data includes work calendar data, maintenance plan data, production line rate data, pipeline constraint configuration data, and production line conflict configuration data;

[0067] In the embodiment of the present invention, product manufacturing data can reflect the constraints of internal manufacturing capacity, such as work calendar data reflecting the factory opening / closing schedule. The embodiment of the present invention builds a resource conflict function based on product manufacturing data such as work calendar data, which can ensure that the scheduling process of production orders complies with the constraints of factory manufacturing capacity, build capacity constraints and resource conflicts.

[0068] S23. Constructing a material set function based on material supply data, wherein the material supply data includes material inventory data, material lead time data, and supplier supply plan data;

[0069] In the embodiment of the present invention, in order to ensure the smooth progress of the production order during the scheduling process, it is necessary to prepare the materials and supplies for production before the expected production date. The embodiment of the present invention constructs a material set function based on the material supply data, and combines the supplier's supply plan and the inventory of existing materials in the factory to calculate whether the set date of the production order can match the required production date, so as to ensure that the production order can be carried out smoothly.

[0070] S24. constructing a product delivery satisfaction function based on product demand data, wherein the product demand data includes product delivery data, product monthly forecast data, and product daily shipment forecast data;

[0071] In an embodiment of the present invention, product demand data can be used to determine the urgency of the product in the current scheduling process and the priority of the product's delivery requirements, so that during the scheduling process, the sequence of different product orders and the supply and demand deduction of product availability can be considered to improve scheduling efficiency.

[0072] S25. Construct a warehouse capacity satisfaction function based on warehouse data, wherein the warehouse data includes warehouse capacity data and box / pallet conversion relationship data.

[0073] In an embodiment of the present invention, during the scheduling process, a warehouse capacity satisfaction function is constructed based on warehouse data, which can calculate the expected output quantity of products in the future period, and combine the available capacity of the warehouse and the box / pallet conversion relationship to calculate whether the warehouse is in a state of overcapacity. This can enable the final production scheduling plan to avoid the risk of overcapacity and improve the reliability of product scheduling.

[0074] In an embodiment of the present invention, product production data, product manufacturing data, material supply data, product demand data construction and warehouse data can be obtained through various information management systems at the supply chain end.

[0075] In one embodiment, the fitness of each individual in the population is calculated according to the objective function and the constraint function, including:

[0076] The start time and end time of the production order are used as decision variables, and the fitness of each individual in the population is calculated according to the objective function and constraint function.

[0077] In an embodiment of the present invention, the start time and end time of a production order are used as decision variables to calculate the fitness of individuals in the population, and comprehensive constraints such as production capacity constraints, resource conflicts, material supply, product delivery and warehouse capacity are considered to ensure that the selected production scheduling plan meets multiple constraints and improve the reliability of production scheduling.

[0078] In one embodiment, after calculating the fitness of each individual in the population according to the objective function and the constraint function, the method further includes:

[0079] Individual sub-adjustment: Individuals with fitness lower than the preset threshold are determined as individuals to be adjusted. When the individuals to be adjusted do not meet the constraints corresponding to all constraint functions, the production order start time and production line data in the individuals to be adjusted are adjusted to form new individuals, and the fitness of the new individuals is recalculated;

[0080] When the fitness of the new individual is higher than the preset threshold, or when the upper limit of the individual sub-adjustment times is reached, the individual sub-adjustment operation is stopped.

[0081] In the embodiment of the present invention, the upper limit of the number of individual sub-adjustments can be set according to actual needs. For example, the upper limit of the number of adjustments can be set to 5, 7, 9 times, etc.

[0082] In an embodiment of the present invention, by adjusting individuals that do not meet the constraints, their fitness can be improved to make them closer to the optimization goal, thereby increasing the chance of being selected, and by sub-adjusting the individuals by adjusting the start time of the production order and the production line data, the rationality of production resource allocation can be effectively improved, thereby effectively improving production efficiency.

