Process production scheduling method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention relates to the technical field of workshop scheduling, and discloses a process scheduling method, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a process path of a to-be-produced object, and obtaining team personnel information; obtaining a first optimal solution according to the process path and the team personnel information; the first optimal solution represents the process information with the highest first personnel utilization rate and / or the shortest total completion time of the team; acquiring a second optimal solution according to the first optimal solution, the station information, the scheduling rule of the target team and the production task of the to-be-generated object; the target team is a team satisfying the team personnel information; the second optimal solution represents the work station and team allocation information with the highest second personnel utilization rate and / or the earliest process end time of the team; scheduling process information and scheduling process information are obtained according to the second optimal solution; therefore, the efficiency and the accuracy of acquiring the scheduling process information and the scheduling process information are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of workshop scheduling, and in particular to a process scheduling method, electronic equipment, storage medium, and program product. Background Art

[0002] At present, in the manufacturing industry, most of the production process stations are arranged on the factory production line, and workers are arranged in shifts and schedules at the same time, so as to minimize costs and increase efficiency. How to maximize production efficiency and balance the utilization of resources (including processes, stations, beats, material supply, manpower, and equipment) is an important consideration indicator for factory scheduling.

[0003] Related technologies have scheduling + shift scheduling solutions, but they still cannot meet the high requirements of current factory production lines for the efficiency and accuracy of scheduling and shift scheduling results. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a process scheduling method, electronic equipment, storage medium and program product, thereby improving the efficiency and accuracy of obtaining scheduling process information and shift scheduling process information.

[0005] To solve the above technical problems, an embodiment of the present application provides a process scheduling method, which obtains the process path of the object to be produced and the team personnel information; obtains a first optimal solution based on the process path and the team personnel information; the first optimal solution represents the process information with the highest utilization rate of the first personnel of the team and / or the shortest total completion time; obtains a second optimal solution based on the first optimal solution, workstation information, the scheduling rules of the target team, and the production task of the object to be generated; the target team is the team that meets the team personnel information; the second optimal solution represents the workstation and team allocation information with the highest utilization rate of the second personnel of the team and / or the earliest process completion time; obtains scheduling process information and shift scheduling process information based on the second optimal solution.

[0006] An embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the process scheduling method as described above.

[0007] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the process scheduling method as described above when executed by a processor.

[0008] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above-mentioned process scheduling method is implemented.

[0009] In some embodiments, the obtaining of the first optimal solution according to the process path and the team member information includes: obtaining a first initialization population according to the process path and the team member information, the first initialization population including multiple solutions, each solution in the first initialization population representing a process information; obtaining the first personnel utilization rate and / or the total completion time corresponding to each solution in the first initialization population; obtaining a first target solution in which the first personnel utilization rate satisfies a first preset condition and / or the total completion time satisfies a second preset condition; performing a first evolutionary processing on the first target solution to obtain a first evolved population; updating the first initialization population to the first evolved population, and re-executing the step of obtaining the personnel utilization rate and / or the total completion time corresponding to each solution in the first initialization population until the first evolution number reaches a first preset threshold or the first target solution is no longer updated; obtaining the solution in which the first personnel utilization rate is the highest and / or the total completion time is the shortest in the first initialization population as the first optimal solution.

[0010] In some embodiments, the process path includes process execution rules; the first evolutionary processing includes: performing a first crossover processing and a first mutation processing on the first target solution, and retaining the solutions that meet the process execution rules among the solutions after the first crossover processing and the first mutation processing as the solutions in the first evolved population.

[0011] In some embodiments, the obtaining of team member information includes: obtaining multiple different team member information; the obtaining of the first optimal solution based on the process path and the team member information includes: obtaining the first optimal solution corresponding to the process path and each team member information; selecting the first optimal solution with the highest utilization rate of the first personnel and / or the shortest total completion time among multiple first optimal solutions as the target optimal solution; the obtaining of the second optimal solution based on the first optimal solution, workstation information, the scheduling rules of the target team, and the production tasks of the object to be generated includes: obtaining the second optimal solution based on the target optimal solution, workstation information, the scheduling rules of the target team, and the production tasks of the object to be generated.

[0012] In some embodiments, the method of obtaining a second optimal solution based on the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated includes: obtaining a second initialization population based on the workstation information, the scheduling rules of the target team, the first optimal solution, and the production task of the object to be generated; the second initialization population includes multiple solutions, and each solution in the second initialization population represents the workstation and team allocation information for completing the production task according to the process information of the first optimal solution; obtaining the process time information corresponding to each solution in the second initialization population according to the process scheduling constraints and the team scheduling constraints; obtaining the second initialization population according to the process time information Initialize the second personnel utilization rate and / or the process end time of each solution in the population; obtain multiple second target solutions in which the second personnel utilization rate satisfies a third preset condition and / or the process end time satisfies a fourth preset condition; perform a second evolutionary process on the second target solution to obtain a second evolved population; update the second initialized population to the second evolved population, and re-execute the step of obtaining the second evolved population until the second evolution number reaches a second preset threshold or the second target solution is no longer updated; obtain the solution in the second initial population in which the process end time is the earliest and / or the second personnel utilization rate is the highest as the second optimal solution.

