Discrete assembly production scheduling method, device and equipment based on mixed linear integer programming

Abstract

The invention discloses a discrete assembly scheduling method, device and equipment based on mixed linear integer programming, relates to the technical field of information, and can solve the technical problems of low scheduling efficiency and insufficient accuracy. The method comprises the following steps: encoding a production scheduling gene, constructing a fitness function based on the production scheduling gene and a preset constraint condition, and constructing a target function based on the production scheduling gene; obtaining a to-be-scheduled order, preprocessing the to-be-scheduled order according to the preset constraint condition to obtain to-be-scheduled information, and constructing chromosomes based on the scheduling gene and the to-be-scheduled information; presetting an initial population for the chromosome, carrying out the iterative optimization of the initial population according to the fitness function, obtaining an excellent population, calculating a target population which enables the target function to be optimal in the excellent population, and taking the target population as a target production scheduling plan of the order to be scheduled.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a discrete assembly scheduling method, device and equipment based on mixed linear integer programming. Background Art

[0002] The reasonable arrangement of production planning and scheduling is what must be considered in the current refined information-based production. In order to reasonably arrange the production planning and scheduling, it is necessary to understand every production detail of the enterprise in detail, establish the relationship between materials, processes, resources, time, logical relationships, technical parameters, costs, BOM (Bill of Material), etc., and iterate repeatedly based on these production details to find the optimal production schedule. In this process, a lot of information is easily missed, and iterative search will consume a lot of time.

[0003] Therefore, there is an urgent need for a production scheduling method that can improve production scheduling efficiency and accuracy. Summary of the invention

[0004] In view of this, the present invention provides a discrete assembly scheduling method, device and equipment based on mixed linear integer programming, which can solve the technical problems of low scheduling efficiency and insufficient accuracy.

[0005] According to one aspect of the present invention, a discrete assembly scheduling method based on hybrid linear integer programming is provided, the method comprising:

[0006] Encoding a production scheduling gene, constructing a fitness function based on the production scheduling gene and preset constraints, and constructing an objective function based on the production scheduling gene;

[0007] Acquire the production order to be scheduled, pre-process the production order to be scheduled according to the preset constraint condition to obtain the production information to be scheduled, and construct a chromosome based on the production scheduling gene and the production information to be scheduled;

[0008] An initial population is preset for the chromosome, and the initial population is iteratively optimized according to the fitness function to obtain an excellent population, and a target population that makes the objective function optimal is calculated among the excellent populations, and the target population is used as the target production schedule for the orders to be scheduled.

[0009] According to another aspect of the present invention, a discrete assembly scheduling device based on mixed linear integer programming is provided, wherein the device comprises:

[0010] An encoding module, used for encoding a production scheduling gene, constructing a fitness function based on the production scheduling gene and preset constraints, and constructing an objective function based on the production scheduling gene;

[0011] An acquisition module, used for acquiring orders to be scheduled, preprocessing the orders to be scheduled according to the preset constraints to obtain information to be scheduled, and constructing chromosomes based on the scheduling genes and the information to be scheduled;

[0012] A calculation module is used to preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function to obtain an excellent population, calculate a target population among the excellent population that makes the objective function optimal, and use the target population as the target production schedule for the order to be scheduled.

[0013] According to another aspect of the present invention, there is provided a storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the discrete assembly scheduling method based on mixed linear integer programming is implemented.

[0014] According to another aspect of the present invention, there is provided a computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned discrete assembly scheduling method based on mixed linear integer programming when executing the program.

[0015] By means of the above technical scheme, the present invention provides a discrete assembly scheduling method, device and equipment based on mixed linear integer programming. The present invention can first encode the scheduling gene, construct a fitness function based on the scheduling gene and preset constraints, and construct an objective function based on the scheduling gene; then obtain the order to be scheduled, pre-process the order to be scheduled according to the preset constraints, obtain the information to be scheduled, and construct a chromosome based on the scheduling gene and the information to be scheduled; finally, preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function, obtain an excellent population, calculate the target population in the excellent population that makes the objective function optimal, and use the target population as the target scheduling plan for the order to be scheduled. Through the technical scheme in the present invention, the scheduling gene is encoded and the preset constraints are pre-set, so that after obtaining the order to be scheduled, the order to be scheduled can be pre-processed under the guidance of the scheduling gene and the preset constraints, solving the problem of insufficient scheduling accuracy caused by missing information. In order to obtain the target production plan for the orders to be scheduled, chromosomes are constructed through the production scheduling gene and the information to be scheduled, and the fitness function is constructed based on the production scheduling gene and the preset constraints, so that iterative optimization is carried out strictly according to the actual situation of the information to be scheduled, which improves the accuracy of production scheduling. The scheduling efficiency is improved by iterative optimization of the production scheduling gene, chromosome, fitness function and objective function that are suitable for discrete assembly scheduling scenarios.