[0083] See also Figure 5 , is another flow chart of the production scheduling method provided by an embodiment of the present invention. Figure 5 As shown, the production scheduling method of the embodiment of the present invention includes:

[0084] S51, calculate the individual fitness of n populations in the gth generation;

[0085] In this embodiment of the present invention, step S51 includes the following sub-steps:

[0086] S511. According to the work order scheduling and conversion cost {y1}, the packaging material complete set satisfaction rate {y2}, the earliest and latest product scheduling time satisfaction rate {y3}, the raw material satisfaction rate {y4}, the semi-finished product satisfaction rate {y5}, the warehouse capacity satisfaction rate {y6}, the resource satisfaction rate {y7} and the personnel scheduling satisfaction rate {y8} in the production scheduling process, calculate the individual fitness of the n populations of the gth generation, that is, Y = {y1}*{y2}*{y3}*…{y8}, where Y is the individual fitness;

[0087] S512, determine whether sub-adjustment is required, if not, go to step S52, if yes, go to step S513;

[0088] In the embodiment of the present invention, when an individual does not satisfy the constraint conditions corresponding to all constraint functions, it can be determined that sub-adjustment is required.

[0089] S513. Perform directional sub-adjustments according to the work order constraint indicators. The sub-adjustments include changes in work order location, changes in work order production lines, etc. Determine whether there are any work order constraints that are not met. If not, stop the sub-adjustment and go to step S52; if so, determine whether n ≤ 7. If not, stop the sub-adjustment and go to step S52. If so, continue to perform directional sub-adjustments according to the work order constraint indicators.

[0090] S52, according to the calculated individual fitness, retain M times of high-quality individuals, perform genetic algorithm crossover mutation on the low-quality individuals, and form a new population of n individuals;

[0091] S53, determine whether g is ≤ 10, if so, go to step S51, if not, go to step S54;

[0092] S54. Output the individual with the best evaluation according to the business indicators.

[0093] In one embodiment, after determining the production schedule corresponding to the individual with the highest fitness in the optimal population as the optimal production schedule, the method further includes:

[0094] When the optimal scheduling plan satisfies the constraints corresponding to all constraint functions and the value of the objective function is within a preset threshold range, the optimal production scheduling plan is applied to the production of the target product.

[0095] In an embodiment of the present invention, when the optimal scheduling plan satisfies the constraints corresponding to all constraint functions and the value of the objective function is within a preset threshold range, it is determined that the current optimal production scheduling plan is feasible and can be applied to the production of the target product. In the actual production process, the execution of the optimal production scheduling plan is monitored and necessary adjustments are made according to actual conditions.

[0096] In one embodiment, the target products include electronic products, auto parts and food seasonings, among which electronic products include motherboards, optical drives and printers, among which auto parts include engine parts, brake system parts and steering system parts, among which food seasonings include soy sauce, cooking oil and oyster sauce, among which

[0097] The implementation of the embodiments of the present invention has the following beneficial effects:

[0098] The embodiment of the present invention calculates the fitness of each individual in the population according to the constraints corresponding to the multi-objective function and the multi-constraint function, which is conducive to balancing different objectives and constraints in the case of multiple objectives and multiple constraints, can adapt to complex and changeable production scheduling problems, and determine the optimal production scheduling plan while optimizing the objective function and satisfying all business constraints, thereby effectively improving the production scheduling efficiency of the product.

[0099] Furthermore, the embodiments of the present invention can improve the fitness of individuals that do not meet the constraints, making them closer to the optimization goal, thereby increasing the chance of being selected, and sub-adjusting the individuals by adjusting the start time of the production order and the production line data can effectively improve the rationality of production resource allocation, thereby further improving the production efficiency of the product.

[0100] Figure 6 FIG. 2 is a schematic diagram showing the structure of an embodiment of a production scheduling device of the present invention. Figure 6 As shown, the device 600 includes: a population initialization module 610, a crossover mutation module 620, an iteration module 630 and an optimal production scheduling solution determination module 640.