[0013] In some embodiments, the second evolutionary process includes: a second crossover process and a second mutation process; the second crossover process and the second mutation process are performed according to a preset crossover mutation rule; the preset crossover mutation rule is: each workstation is selected with the same probability, and / or each work group is selected with the same probability.

[0014] In some embodiments, the process scheduling constraints are implemented through mixed integer programming, including at least one of the following: processes on the same workstation do not overlap, processes satisfy the parent-child relationship, the simultaneous start time of processes is earlier than the simultaneous end time of processes, and some processes must start at the same time; the team scheduling constraints are implemented through mixed integer programming, including at least one of the following: the working hours do not exceed the prescribed working hours and overtime hours, the team rest time is not less than a fixed time, and the team must work on the specified date.

[0015] The technical solution provided by the embodiment of the present application has at least the following advantages:

[0016] This embodiment first obtains the first optimal solution according to the process path and the team personnel information, thereby decoupling the step of obtaining the process information corresponding to the first optimal solution from the step of obtaining the process information and the scheduling process information, and then based on the process information of the team with the highest first personnel utilization rate and / or the shortest total completion time represented by the determined first optimal solution, the second optimal solution is further obtained in combination with the workstation information and the scheduling rules of the target team, and the scheduling process information and the scheduling process information are obtained according to the second optimal solution; since the first optimal solution with higher accuracy is obtained in advance in this embodiment, the dimension used in the process of obtaining the second optimal solution is reduced, and the complexity of the calculation of the obtained second optimal solution is reduced, thereby improving the efficiency of obtaining the scheduling process information and the scheduling process information. At the same time, even if the current scheduling and scheduling task volume is large, it is not easy to fall into the dimensional disaster, thereby improving the accuracy of obtaining the scheduling process information and the scheduling process information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0018] Figure 1 is a flow chart of a process scheduling method according to an embodiment of the present application;

[0019] Figure 2 is a process topology diagram of an object to be produced according to an embodiment of the present application;

[0020] Figure 3A is based on Figure 2 The Gantt chart corresponding to the first optimal solution obtained from the process topology diagram when the number of team members is 5;

[0021] Figure 3B is based on Figure 2 The Gantt chart corresponding to the first optimal solution obtained from the process topology diagram when the number of team members is 6;

[0022] Figure 3C is based on Figure 2 The Gantt chart corresponding to the first optimal solution obtained from the process topology diagram when the number of team members is 10;

[0023] Figure 4 is a flowchart of each sub-step of step 102 according to an embodiment of the present application;

[0024] Figure 5 is a logic diagram of the genetic algorithm in step 102 according to an embodiment of the present application;

[0025] Figure 6is a flowchart of each sub-step of step 103 according to an embodiment of the present application;

[0026] Figure 7 is a logic diagram of the genetic algorithm in step 103 according to an embodiment of the present application;

[0027] Figure 8 is a Gantt chart of the scheduling process information finally output by the genetic algorithm according to an embodiment of the present application;

[0028] Fig. 9 It is a Gantt chart of the scheduling process information finally output by the genetic algorithm according to one embodiment of the present application;

[0029] Fig.10 is an overall logic diagram of a process scheduling method according to an embodiment of the present application;

[0030] Fig.11 is a flow chart of a process scheduling method according to another embodiment of the present application;

[0031] Fig.12 It is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] As can be seen from the background technology, the current scheduling + shift scheduling solution still cannot meet the high requirements of the current factory production line for efficiency and accuracy.

[0033] Through analysis, it is found that the reason why the efficiency and accuracy of the existing scheduling + scheduling solutions are low is that the existing scheduling + scheduling solutions mainly unify process optimization, scheduling, and scheduling into a genetic algorithm; however, when the scheduling task volume is too large, the genetic algorithm is prone to fall into the dimensionality curse, and the solution time is long, and there is also the risk of falling into the local optimal solution, and the global optimal solution cannot be obtained, thus affecting the efficiency and accuracy of the scheduling results and the scheduling results.

[0034] Therefore, the present application provides a process scheduling method, including: obtaining the process path of the object to be produced and obtaining the team personnel information; obtaining a first optimal solution based on the process path and the team personnel information; the first optimal solution represents the process information with the highest utilization rate of the first personnel of the team and / or the shortest total completion time; obtaining a second optimal solution based on the first optimal solution, the workstation information, and the scheduling rules of the target team; the target team is the team that meets the team personnel information; the second optimal solution represents the workstation and team allocation information with the highest utilization rate of the second personnel of the team and / or the earliest process completion time; and obtaining scheduling process information and shift scheduling process information based on the second optimal solution.