[0016] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages 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

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation on the local application. In the drawings:

[0018] Figure 1 A schematic flow chart of a discrete assembly scheduling method based on hybrid linear integer programming provided by an embodiment of the present invention is shown;

[0019] Figure 2 A schematic flow chart of another discrete assembly scheduling method based on hybrid linear integer programming provided by an embodiment of the present invention is shown;

[0020] Figure 3 A schematic structural diagram of a discrete assembly production scheduling device based on mixed linear integer programming provided by an embodiment of the present invention is shown;

[0021] Figure 4 A schematic structural diagram of another discrete assembly scheduling device based on mixed linear integer programming provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0023] This embodiment provides a discrete assembly scheduling method based on hybrid linear integer programming, such as Figure 1 As shown, the method includes:

[0024] 101. Encode a production scheduling gene, construct a fitness function based on the production scheduling gene and preset constraints, and construct an objective function based on the production scheduling gene.

[0025] 102. Obtain the production orders to be scheduled, pre-process the production orders to be scheduled according to the preset constraints to obtain the production information to be scheduled, and construct chromosomes based on the production scheduling genes and the production information to be scheduled.

[0026] 103. Preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function to obtain an excellent population, calculate a target population among the excellent populations that optimizes the objective function, and use the target population as a target production schedule for the orders to be scheduled.

[0027] For example steps 101-103, for the production scheduling gene:

[0028] First, because the genetic algorithm constructs chromosomes from genes in the iterative optimization process, and then iterates and optimizes on the basis of chromosomes, it is necessary to encode the production scheduling genes corresponding to the production scheduling scenario. Secondly, in addition to being used to construct chromosomes, production scheduling genes are also used to construct fitness functions, and the role of fitness functions is to screen out multiple groups of excellent populations. Therefore, production scheduling genes are variables related to production scheduling efficiency in production scheduling scenarios, such as using the minimum inventory and the minimum time to complete the maximum output. Among them, the variables related to production scheduling efficiency are inventory, time, and output, so production scheduling genes are inventory, time, and output.

[0029] For preset constraints:

[0030] In the production scheduling scenario, the workshop has a lot of detailed information, and these detailed information have constraints on each other. For example, the process of some products can only be produced on a specific production line, so other production lines cannot provide production for these products; the process is sorted by process, so the process must be executed in the order of the process, so in terms of execution time, the end time of the previous process must be less than or equal to the start time of the next process; the product has a delivery date restriction, and the product with a short delivery date must be produced first, so the production of products has a priority order, etc. These constraints affect the subsequent formulation of the target production schedule for the orders to be scheduled. Therefore, in the iterative optimization process of population screening, these constraints must be put into the fitness function to be more in line with the actual production scheduling scenario, thereby improving the accuracy of population screening. Therefore, preset constraints are constructed by these constraints, and these preset constraints are used to construct the fitness function. In addition, in order to iteratively optimize the production orders to be scheduled, it is necessary to process the production orders to be scheduled in a targeted manner and obtain the information required for iterative optimization from the production orders to be scheduled. Therefore, it is necessary to pre-process the production orders to be scheduled according to the preset constraints to obtain the information to be scheduled.

[0031] For the objective function:

[0032] The role of the fitness function is to screen out multiple excellent populations, and the role of the objective function is to screen out a target population from these excellent populations. This target population is a set of specific solutions of chromosomes, that is, the target production schedule for the orders to be scheduled.

[0033] For chromosomes and initial populations:

[0034] Chromosomes are variable expressions. The initial populations preset for chromosomes are specific values. During iterative optimization, these initial populations are used as objects.

[0035] The present invention provides a discrete assembly scheduling method, device and equipment based on mixed linear integer programming. The present invention can first encode the scheduling gene, construct a fitness function based on the scheduling gene and preset constraints, and construct an objective function based on the scheduling gene; then obtain the order to be scheduled, pre-process the order to be scheduled according to the preset constraints, obtain the information to be scheduled, and construct a chromosome based on the scheduling gene and the information to be scheduled; finally, preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function, obtain an excellent population, calculate the target population that makes the objective function optimal in the excellent population, and use the target population as the target scheduling plan for the order to be scheduled. Through the technical solution of the present invention, the scheduling gene is encoded and the preset constraints are pre-set, so that after obtaining the order to be scheduled, the order to be scheduled can be pre-processed under the guidance of the scheduling gene and the preset constraints, solving the problem of insufficient scheduling accuracy caused by missing information. In order to obtain the target production plan for the orders to be scheduled, chromosomes are constructed through the production scheduling gene and the information to be scheduled, and the fitness function is constructed based on the production scheduling gene and the preset constraints, so that iterative optimization is carried out strictly according to the actual situation of the information to be scheduled, which improves the accuracy of production scheduling. The scheduling efficiency is improved by iterative optimization of the production scheduling gene, chromosome, fitness function and objective function that are suitable for discrete assembly scheduling scenarios.

[0036] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another discrete assembly scheduling method based on mixed linear integer programming is provided, such as Figure 2 As shown, the method includes:

[0037] 201. Encoding genes for production scheduling.