[0101] Initialization population module 610, used to obtain target product production order data, and initialize the population based on the production order data, the population including multiple individuals;

[0102] The crossover and mutation module 620 is used to calculate the fitness of each individual in the population according to the objective function and the constraint function, determine the high-quality individuals and the low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; wherein the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function;

[0103] Iteration module 630, used for iteratively performing crossover and mutation operations on the next generation population until a stop iteration condition is met to obtain an optimal population;

[0104] The optimal production schedule determination module 640 is used to determine the production schedule corresponding to the individual with the highest fitness in the optimal population as the optimal production schedule

[0105] In one embodiment, initializing a population based on production order data includes:

[0106] The planning group order data is formed based on the production order data, and the planning group order data is copied and multiplied as an individual to form a new individual; wherein the planning group order is a group of production orders belonging to the same category;

[0107] When the number of individuals reaches the preset condition, the replication and reproduction of the individuals will stop, and all individuals will form a population.

[0108] In one embodiment, the production scheduling device further includes a constraint function building module, which is used to:

[0109] Constructing a capacity constraint function based on product production data, where the product production data includes production order data, BOM data, and process route data;

[0110] Constructing a resource conflict function based on product manufacturing data, wherein the product manufacturing data includes work calendar data, maintenance plan data, production line rate data, pipeline constraint configuration data, and production line conflict configuration data;

[0111] Constructing a material set function based on material supply data, where the material supply data includes material inventory data, material lead time data, and supplier supply plan data;

[0112] Building a product delivery satisfaction function based on product demand data, wherein the product demand data includes product delivery data, product monthly forecast data, and product daily shipment forecast data;

[0113] A warehouse capacity satisfaction function is constructed based on warehouse data, wherein the warehouse data includes warehouse capacity data and box / pallet conversion relationship data.

[0114] In one embodiment, the crossover mutation module 620 is further configured to:

[0115] The start time and end time of the production order are used as decision variables, and the fitness of each individual in the population is calculated according to the objective function and constraint function.

[0116] In one embodiment, the crossover mutation module 620 is further configured to:

[0117] Individual sub-adjustment: Individuals with fitness lower than the preset threshold are determined as individuals to be adjusted. When the individuals to be adjusted do not meet the constraints corresponding to all constraint functions, the production order start time and production line data in the individuals to be adjusted are adjusted to form new individuals, and the fitness of the new individuals is recalculated;

[0118] When the fitness of the new individual is higher than the preset threshold, or when the upper limit of the individual sub-adjustment times is reached, the individual sub-adjustment operation is stopped.

[0119] In one embodiment, the production scheduling device further includes a scheduling solution application module for:

[0120] When the optimal scheduling plan satisfies the constraints corresponding to all constraint functions and the value of the objective function is within a preset threshold range, the optimal production scheduling plan is applied to the production of the target product.

[0121] In one embodiment, the target product is soy sauce.

[0122] Figure 7 The schematic diagram of the structure of an embodiment of the production scheduling device of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the production scheduling device.

[0123] like Figure 7 As shown, the production scheduling device may include: a processor 702 , a communications interface 704 , a memory 706 , and a communication bus 708 .

[0124] The processor 702, the communication interface 704, and the memory 706 communicate with each other via the communication bus 708. The communication interface 704 is used to communicate with other devices such as a client or other server network elements. The processor 702 is used to execute the program 710, which can specifically execute the relevant steps in the above-mentioned production scheduling method embodiment.

[0125] Specifically, the program 710 may include program code including computer executable instructions.

[0126] The processor 702 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the production scheduling device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0127] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0128] Program 710 can be specifically called by processor 702 to enable the production scheduling device to perform operations such as the above-mentioned production scheduling method.

[0129] The present invention also provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction is executed on a production scheduling device / apparatus, the production scheduling device / apparatus performs the operations of the production scheduling method as described above.