[0035] This embodiment first obtains the first optimal solution according to the process path and the team personnel information, thereby decoupling the step of obtaining the process information corresponding to the first optimal solution from the step of obtaining the process information and the scheduling process information, and then based on the process information of the team with the highest first personnel utilization rate and / or the shortest total completion time represented by the determined first optimal solution, the second optimal solution is further obtained in combination with the workstation information and the scheduling rules of the target team, and the scheduling process information and the scheduling process information are obtained according to the second optimal solution; since the first optimal solution with higher accuracy is obtained in advance in this embodiment, mixed integer programming is introduced in the process of obtaining the second optimal solution, the used dimension is reduced, and the complexity of the calculation of the obtained second optimal solution is reduced, thereby improving the efficiency of obtaining the scheduling process information and the scheduling process information. At the same time, even if the current scheduling and scheduling task volume is large, it is not easy to fall into the dimensionality disaster, thereby improving the accuracy of obtaining the scheduling process information and the scheduling process information.

[0036] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0037] An embodiment of the present application relates to a process scheduling method, and a specific flow chart is shown as follows: Figure 1 As shown, the process scheduling method includes the following steps:

[0038] Step 101, obtaining the process path of the object to be produced and obtaining the team member information.

[0039] The execution subject of this embodiment is an electronic device, which can be a terminal, a server, etc. In the production process, when the factory receives a new order, the employee will input the BOM (Bill Of Material) information of the new order into the electronic device, and the electronic device will receive the BOM information of the new order. The BOM information is also called a product structure tree, which describes the composition of each component of a product and the hierarchical structure of each component. For example, if the new order is to manufacture 10 cars, the corresponding BOM information is the various components of the vehicle and the hierarchical relationship between the various components.

[0040] The electronic device can obtain the information of each component that needs to be produced for the new order based on the BOM information of the new order. Each component is an object to be produced. For example, the new order is to manufacture 10 cars. According to the BOM information, it can be obtained that 40 wheels and 10 car bodies need to be manufactured. The wheels can be regarded as one object to be produced and the car bodies as another production object. The manufacturing quantity of 40 wheels is used as the production task of the object to be produced, and the manufacturing quantity of 10 car bodies is used as the production task of another object to be produced.

[0041] Specifically, the electronic device stores a corresponding process path in advance for each component, i.e., the object to be produced. The relevant information of the process path can be provided by a process topology diagram, and the process topology diagram can be input into the electronic device in advance by the employee, such as Figure 2 As shown, it is a process topology diagram of an object to be produced in this embodiment, and the process topology diagram shows multiple processes for manufacturing corresponding parts, the execution order of multiple processes, and the number of workers and time required for each process. Taking the object to be produced as a wheel as an example, the production processes of the wheel in the figure include Task1 (5, 3), Task2 (3, 3), Task3 (5, 2), Task4 (4, 4), Task5 (2, 2), Task6 (4, 2), and Task7 (7, 5). The number of workers corresponding to each process is 5, 3, 5, 4, 3, 4, and 7 respectively, and the time corresponding to each process is 3, 3, 2, 4, 2, 2, and 5 respectively; the arrows in the figure indicate the sequence between the processes, and Task1 and Task2 are executed first. Task1 and Task2 can be executed simultaneously or distributedly. After Task1 is executed, Task3 and Task5 can be executed in sequence. After Task1 and Task2 are executed, Task4 and Task6 can be executed in sequence. After Task5 and Task6 are executed, Task7 is executed.

[0042] Specifically, the team member information is the number of team members required, and the team member information can be set by employees. For example, after entering the BOM information of the new order, the number of team members required for each object to be produced is entered. For example, the number of team members can be entered as 5, 6, or 10, so as to obtain a more accurate first optimal solution based on the set team member information.

[0043] Step 102, obtaining a first optimal solution according to the process path and team member information.

[0044] Specifically, after obtaining the process path and team member information, a first optimal solution is obtained according to the process path and the team member information. The first optimal solution represents the process information with the highest utilization rate of the first staff of the team and / or the shortest total completion time.

[0045] Specifically, this embodiment uses a genetic algorithm to obtain the first optimal solution, and this embodiment customizes the decoding part and the crossover and mutation parts of the genetic algorithm. This embodiment uses the personnel utilization rate and the total completion time of the process as the target for the decoding part of the genetic algorithm, and can visualize the specific optimization result, i.e., the first optimal solution, through a Gantt chart. Figure 3A yes Figure 2 The Gantt chart corresponding to the first optimal solution obtained when the number of team members is 5 is shown in the process topology diagram. Figure 3B yes Figure 2 The Gantt chart corresponding to the first optimal solution obtained when the number of team members is 6 is shown in the process topology diagram. Figure 3C yes Figure 2 The process topology diagram shown in the figure is a Gantt chart corresponding to the first optimal solution obtained when the number of team members is 10, where the horizontal axis of the Gantt chart represents time, and the vertical axis represents different tasks. Different colors are used to represent different processes in the figure, and the number of people used in each process is also marked. For example, as shown in Figure 3, Task 1 is first executed, which requires 5 people; then, Task 2 and Task 3 are executed in parallel, which require 3 people and 5 people respectively; then, Task 4 is executed, which requires 4 people; then, Task 5 and Task 6 are executed in parallel, which require 2 people and 4 people respectively; finally, Task 7 is executed, which requires 7 people; Figure 3B , Figure 3C Corresponding explanations can also be given and no further elaboration will be given.