[0038] It should be noted that for the steps 201-205 of the embodiment, in the production scheduling scenario of a discrete assembly workshop, for example, the purpose is to calculate the target production schedule for the orders to be scheduled. In the process of calculating the target production schedule, the continuous variable of time is involved, and integer variables such as workstations, product types, and production quantities are also involved, and there are linear preset constraints. Therefore, it is a mixed linear integer programming problem. In the case of a wide variety of products, complex production processes, and insufficient inventory, it is difficult to solve directly. Therefore, by adopting the operation of cross-mutation of the initial population and screening with the fitness function constructed based on the preset constraints, the mixed linear integer programming problem can be efficiently solved, and the excellent population can be quickly solved, so as to select the target population that optimizes the objective function from the excellent population, that is, the target production schedule.

[0039] For this embodiment, as an implementation method, the production scheduling gene includes the start time s of the wth batch of product i at the jth station on the production line k. iwkj , the end time e of batch w of product i at station j on production line k iwkj , the production quantity n of product i on production line k ik 、The number of accessory q required for product i b iq . Among them, i=1,2,…,x; j=1,2,…,u k ;k=1,2,…,z; w=1,2,…,n ik .

[0040] 202. Construct a fitness function based on the production scheduling gene and preset constraints, and construct an objective function based on the production scheduling gene.

[0041] For this embodiment, as an implementation method, the preset constraint condition includes: the unfinished quantity of product i is equal to the planned production quantity of product i minus the total production quantity of product i on all production lines that can produce: Among them, use a ik Indicates whether product i is produced on production line k. If the value is 0, it means that product i is not produced on production line k. If the value is 1, it means that product i is produced on production line k. z is the total number of production lines.

[0042] The unfinished quantity of product i is greater than or equal to zero:

[0043] The remaining inventory of accessory q is equal to the inventory of accessory q minus the demand for accessory q: Among them, b iq : The number of units required for accessory q required for product i.

[0044] The remaining inventory of accessory q is greater than or equal to zero:

[0045] The actual production time e of the wth batch of product i at the jth station on production line k iwkj -s iwkj Less than or equal to its standard time: e iwkj -s iwkj ≤t ikj γ, where t ikj : The standard time for product i to reach the jth station on production line k, γ: A random number that conforms to the normal distribution and is used to simulate actual production, γ~N(1,σ 2 ), γ is greater than 0.

[0046] The total working time spent on producing all products at a workstation on a production line is less than or equal to the end time e iwkj Maximum value of - start time s iwkj Minimum value (i.e. idle time is allowed for production at the workstation):

[0047] Product i produced on the same production line meets the preset production sequence, for example, the product has a delivery priority: e iwkj ≤s i(w+1)kj

[0048] The workstations are produced according to the process: iwkj ≤s iwk(j+1)

[0049] The same station on the same production line cannot process different products at the same time: max(s iwkj ,s (i+l)wkj )+min(t ikj ,t (i+l)kj )≤max(e ikj ,e (i+l)wkj )

[0050] The production quantity of product i on production line k is greater than or equal to zero: n ik ≥0,

[0051] The starting time s of the wth batch of product i at station j on production line k iwkj Greater than or equal to the preset allowed start time of production line k: s iwkj ≥st k

[0052] For this embodiment, based on the production scheduling gene construction: (1) the total production time is minimized:

[0053] (2) The number of unfinished products for all the products mentioned is the smallest:

[0054] (3) The remaining inventory of all the above accessories is the smallest:

[0055] The constructing of the fitness function based on the production scheduling gene and the preset constraints includes: constructing the fitness function according to the minimum unfinished quantity of all the products, the minimum remaining inventory of all the accessories, the minimum total production time and the corresponding preset constraints;

[0056] Among them, the fitness function is L and the preset constraints, L is:

[0057]

[0058] λ i is the unfinished quantity M of product i i The first preset weight coefficient, μ q The remaining N of the spare parts inventory of spare part q q A second preset weight coefficient of .

[0059] The constructing of the objective function based on the production scheduling gene includes: constructing the objective function according to the minimum total production time.

[0060] Objective function:

[0061] 203. Obtain the production order to be scheduled, and pre-process the production order to be scheduled according to the preset constraint conditions to obtain the production information to be scheduled.

[0062] For this embodiment, the production orders to be scheduled are preprocessed according to the preset constraints to obtain the information to be scheduled, including: obtaining the product types included in the production orders to be scheduled, and determining the preset production sequence according to the delivery time corresponding to each product; obtaining the initial planned production quantity and finished product inventory corresponding to each product, and subtracting the finished product inventory from the initial planned production quantity corresponding to the same product to obtain the planned production quantity corresponding to each product; obtaining the total number of production lines and the number of workstations in each production line to determine the product types that the production line can produce; obtaining the process of each product, the accessories required for each product, the required number of each accessory and the accessory inventory of each accessory; obtaining the production batch; determining the product type, the preset production sequence, the planned production quantity, the total number of production lines, the number of workstations, the product types that the production line can produce, the process, the accessories, the required number, the accessory inventory and the production batch as the information to be scheduled.

[0063] 204. List the product types, the total number of production lines, the number of workstations, the accessories, and the production batches in the information to be scheduled according to the production scheduling gene to obtain chromosomes.

[0064] For example, if there are 3 types of products, then they are listed from p1 to p3. If the total number of production lines is 3, then they are listed from k1 to k3. If the number of workstations is 3, then they are listed from j1 to j3. If there are 2 types of accessories, then they are listed from q1 to q2. If the production batch of product p1 is 4 batches, then product p1 is listed from l1 to l4. If the production batch of product p2 is 1 batch, then product p2 only has l1.