[0130] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0131] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0132] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0133] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A production scheduling method, characterized in that: include: Obtaining target product production order data, and initializing a population based on the production order data, wherein the population includes a plurality of individuals; Crossover and mutation: Calculate the fitness of each individual in the population according to the objective function and constraint function, determine high-quality individuals and low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; wherein, the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function; Iteratively performing a crossover mutation operation on the next generation population until a stop iteration condition is met to obtain an optimal population; The production scheduling plan corresponding to the individual with the highest fitness in the optimal population is determined as the optimal production scheduling plan.

2. The production scheduling method according to claim 1, characterized in that: The initializing the population based on the production order data comprises: Based on the production order data, plan group order data is formed, and the plan group order data is copied and multiplied as an individual to form a new individual; wherein the plan group order is a group of production orders belonging to the same category; When the number of individuals reaches the preset condition, the replication and reproduction of the individuals will stop, and all individuals will form a population.

3. The production scheduling method according to claim 1, characterized in that: Also includes: Constructing the capacity constraint function based on product production data, wherein the product production data includes production order data, BOM data and process route data; Constructing the resource conflict function based on product manufacturing data, wherein the product manufacturing data includes work calendar data, maintenance plan data, production line rate data, pipeline constraint configuration data and production line conflict configuration data; Constructing the material set function based on material supply data, wherein the material supply data includes material inventory data, material lead time data and supplier supply plan data; Constructing the product delivery satisfaction function based on product demand data, wherein the product demand data includes product delivery data, product monthly forecast data and product daily shipment forecast data; The warehouse capacity satisfaction function is constructed based on warehouse data, wherein the warehouse data includes warehouse capacity data and box / pallet conversion relationship data.

4. The production scheduling method according to claim 1, wherein: The step of calculating the fitness of each individual in the population according to the objective function and the constraint function includes: The start time and the end time of the production order are used as decision variables, and the fitness of each individual in the population is calculated according to the objective function and the constraint function.

5. The production scheduling method according to claim 1, characterized in that: After calculating the fitness of each individual in the population according to the objective function and the constraint function, the method further includes: Individual sub-adjustment: determine the individuals whose fitness is lower than the preset threshold as the individuals to be adjusted. When the individuals to be adjusted do not meet the constraints corresponding to all constraint functions, adjust the production order start time and production line data in the individuals to be adjusted to form new individuals, and recalculate the fitness of the new individuals. When the fitness of the new individual is higher than a preset threshold, or when the upper limit of the individual sub-adjustment times is reached, the individual sub-adjustment operation is stopped.

6. The production scheduling method according to claim 1, characterized in that: After determining the production scheduling scheme corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling scheme, the method further includes: When the optimal production scheduling solution satisfies the constraint conditions corresponding to all constraint functions and the value of the objective function is within a preset threshold range, the optimal production scheduling solution is applied to the production of the target product.

7. A production scheduling device, characterized in that: include: An initialization population module is used to obtain target product production order data and initialize a population based on the production order data, wherein the population includes a plurality of individuals; A crossover and mutation module is used to calculate the fitness of each individual in the population according to the objective function and the constraint function, determine the high-quality individuals and the low-quality individuals according to the fitness, retain the high-quality individuals, and randomly select a preset number of low-quality individuals for crossover and mutation to obtain the next generation population; wherein the objective function includes the minimum total production time objective function, the minimum comprehensive cost objective function and the maximum delivery on-time rate objective function, and the constraint function includes the production capacity constraint function, the resource conflict function, the material completeness function, the product delivery satisfaction function and the warehouse capacity satisfaction function; An iteration module, used for iteratively performing a crossover and mutation operation on the next generation population until a stop iteration condition is met to obtain an optimal population; The optimal production scheduling scheme determination module is used to determine the production scheduling scheme corresponding to the individual with the highest fitness in the optimal population as the optimal production scheduling scheme.

8. A production scheduling device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the production scheduling method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction, and when the executable instruction is executed on the production scheduling device / apparatus, the production scheduling device / apparatus performs the operation of the production scheduling method as described in any one of claims 1-6.

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