[0046] It can be seen that the number of people required for different processes and the parallel execution status are clear at a glance; at the same time, the genetic algorithm can also output the total completion time and the first personnel utilization rate corresponding to the first optimal solution. For example, when the number of team members is 5, the output Figure 3A The Gantt chart shown in the figure outputs the corresponding total completion time of 25h and the utilization rate of the first person of 77%. When the number of people in the team is 6, the output Figure 3B The Gantt chart shown in the figure outputs the corresponding total completion time of 20h and the utilization rate of the first person of 81%. When the number of people in the team is 10, the output Figure 3C The Gantt chart shown simultaneously outputs the corresponding total completion time of 20h and the first personnel utilization rate of 49%.

[0047] In addition, since the process path diagram is a topological structure, based on the properties of the topology, the population that does not conform to the topological structure can be deleted during the population crossover and mutation process, thereby accelerating the genetic algorithm.

[0048] Specifically, step 102 of this embodiment can use a genetic algorithm to obtain a first optimal solution. Step 102 includes the following sub-steps: Figure 4 FIG. 1 is a flow chart of each sub-step of step 102 of this embodiment, which includes the following sub-steps:

[0049] Step 1021, obtaining a first initialization population according to the process path and team member information.

[0050] Specifically, the first initialization population includes multiple solutions. The multiple solutions in the first initialization population are multiple process information randomly generated according to the process path and team personnel information. Each solution in the first initialization population represents a process information. Each process information represents the execution order of each process for producing an object to be produced, the number of people in each process and the time taken. Each solution in the first initialization population satisfies the process execution rules of the process path and satisfies the number of team members set by the team personnel information.

[0051] Step 1022, obtaining the first personnel utilization rate and / or total completion time corresponding to each solution in the first initialized population.

[0052] Specifically, after obtaining each solution of the first initialized population, the first personnel utilization rate and / or the total completion time of the team is calculated according to the process information represented by each solution. The first personnel utilization rate is the ratio of the actual working time of all workers in the team to the available working time of all workers in the team, and the total completion time is the time used to produce an object to be produced.

[0053] Step 1023, obtaining a first target solution in which the first personnel utilization rate satisfies a first preset condition and / or the total completion time satisfies a second preset condition.

[0054] Specifically, after obtaining the first personnel utilization rate and / or the total completion time corresponding to each solution of the first initialized population, determine whether the first personnel utilization rate meets the first preset condition, and / or determine whether the total completion time meets the second preset condition, and obtain the first target solution whose first personnel utilization rate meets the first preset condition and / or whose total completion time meets the second preset condition. Among them, the first preset condition can be the top N solutions with the highest first personnel utilization rate, N is a natural number greater than 0, N can be 3, 4, 5, and the first preset condition can also be a solution whose first personnel utilization rate is greater than a preset utilization rate, and the preset utilization rate can be 80%, 85%, 90%; the second preset condition can be the top M solutions with the highest total completion time, M is a natural number greater than 0, M can be 3, 4, 5, and the second preset condition can also be a solution whose total completion time is greater than a preset time, and the preset time can be 5min, 10min, 15min

[0055] It should be noted that, when both the first personnel utilization rate and the total completion time are obtained, the first target solution is a solution that satisfies both the first preset condition and the second preset condition.

[0056] Step 1024: Perform a first evolutionary process on the first target solution to obtain a first evolved population.

[0057] Specifically, after the first target solution is obtained, performing a first evolutionary process on the first target solution can obtain more evolved solutions, thereby improving the accuracy of the first optimal solution finally obtained.

[0058] Specifically, the process path includes the process execution rules, that is, the execution order of each process; the first evolutionary process is specifically: performing the first crossover process and the first mutation process on the first target solution, and retaining the solution that meets the process execution rules in the solution after the first crossover process and the first mutation process as the solution in the first evolved population. This embodiment can obtain as many better solutions as possible by performing the first crossover process and the first mutation process on the first target solution. Since the process path diagram is a topological structure, based on the nature of the topology, there may be some solutions that do not meet the process execution rules during the population crossover and mutation process. Therefore, these solutions need to be deleted, and only the solutions that meet the process execution rules are retained, thereby improving the accuracy of the population after the first evolution, and can also improve the speed of genetic algorithm calculation, so this process can also be called pruning acceleration.

[0059] Step 1025, determining whether the first evolution number reaches a first preset threshold or whether the first target solution is no longer updated.