[0065]

[0066] 205. Preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function to obtain an excellent population, calculate a target population among the excellent populations that optimizes the objective function, and use the target population as a target production schedule for the orders to be scheduled.

[0067] The iterative optimization of the initial population according to the fitness function to obtain an excellent population includes: taking the initial population as a parent, performing a crossover operation on different parent generations to obtain offspring, performing a mutation operation on the same offspring to obtain an initial new offspring, judging whether the initial new offspring passes a preset verification rule, if not, repairing the initial new offspring until it passes the preset verification rule to obtain a new offspring to be calculated, and if so, determining the initial new offspring as the new offspring to be calculated; calculating the fitness function value corresponding to the new offspring to be calculated, screening the new offspring to be calculated according to the fitness function value to obtain an intermediate new offspring, determining whether the iterative optimization is completed according to the fitness function value, if so, determining the intermediate new offspring as an excellent population, and if not, using the intermediate new offspring as a new parent, and continuing the iterative optimization until the iterative optimization is completed to obtain the excellent population.

[0068] For the initial population:

[0069] Chromosomes are listed variable expressions, and the initial population is the value of the variable expression. For example, the size of the initial population is 500. These 500 are used as the first generation, that is, the parent generation. The size of the offspring after the crossover operation is 500. After the mutation operation, the initial new offspring is greater than 500, for example, 600. Regardless of whether it is repaired or not, the new offspring to be calculated are 600. The fitness function value is used to screen 500 intermediate new offspring from the 600 new offspring to be calculated (the size of the intermediate new offspring is the same as the size of the initial population).

[0070] When taking values ​​for chromosomes, that is, when the initial population is preset for chromosomes, as an implementation method, the products can be fully produced on the production line in the order that meets the inventory, so that the production quantity of the front products can be met first. For example, there are a total of p1, p2, p3, and p4 products. The first initial population is: all products are produced on production line k1, and the other production lines are empty. The second initial population is: put any one of p1, p2, p3, and p4 on k2 for production, and the rest are left on k1 for production, and the other production lines are empty. The third initial population is: replace the product on production line k2 in the second initial population. For example, the second initial population puts p1 on k2 for production, and p2, p3, and p4 are left on k1 for production. Then, for the third initial population, select one from p2, p3, and p4, for example, p2 replaces p1 to produce on k2, so that p2 is produced on k2, p1, p3, and p4 are produced on k1, and the other production lines are empty. And so on, multiple initial populations are obtained.

[0071] For crossover and mutation:

[0072] A crossover is between two different parent generations, resulting in two offspring, while a mutation operation is performed within each offspring, and a mutation is performed within any offspring to obtain an initial new offspring. And whether it is a crossover or a mutation, it is preferred that the production line is used as the boundary.

[0073] The process of a crossover is: select any two different parents, such as parent 1 and parent 2, select any production line to be exchanged, such as production line k1, pack all the values ​​of the production scheduling genes of parent 1 related to production line k1 (the values ​​of all the production scheduling genes of parent 1 related to production line k1, that is, the initial population of all the production scheduling genes related to production line k1 of parent 1) to obtain package 1, pack all the production scheduling genes of parent 2 related to production line k1 to obtain package 2, exchange package 1 of parent 1 with package 2 of parent 2. After the exchange, the production line k1 of parent 1 is package 2, and the production line k1 of parent 2 is package 1.

[0074] The process of a mutation is: select any one child, for example, child 1, select any two different production lines, for example, production line k1 and production line k2, and transfer part or all of a product (for example, p1) on production line k2 to production line k1 for production. Specifically, the transfer can be done by adding batches. For example, the production of product p1 on production line k1 includes three batches l1, l2, and l3, and the production of product p1 on production line k2 includes three batches. Then, the mutation is to transfer two batches (it can be one batch or three batches, which are not limited here) of product p1 on production line k2 to production line k1. After the mutation, the production of product p1 on production line k1 includes three batches l1, l2, l3, l4, and l5, and the production of product p1 on production line k2 includes only one batch.

[0075] For verification and repair:

[0076] After the crossover and mutation operations are performed on the parent generation, there may be some conflicts. Such conflicts will affect the subsequent screening of the fitness function. Therefore, it is necessary to first determine whether there is a conflict. If so, repair it. If not, there is no need to perform the repair step. The generation without conflict will be used as the new generation to be calculated and screened by the fitness function.

[0077] The judging whether the initial new offspring passes the preset verification rules, and if not, repairing the initial new offspring until the preset verification rules are passed to obtain the new offspring to be calculated, includes: the preset verification rules include preset time verification rules and preset inventory verification rules, wherein the preset time verification rules are: whether there is overlap in the start and end time periods of different stations of batch l of product i on production line k, and whether there is overlapping production time at the same station on production line k, and the preset inventory verification rules are: whether the demand number of accessory q of product i on production line k is less than or equal to the accessory inventory of accessory q on production line k; if there is overlap in the start and end time periods of different stations of the same production line, the same product and the same batch overlap, then any one of the start and end time periods of the overlapping stations is postponed to a non-overlapping position, if there is overlapping production time at the same station on the same production line, then any one of the overlapping production times is postponed to a non-overlapping position; if the preset inventory verification rules are not passed, the demand number of accessory q of product i on other production lines is deleted.