[0060] Specifically, after the first target solution is subjected to the first evolutionary process to obtain the first evolved population, the first evolutionary times of the first evolutionary process can be judged. If the first evolutionary times do not reach the first preset threshold, step 1026 is entered; if the first evolutionary times reach the first preset threshold, step 1027 is entered; or, it can be judged whether the first target solution is no longer updated. If the first target solution is updated, it is considered that the first evolved population can continue to evolve to obtain a better solution, so step 1026 is entered, and then step 1022 is re-entered; if the first target solution is no longer updated, it can be considered that even if the first evolved population is evolved again, a better solution cannot be obtained, so the loop process can be stopped at this time and step 1027 can be directly entered. The first target solution is updated, which means that the first target solution obtained in this round is different from the first target solution obtained in the previous round. The first target solution is no longer updated, which means that the first target solution obtained in this round is exactly the same as the first target solution obtained in the previous round.

[0061] It should be noted that, when only determining whether the first target solution is no longer updated is selected, step 1024 can be omitted, and there is no need to perform a step of performing a first evolutionary process on the first target solution to obtain a first evolved population.

[0062] Step 1026: Update the first initialized population to the first evolved population, that is, replace each solution of the first initialized population with each solution of the first evolved population.

[0063] Specifically, when it is determined that the first evolution number has not reached the first preset threshold or the first target solution is determined to be updated, each solution of the first evolved population is added to the first initialized population, and then step 1022 is re-entered, that is, the step of obtaining the personnel utilization rate and / or total completion time corresponding to each solution in the first initialized population is re-executed until the first evolution number reaches the first preset threshold or the first target solution is no longer updated.

[0064] Step 1027, obtaining the solution with the highest first personnel utilization rate and / or the shortest total completion time in the first initialized population as the first optimal solution.

[0065] Specifically, since the first personnel utilization rate and / or total completion time corresponding to each solution in the first initialized population has been obtained in this cycle, when this embodiment determines that the first evolution number reaches the first preset threshold or determines that the first target solution is no longer updated, the solution with the highest first personnel utilization rate and / or the shortest total completion time can be selected as the first optimal solution.

[0066] It should be noted that the utilization rate of the first personnel is inversely proportional to the total completion time. The higher the utilization rate of the first personnel, the shorter the total completion time, and the lower the utilization rate of the first personnel, the longer the total completion time. Therefore, in this embodiment, when both the utilization rate of the first personnel and the total completion time are obtained, the solution with the highest utilization rate of the first personnel and the solution with the shortest total completion time are the same solution.

[0067] like Figure 5As shown, it is a logical schematic diagram of the genetic algorithm in step 102 of this embodiment. The input of the genetic algorithm is the process path and the team member information. The genetic algorithm obtains a first initialization population [o1, o2, o3, o4, ...] according to the process path and the team member information; the decoding part of the genetic algorithm is performed on the first initialization population, and the first personnel utilization rate and the total completion time of the process are taken as the target to obtain the first personnel utilization rate and / or the total completion time corresponding to each solution in the first initialization population; then, a first target solution in which the first personnel utilization rate meets the first preset condition and / or the total completion time meets the second preset condition is obtained, and the first target solution is subjected to a first evolutionary process (including a first crossover process, a first mutation process, and pruning acceleration) to obtain a first evolved population [o1', o2', o3', o4', ...], each solution of the first evolved population is added to the first initialization population, and the step of obtaining the personnel utilization rate and / or the total completion time corresponding to each solution in the first initialization population is re-executed until the first evolution number reaches the first preset threshold or the first target solution is no longer updated, and the output is as follows: Figure 3A or Figure 3B or Figure 3C A Gantt chart of the first optimal solution is shown.

[0068] Step 103, obtaining a second optimal solution according to the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated.

[0069] Specifically, after obtaining the first optimal solution, the second optimal solution is obtained based on the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated, wherein the target team is the team that meets the team personnel information, and the second optimal solution represents the workstation and team allocation information with the highest second personnel utilization rate and / or the earliest process completion time; the workstation information represents the workstation in the factory that can produce the object to be produced, as well as the workable time and non-working time of the workstation, and the scheduling rules of the target team represent the workable time of the workers in the team.

[0070] Specifically, after obtaining a new order, the production task of the object to be produced, that is, the number of objects to be produced that need to be produced, is calculated based on the factory's inventory. The number of objects to be produced is the number of objects to be produced required for the new order minus the corresponding inventory.

[0071] Specifically, the two most critical links in this embodiment are the allocation of workstations and teams. If mixed integer programming is used for workstation selection and team allocation, techniques such as the big M method will be used, which will not only reduce the speed of solving, but also increase the complexity of the genetic algorithm and reduce the versatility of the genetic algorithm. Therefore, this embodiment uses workstation selection and team allocation as integer variables, introduces genetic algorithms to solve integer variables, and uses mixed integers to solve other variables such as the start and end time of each process, the overtime time of the team, etc. In this way, this embodiment allows all the most core constraints to be expressed by the start and end variables of the process.

[0072] Specifically, after obtaining the corresponding second objective solution through the genetic algorithm combined with integer variables and mixed integer programming, this embodiment can set the earliest end time or the highest personnel utilization rate as the optimization goal of the genetic algorithm, and in the process of population cross-mutation, ensure that the allocation of workstations and work teams appears with an average probability, so as to make full use of resources and obtain near-optimal scheduling and scheduling results.