[0078] Among them, this conflict includes time conflict and inventory conflict.

[0079] For time conflict: Since the above preferred implementation is to replace the entire production line with the production line as the boundary, there is no time conflict. If it is not in accordance with the above preferred implementation, there may be time conflicts. For example, after crossing, if there is time overlap between different stations (for example, there is overlap between time period 1 at the start and end of station 1 and time period 2 at the start and end of station 2), and the stations must follow the order of the process, one can complete the next, which means that the process is not carried out in the order of the process. Therefore, the offspring obtained by this crossing is an unqualified offspring, and it is necessary to repair this unqualified offspring. The specific repair is that when overlap is found, if the overlap is the start and end time period of different stations, any one of the start and end time periods of the overlapping stations is postponed to a non-overlapping position. As an implementation method, any one of the overlapping stations is postponed to the last station of the same production line k, the same product i, and the same batch l. Since the stations have been calibrated, as long as there is no overlap, the stations strictly follow the order of the process. If the overlapping production time is of the same workstation, that is, only one production task corresponding to one production time can be completed at the same workstation, then any one of the overlapping production times will be postponed to a non-overlapping position. As an implementation method, all workstations after this workstation will be postponed as a whole until any one of the overlapping production times is postponed to between this workstation and the workstation after this workstation, and there is no overlap in production time.

[0080] Regarding inventory conflicts, for example, in the above embodiment, part or all of a product (for example, p1) on production line k2 is transferred to production line k1 for production, and the original requirement on production line k1 is to produce 100 p1s, and the corresponding inventory demand on production line k1 is to produce 100 p1s. For example, if 50 p1s are transferred to production line k1, the inventory on production line k1 is not enough. Therefore, repair is needed. The specific repair method is to delete the original demand for inventory on production line k2.

[0081] When using the fitness function for screening, due to the evaluation criteria of minimizing the total production time, minimizing the number of unfinished products of all the products, and minimizing the remaining inventory of all the accessories, when the production quantity in the new generation to be calculated exceeds the expected value, the inventory demand exceeds the existing inventory, the production time conflicts, etc., its fitness is reduced, thereby screening out those with high fitness.

[0082] One traditional selection strategy is to set a fixed fitness threshold, retain populations with a fitness threshold greater than or equal to the fitness threshold, and eliminate populations with a fitness threshold less than the fitness threshold. However, in the production scheduling scenario, due to different BOMs and processes for different products, there are also large differences in production time on different production lines and different workstations. That is, the value of the fitness function is affected by multiple variables and changes at any time. Therefore, it is impossible to determine a fixed fitness function threshold, resulting in the method of setting a fixed fitness threshold not being applicable to the production scheduling scenario. Another traditional selection strategy is to calculate a dynamic fitness threshold each time. This method increases the amount of calculation and reduces the iteration efficiency. Therefore, when using the fitness function for screening, the fitness function values ​​of the new offspring to be calculated are sorted, and the ones ranked first are retained, and the ones ranked later are eliminated.

[0083] After the crossover operation is performed and the offspring is obtained, the traditional genetic mutation strategy is different from the present embodiment applied to the production scheduling scenario. The traditional genetic mutation strategy does not generate new individuals after the offspring is mutated, that is, the size of the offspring remains the same after the mutation, while the present embodiment applied to the production scheduling scenario generates new individuals after the offspring is mutated, that is, the size of the offspring is increased. The advantage of the present embodiment is that, in one mutation operation, the population diversity is improved compared to the traditional genetic mutation strategy, and the final excellent population can be screened from a more diverse population, thereby improving the accuracy and efficiency of screening.

[0084] The method of determining whether the iterative optimization is completed according to the fitness function value, and if so, determining the intermediate new offspring as an excellent population, as an implementation method, includes: calculating the degree of deviation between the fitness function values, and if the degree of deviation is less than a preset threshold, completing the iterative optimization, and determining the intermediate new offspring as an excellent population. The degree of deviation may be a variance or a standard deviation. If the degree of deviation is less than the preset threshold, it means that the fitness function values ​​of the intermediate new offspring are not much different and have converged, thus indicating that the iterative optimization is completed. As another implementation method, a maximum number of iterations is preset, and when the maximum number of iterations is reached, it can indicate that the iterative optimization is completed.

[0085] The present invention provides a discrete assembly scheduling method, device and equipment based on mixed linear integer programming. The present invention can first encode the scheduling gene, construct a fitness function based on the scheduling gene and preset constraints, and construct an objective function based on the scheduling gene; then obtain the order to be scheduled, pre-process the order to be scheduled according to the preset constraints, obtain the information to be scheduled, and construct a chromosome based on the scheduling gene and the information to be scheduled; finally, preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function, obtain an excellent population, calculate the target population that makes the objective function optimal in the excellent population, and use the target population as the target scheduling plan for the order to be scheduled. Through the technical solution of the present invention, the scheduling gene is encoded and the preset constraints are pre-set, so that after obtaining the order to be scheduled, the order to be scheduled can be pre-processed under the guidance of the scheduling gene and the preset constraints, solving the problem of insufficient scheduling accuracy caused by missing information. In order to obtain the target production plan for the orders to be scheduled, chromosomes are constructed through the production scheduling gene and the information to be scheduled, and the fitness function is constructed based on the production scheduling gene and the preset constraints, so that iterative optimization is carried out strictly according to the actual situation of the information to be scheduled, which improves the accuracy of production scheduling. The scheduling efficiency is improved by iterative optimization of the production scheduling gene, chromosome, fitness function and objective function that are suitable for discrete assembly scheduling scenarios.

[0086] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in the figure, the embodiment of the present invention provides a discrete assembly scheduling device based on mixed linear integer programming, such as Figure 3 As shown, the device includes: an encoding module 31, an acquisition module 32, and a calculation module 33;

[0087] The encoding module 31 may be used to encode the production scheduling gene, construct a fitness function based on the production scheduling gene and preset constraints, and construct an objective function based on the production scheduling gene;

[0088] The acquisition module 32 may be used to acquire the production order to be scheduled, pre-process the production order to be scheduled according to the preset constraint conditions to obtain the production information to be scheduled, and construct a chromosome based on the production scheduling gene and the production information to be scheduled;

[0089] The calculation module 33 can be used to preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function to obtain an excellent population, calculate the target population among the excellent population that makes the objective function optimal, and use the target population as the target production schedule for the orders to be scheduled.

[0090] Correspondingly, in order to iteratively optimize the initial population according to the fitness function and obtain an excellent population, the calculation module 33 can be specifically used to: take the initial population as the parent generation, perform a crossover operation on different parent generations to obtain offspring, perform a mutation operation on the same offspring to obtain an initial new offspring, determine whether the initial new offspring passes the preset verification rule, if not, repair the initial new offspring until it passes the preset verification rule to obtain a new offspring to be calculated, if so, determine the initial new offspring as the new offspring to be calculated; calculate the fitness function value corresponding to the new offspring to be calculated, screen the new offspring to be calculated according to the fitness function value to obtain an intermediate new offspring, determine whether the iterative optimization is completed according to the fitness function value, if so, determine the intermediate new offspring as an excellent population, if not, use the intermediate new offspring as a new parent generation, and continue iterative optimization until the iterative optimization is completed to obtain the excellent population.

[0091] Correspondingly, the production scheduling gene includes the start time s of the wth batch of product i at the jth station on the production line k iwkj , the end time e of batch w of product i at station j on production line k iwkj , the production quantity n of product i on production line k ik 、The number of accessory q required for product i b iq The preset constraints include: the unfinished quantity of product i is equal to the planned production quantity of product i minus the total production quantity of product i on all production lines, and the unfinished quantity of product i is greater than or equal to zero; the remaining inventory of accessory q is equal to the inventory of accessory q minus the demand for accessory q, and the remaining inventory of accessory q is greater than or equal to zero; the actual production time e of the wth batch of product i at the jth station on the kth production line is iwkj -s iwkj is less than or equal to its standard time, whether product i is produced on production line k, and the total working time spent on producing all products at a station on a production line is less than or equal to the end time e iwkj Maximum value of - start time s iwkj The minimum value of , product i produced on the same production line meets the preset production sequence, the workstations produce according to the process, the same workstation on the same production line cannot process different products at the same time, the production quantity of product i on production line k is greater than or equal to zero, and the start time s of the wth batch of product i at workstation j on production line k is iwkjGreater than or equal to the preset allowed start time of production line k; in order to construct a fitness function based on the production scheduling gene and preset constraints, the encoding module 31 can be used specifically to: construct a fitness function based on the minimum number of unfinished products of all the products, the minimum remaining inventory of all the accessories, the minimum total production time and the corresponding preset constraints; in order to construct an objective function based on the production scheduling gene, the encoding module 31 can be used specifically to: construct an objective function based on the minimum total production time.

[0092] Correspondingly, in order to pre-process the production orders to be scheduled according to the preset constraints and obtain the production information to be scheduled, the acquisition module 32 can be specifically used to: obtain the product types included in the production orders to be scheduled, and determine the preset production sequence according to the delivery time corresponding to each product; obtain the initial planned production quantity and finished product inventory corresponding to each product, and subtract the finished product inventory from the initial planned production quantity corresponding to the same product to obtain the planned production quantity corresponding to each product; obtain the total number of production lines and the number of workstations in each production line to determine the product types that the production line can produce; obtain the process of each product, the accessories required for each product, the required number of each accessory and the accessory inventory of each accessory; obtain the production batch; determine the product type, the preset production sequence, the planned production quantity, the total number of production lines, the number of workstations, the product type that the production line can produce, the process, the accessories, the required number, the accessory inventory and the production batch as the production information to be scheduled.

[0093] Correspondingly, in order to construct a chromosome based on the production scheduling gene and the information to be scheduled, the acquisition module 32 can be specifically used to: list the product types, the total number of production lines, the number of workstations, the accessories, and the production batches in the information to be scheduled according to the production scheduling gene to obtain a chromosome.