[0073] Specifically, step 103 of this embodiment uses a genetic algorithm combined with integer variables and mixed integer programming to obtain a corresponding second objective solution, such as Figure 6 FIG. 1 is a flow chart of each sub-step of step 103 of this embodiment, which includes the following sub-steps:

[0074] Step 1031, obtaining a second initialization population according to the workstation information, the scheduling rules of the target team, the first optimal solution, and the production task of the object to be generated.

[0075] Specifically, the second initialization population includes multiple solutions. The multiple solutions in the second initialization population are multiple workstation and team allocation information randomly generated according to the workstation information, the target team, and the first optimal solution. Each solution in the second initialization population represents the workstation and team allocation information for completing the production task according to the process information of the first optimal solution; the workstation allocation information indicates at which workstation each process (each process corresponding to the process information of the first optimal solution) is specifically performed, and the team allocation information indicates by which team each process (each process corresponding to the process information of the first optimal solution) is specifically performed.

[0076] Step 1032, obtaining the process time information corresponding to each solution in the second initialization population according to the process scheduling constraints and the team scheduling constraints.

[0077] Specifically, the genetic algorithm of this embodiment imposes process scheduling constraints and team scheduling constraints on each solution in the second initialized population to obtain the process time information corresponding to each solution, that is, when each solution completes the production task of the object to be generated, the start time and end time of each process and the overtime duration of the target team.

[0078] Among them, the process scheduling constraints are implemented through mixed integer programming, including at least one of the following: the processes on the same workstation do not overlap, the processes satisfy the parent-child relationship, the simultaneous start time of the processes is earlier than the simultaneous end time of the processes, and some processes must start at the same time; the team scheduling constraints are implemented through mixed integer programming, including at least one of the following: the working hours do not exceed the prescribed working hours and overtime hours, the team rest time is not less than a fixed time, and the team must work on the specified date, which can be a working day.

[0079] Step 1033, obtaining the second personnel utilization rate and / or process end time of each solution in the second initialization population according to the process time information.

[0080] Specifically, after obtaining the process time information corresponding to each solution in the second initialization population, that is, when each solution completes the production task of the object to be generated, the start time and end time of each process, and the overtime of the target team, the second personnel utilization rate and / or process end time of each solution in the second initialization population can be calculated according to the process time information. The second personnel utilization rate is the ratio of the actual working time of all workers in the target team to the available working time of all workers in the target team, and the process end time is the moment when the production task of the object to be produced is completed.

[0081] Step 1034, obtaining multiple second target solutions in which the second personnel utilization rate satisfies the third preset condition and / or the process end time satisfies the fourth preset condition.

[0082] Specifically, after obtaining the second personnel utilization rate and / or process end time corresponding to each solution of the second initialization population, it is determined whether the second personnel utilization rate meets the third preset condition, and / or it is determined whether the process end meets the second preset condition, and the second target solution whose second personnel utilization rate meets the third preset condition and / or whose process end meets the fourth preset condition is obtained. Among them, the third preset condition can be the first P solutions with the highest second personnel utilization rate, P is a natural number greater than 0, P can be 3, 4, 5, and the third preset condition can also be a solution whose second personnel utilization rate is greater than a preset utilization rate, and the preset utilization rate can be 80%, 85%, 90%; the fourth preset condition can be the first Q solutions with the earliest process end time, Q is a natural number greater than 0, Q can be 3, 4, 5, and the fourth preset condition can also be a solution whose process end time is earlier than a preset time.

[0083] It should be noted that, when the second personnel utilization rate and the process end time are both obtained, the second target solution is a solution that satisfies both the third preset condition and the fourth preset condition.

[0084] Step 1035: Perform a second evolutionary process on the second target solution to obtain a second evolved population.

[0085] Specifically, after the second target solution is obtained, performing a second evolutionary process on the second target solution can obtain more evolved solutions, thereby improving the accuracy of the second optimal solution finally obtained.

[0086] Specifically, the second evolution process includes: a second crossover process and a second mutation process; the second crossover process and the second mutation process are performed according to a preset crossover mutation rule; the preset crossover mutation rule is: each workstation is selected with the same probability, and / or each shift is selected with the same probability. This embodiment can obtain as many better solutions as possible by performing a second crossover process and a second mutation process on the second target solution, thereby improving the accuracy of the population after the second evolution.

[0087] Step 1036, determining whether the second evolution number reaches a second preset threshold or whether the second target solution is no longer updated.

[0088] Specifically, after performing a second evolutionary process on the second target solution to obtain a second evolved population, the second evolutionary number of the second evolutionary process can be judged. If the second evolutionary number does not reach the second preset threshold, enter step 1037; if the second evolutionary number reaches the second preset threshold, enter step 1038. Alternatively, it can be judged whether the second target solution is no longer updated. If the second target solution is updated, it is considered that the second evolved population can continue to evolve to obtain a better solution, so step 1037 is entered, and then step 1032 is re-entered; if the second target solution is no longer updated, it can be considered that even if the second evolved population is evolved again, a better solution cannot be obtained. Therefore, the loop process can be stopped at this time and step 1038 can be directly entered.