[0094] Correspondingly, in order to determine whether the initial new generation passes the preset verification rule, if not, the initial new generation is repaired until the preset verification rule is passed to obtain the new generation to be calculated, the calculation module 33 specifically includes: a judgment unit 331, which can be specifically used for the preset verification rule including a preset time verification rule and a preset inventory verification rule, wherein the preset time verification rule is: whether there is overlap in the start and end time periods of different stations for batch l of product i on production line k, and whether there is overlapping production time at the same station on production line k, and the preset inventory verification rule is: whether the number of requirements for accessory q of product i on production line k is less than or equal to the accessory inventory of accessory q on production line k; if the start and end time periods of different stations of the same production line, the same product and the same batch overlap, then any one of the start and end time periods of the overlapping stations is postponed to a non-overlapping position, if there is overlapping production time at the same station on the same production line, then any one of the overlapping production times is postponed to a non-overlapping position; if the preset inventory verification rule is not passed, the number of requirements for accessory q of product i on other production lines is deleted.

[0095] Correspondingly, in order to determine whether the iterative optimization is completed according to the fitness function value, if so, the intermediate new offspring is determined as an excellent population, the calculation module 33 specifically includes: a determination unit 332, which can be used to calculate the degree of deviation between the fitness function values. If the degree of deviation is less than a preset threshold, the iterative optimization is completed and the intermediate new offspring is determined as an excellent population.

[0096] It should be noted that for other corresponding descriptions of the functional units involved in the discrete assembly scheduling device based on mixed linear integer programming provided in this embodiment, reference can be made to Figure 1 to Figure 2 The corresponding description will not be repeated here.

[0097] Based on the above Figure 1 to Figure 2 The method shown in the embodiment, accordingly, also provides a storage medium, which can be volatile or non-volatile, and stores a computer program, which is executed by a processor to implement the above-mentioned Figure 1 to Figure 2 The discrete assembly scheduling method based on mixed linear integer programming is shown.

[0098] Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0099] Based on the above Figure 1 to Figure 2The method shown and Figure 3 , Figure 4 In order to achieve the above-mentioned purpose, the present embodiment further provides a computer device, which includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 to Figure 2 The discrete assembly scheduling method based on mixed linear integer programming is shown.

[0100] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0101] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0102] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned computer device and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the non-volatile storage medium, and communication with other hardware and software in the information processing entity device.

[0103] Through the description of the above implementation modes, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0104] The present invention provides a discrete assembly scheduling method, device and equipment based on mixed linear integer programming. The present invention can first encode the scheduling gene, construct a fitness function based on the scheduling gene and preset constraints, and construct an objective function based on the scheduling gene; then obtain the order to be scheduled, pre-process the order to be scheduled according to the preset constraints, obtain the information to be scheduled, and construct a chromosome based on the scheduling gene and the information to be scheduled; finally, preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function, obtain an excellent population, calculate the target population that makes the objective function optimal in the excellent population, and use the target population as the target scheduling plan for the order to be scheduled. Through the technical solution of the present invention, the scheduling gene is encoded and the preset constraints are pre-set, so that after obtaining the order to be scheduled, the order to be scheduled can be pre-processed under the guidance of the scheduling gene and the preset constraints, solving the problem of insufficient scheduling accuracy caused by missing information. In order to obtain the target production plan for the orders to be scheduled, chromosomes are constructed through the production scheduling gene and the information to be scheduled, and the fitness function is constructed based on the production scheduling gene and the preset constraints, so that iterative optimization is carried out strictly according to the actual situation of the information to be scheduled, which improves the accuracy of production scheduling. The scheduling efficiency is improved by iterative optimization of the production scheduling gene, chromosome, fitness function and objective function that are suitable for discrete assembly scheduling scenarios.

[0105] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present invention. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0106] The above serial numbers of the present invention are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present invention, but the present invention is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A discrete assembly scheduling method based on hybrid linear integer programming, characterized in that: The method comprises: Encoding a production scheduling gene, constructing a fitness function based on the production scheduling gene and preset constraints, and constructing an objective function based on the production scheduling gene; Acquire the production order to be scheduled, pre-process the production order to be scheduled according to the preset constraint condition to obtain the production information to be scheduled, and construct a chromosome based on the production scheduling gene and the production information to be scheduled; An initial population is preset for the chromosome, and the initial population is iteratively optimized according to the fitness function to obtain an excellent population, and a target population that makes the objective function optimal is calculated among the excellent populations, and the target population is used as the target production schedule for the orders to be scheduled.

2. The method according to claim 1, characterized in that The iterative optimization of the initial population according to the fitness function to obtain an excellent population includes: Taking the initial population as the parent generation, performing a crossover operation on different parent generations to obtain offspring, performing a mutation operation on the same offspring to obtain an initial new offspring, judging whether the initial new offspring passes a preset verification rule, if not, repairing the initial new offspring until it passes the preset verification rule to obtain a new offspring to be calculated, if yes, determining the initial new offspring as the new offspring to be calculated; Calculate the fitness function value corresponding to the new offspring to be calculated, screen the new offspring to be calculated according to the fitness function value to obtain the intermediate new offspring, determine whether the iterative optimization is completed according to the fitness function value, if so, determine the intermediate new offspring as an excellent population, if not, use the intermediate new offspring as a new parent generation, and continue iterative optimization until the iterative optimization is completed to obtain the excellent population.

3. The method according to claim 1, characterized in that: The production scheduling gene includes the start time s of the wth batch of product i at the jth station on the production line k iwkj , the end time e of batch w of product i at station j on production line k iwkj , the production quantity n of product i on production line k ik 、The number of accessory q required for product i b iq ; The preset constraints include: the unfinished quantity of product i is equal to the planned production quantity of product i minus the total production quantity of product i on all production lines, and the unfinished quantity of product i is greater than or equal to zero; the remaining inventory of accessory q is equal to the inventory of accessory q minus the demand for accessory q, and the remaining inventory of accessory q is greater than or equal to zero; the actual production time e of the wth batch of product i at the jth station on the production line k is iwkj -s iwkj is less than or equal to its standard time, whether product i is produced on production line k, and the total working time spent on producing all products at a station on a production line is less than or equal to the end time e iwkj Maximum value of - start time s iwkj The minimum value of , product i produced on the same production line meets the preset production sequence, the workstations produce according to the process, the same workstation on the same production line cannot process different products at the same time, the production quantity of product i on production line k is greater than or equal to zero, and the start time s of the wth batch of product i at workstation j on production line k is iwkj Greater than or equal to the preset allowed start time of production line k; The constructing of a fitness function based on the production scheduling gene and preset constraints includes: Constructing a fitness function based on the minimum unfinished quantity of all the products, the minimum remaining inventory of all the accessories, the minimum total production time and the corresponding preset constraints; The constructing of the objective function based on the production scheduling gene comprises: An objective function is constructed based on minimizing the total production time.

4. The method according to claim 3, characterized in that: The pre-processing of the production order to be scheduled according to the preset constraint condition to obtain the production information to be scheduled includes: Obtain the product types included in the production order to be scheduled, and determine the preset production sequence according to the delivery time corresponding to each product; Obtaining the initial planned production quantity and finished product inventory corresponding to each product, and subtracting the finished product inventory from the initial planned production quantity corresponding to the same product to obtain the planned production quantity corresponding to each product; Obtain the total number of production lines and the number of workstations in each production line to determine the types of products that can be produced by the production line; Obtaining the process of each product, the accessories required for each product, the required quantity of each accessory, and the accessories inventory of each accessory; Get production batches; The product types, the preset production sequence, the planned production quantity, the total number of production lines, the number of workstations, the types of products that the production line can produce, the processes, the accessories, the required number, the accessories inventory and the production batches are determined as the information to be scheduled.

5. The method according to claim 4, characterized in that The step of constructing a chromosome based on the production scheduling gene and the production scheduling information includes: The product types, the total number of production lines, the number of workstations, the accessories, and the production batches in the information to be scheduled are listed according to the scheduling gene to obtain chromosomes.

6. The method according to claim 3, characterized in that The determining whether the initial new offspring passes a preset verification rule, and if not, repairing the initial new offspring until the preset verification rule is passed to obtain a new offspring to be calculated, includes: The preset verification rule includes a preset time verification rule and a preset inventory verification rule, wherein the preset time verification rule is: whether the start and end time periods of different workstations of batch l of product i on production line k overlap, and whether there is overlapping production time at the same workstation on production line k; the preset inventory verification rule is: whether the demand number of accessory q for product i on production line k is less than or equal to the accessory inventory of accessory q on production line k; If the start and end time periods of different workstations of the same production line, the same product, and the same batch overlap, any one of the start and end time periods of the overlapping workstations will be postponed to a non-overlapping position; if there are overlapping production times at the same workstation on the same production line, any one of the overlapping production times will be postponed to a non-overlapping position; If the preset inventory verification rule is not passed, the demand quantity of accessory q for product i on other production lines will be deleted.

7. The method according to claim 2, characterized in that The step of determining whether the iterative optimization is completed according to the fitness function value, and if so, determining the intermediate new offspring as an excellent population, comprises: The degree of deviation between the fitness function values ​​is calculated. If the degree of deviation is less than a preset threshold, the iterative optimization is completed and the intermediate new offspring is determined as an excellent population.

8. A discrete assembly scheduling device based on mixed linear integer programming, characterized in that: The device comprises: An encoding module, used for encoding a production scheduling gene, constructing a fitness function based on the production scheduling gene and preset constraints, and constructing an objective function based on the production scheduling gene; An acquisition module, used for acquiring orders to be scheduled, preprocessing the orders to be scheduled according to the preset constraints to obtain information to be scheduled, and constructing chromosomes based on the scheduling genes and the information to be scheduled; A calculation module is used to preset an initial population for the chromosome, iteratively optimize the initial population according to the fitness function to obtain an excellent population, calculate a target population among the excellent population that makes the objective function optimal, and use the target population as the target production schedule for the order to be scheduled.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the discrete assembly scheduling method based on mixed linear integer programming according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in a storage medium and executable on the processor, characterized in that: When the processor executes the program, the discrete assembly scheduling method based on mixed linear integer programming according to any one of claims 1 to 7 is implemented.

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