[0089] Step 1037: Update the second initialization population to the second evolved population, that is, replace each solution of the second initialization population with each solution of the second evolved population.

[0090] Specifically, when it is determined that the second evolution number has not reached the second preset threshold or the second target solution is updated, the second initialized population is updated to the second evolved population, and step 1032 is re-entered to re-execute the step of obtaining the process time information corresponding to each solution in the second initialized population according to the process scheduling constraints and the team scheduling constraints until the second evolution number reaches the second preset threshold or the second target solution is no longer updated.

[0091] It should be noted that when only determining whether the second target solution is no longer updated is selected, step 1035 can be omitted, and there is no need to perform a step of performing a second evolutionary process on the second target solution to obtain a second evolved population.

[0092] Step 1038, obtaining the solution with the earliest process end time and / or the highest second personnel utilization rate in the second initialized population as the second optimal solution.

[0093] Specifically, since the second personnel utilization rate and / or process end time corresponding to each solution in the second initialization population has been obtained in this cycle, when this embodiment determines that the second evolution number reaches the second preset threshold or determines that the second target solution is no longer updated, the solution with the highest second personnel utilization rate and / or the earliest process end time can be selected as the second optimal solution.

[0094] It should be noted that the utilization rate of the second personnel is positively correlated with the process end time. The higher the utilization rate of the second personnel, the earlier the process end time, and the lower the utilization rate of the second personnel, the later the process end time. Therefore, in this embodiment, when both the utilization rate of the second personnel and the process end time are obtained, the solution with the highest utilization rate of the second personnel and the solution with the earliest process end time are the same solution.

[0095] like Figure 7 As shown, it is a logical schematic diagram of the genetic algorithm in step 103 of this embodiment. The input of the genetic algorithm is the first optimal solution, the scheduling rules of the workstation information and the team information, and the production task of the object to be generated. The genetic algorithm obtains the second initialization population according to the first optimal solution, the scheduling rules of the workstation information and the team information, and the production task of the object to be generated. A solution in the second initialization population can be expressed as [m1, m2, m3, ..., t1, t2, t3, ...]; m represents the workstation at which each process is performed, and t represents the team by which each process is performed.

[0096] The genetic algorithm of this embodiment also needs to define optimization variables, including start i (start time of process i), end i (end time of process i), slack t (overtime hours of team t), makespan (process completion time / second staff utilization rate).

[0097] The genetic algorithm of this embodiment also sets process scheduling constraints, which include start i ≥workcenter start (Start time of process i ≥ end time of station occupation of film), start i,n +CTi≤start j,n (The processes at the same station cannot overlap), end i,n ≥start j,n +CT j (The child process must start after the parent process is completed), start i=start j (Some processes need to start at the same time).

[0098] The genetic algorithm of this embodiment also sets shift scheduling constraints, which include end i,t -start i,t ≤worktime t +slack t (The continuous working time of the team shall not exceed the sum of the team working time and the overtime time), end m,t -start n,t ≥resttime t (The rest time of the team must meet the specified rest time), [start i , end i ,inworkDay1]V[start i , end i , in workDay2]V……V[start i , end i , in workDay n ](The team must work on the designated working days).

[0099] The genetic algorithm of this embodiment performs a second evolutionary process (including a second crossover process and a second mutation process) on the second target solution to obtain a second evolved population. A solution of the second evolved population can be expressed as [m1', m2', m3', ..., t1', t2', t3', ...]. The second crossover process and the second mutation process are performed according to the rule that each workstation is selected with the same probability and each work group is selected with the same probability. Afterwards, each solution of the second evolved population is added to the second initialization population, and the step of obtaining the second evolved population is re-executed until the second evolution number reaches the second preset threshold or the second target solution is no longer updated, so as to obtain the second optimal solution.

[0100] Step 104, obtaining scheduling process information and shift scheduling process information according to the second optimal solution.

[0101] Specifically, after obtaining the second optimal solution as mentioned above, since the second optimal solution represents the workstation and team allocation information with the highest second personnel utilization rate and / or the earliest process completion time in the team, the scheduling process information and the shift scheduling process information can be obtained according to the second optimal solution. Both the scheduling process information and the shift scheduling process information meet the process information requirements of the first optimal solution, and both the scheduling process information and the shift scheduling process information can be displayed through the Gantt chart.

[0102] like Figure 8As shown in the figure, it is the Gantt chart of the scheduling process information finally output by the genetic algorithm, where the horizontal axis represents time, the vertical axis represents different workstations, and each color represents a different process of the same object to be produced. Fig. 9 As shown in FIG. 1 , it is a Gantt chart of the scheduling process information finally output by the genetic algorithm, in which the horizontal axis represents time, the vertical axis represents different workstations, and each color represents a team.

[0103] This embodiment decouples the step of obtaining the process information corresponding to the first optimal solution from the step of obtaining the process information and the scheduling process information. The dimension used in the process of obtaining the second optimal solution is reduced, and it is not easy to fall into the dimensionality disaster, thereby improving the efficiency and accuracy of obtaining the scheduling process information and the scheduling process information.

[0104] like Fig.10 As shown, it is an overall logical schematic diagram of the process scheduling method of this embodiment. In this embodiment, the step of obtaining the process information corresponding to the first optimal solution is separately decoupled. The first optimal solution is obtained by using a genetic algorithm (including decoding and a first evolutionary process), and the second optimal solution is obtained by using a genetic algorithm + mixed integer programming. The genetic algorithm (including decoding and a first evolutionary process) receives the process path and the team member information, and obtains the first optimal solution according to the process path and the team member information. The genetic algorithm (including decoding and a first evolutionary process) also receives the BOM information of the new order, and transmits the first optimal solution and the BOM information to the genetic algorithm + mixed integer programming. The genetic algorithm + mixed integer programming The plan obtains the actual demand quantity of the object to be produced according to the BOM information, subtracts the inventory to obtain the production task of the object to be produced, and then combines the scheduling rules and workstation information of the target team to obtain the second optimal solution. The scheduling process information and the shift scheduling process information are obtained through the second optimal solution, that is, the working and rest time of employees are set, and the unavailable time of each workstation is customized, so as to flexibly arrange the work and rest of the team, thereby realizing the rhythm adaptive scheduling; this embodiment can not only strictly consider each team and process constraint, but also accelerate the solution process through skills to obtain a higher quality solution; that is, this embodiment takes scheduling and shift scheduling into consideration, and can realize variable beat to drive production.

[0105] Furthermore, the scheduling algorithm of this embodiment can receive real-time working conditions from the work reporting system, and realize real-time rescheduling in a complex factory operation environment after updating the preconditions (such as redefining optimization variables, process scheduling constraints, and team scheduling constraints). The process optimization proposed in this embodiment combined with scheduling can help the lean team design the process path, prefabricate the bottleneck process and adjust it, thereby increasing output; this embodiment can also calculate employee working hours, estimate labor costs and adjust them to achieve cost reduction and efficiency improvement.

[0106] Another embodiment of the present application relates to a process scheduling method, and the specific flow chart is as follows: Fig.11As shown, the process scheduling method includes the following steps:

[0107] Step 201, obtaining the process path of the object to be produced and obtaining information of multiple different team members.

[0108] Step 202, obtaining the first optimal solution corresponding to the process path and each team member information.

[0109] Step 203 , selecting a first optimal solution with the highest first staff utilization rate and / or the shortest total completion time from among multiple first optimal solutions as a target optimal solution.

[0110] Step 204, obtaining a second optimal solution according to the target optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated.

[0111] Step 205, obtaining scheduling process information and shift scheduling process information according to the second optimal solution.

[0112] The difference between this embodiment and the previous embodiment is that: this embodiment obtains multiple different team member information, obtains the first optimal solution corresponding to the process path and each team member information, that is, each team member information is subjected to the genetic algorithm of step 102 in the above embodiment to obtain the corresponding first optimal solution; and selects the first optimal solution with the highest first personnel utilization rate and / or the shortest total completion time from multiple first optimal solutions as the target optimal solution, so as to obtain the second optimal solution according to the target optimal solution, workstation information, scheduling rules of the target team, and production tasks of the object to be generated, that is, the input parameters of the genetic algorithm in step 103 in the above embodiment are modified from the first optimal solution to the target optimal solution, and other parameters remain unchanged.

[0113] For example, if the team members are 5, 6, and 10, the following outputs are given: Figure 3A , Figure 3B , Figure 3C The Gantt chart shown in the figure outputs the corresponding total completion time and the utilization rate of the first personnel, and selects the first optimal solution with the highest utilization rate of the first personnel and / or the shortest total completion time. It can be seen that when the team member information is 6, the corresponding first personnel utilization rate is the highest and the total completion time is the shortest. The first optimal solution corresponding to the team member information of 6 is taken as the target optimal solution.

[0114] This embodiment and the previous embodiment can be implemented in conjunction with each other. The relevant technical details mentioned in the previous embodiment are still valid in this embodiment. To avoid repetition, they will not be repeated here. The relevant technical details of this embodiment can also be applied to the previous embodiment. To avoid repetition, they will not be repeated here.

[0115] This embodiment obtains the first optimal solution corresponding to the process path and each team member information by obtaining multiple different team member information. By setting more team member information, the target optimal solution with the highest first personnel utilization rate and / or the shortest total completion time can be selected from multiple team member information, thereby improving the accuracy of obtaining the target optimal solution and further improving the accuracy of the second optimal solution obtained based on the target optimal solution.

[0116] An embodiment of the present application relates to an electronic device, such as Fig.12 As shown, it includes: at least one processor 301; and a memory 302 that is communicatively connected to the at least one processor 301; wherein the memory stores instructions that can be executed by the at least one processor 301, and the instructions are executed by the at least one processor 301 so that the at least one processor 301 can execute the above-mentioned process scheduling method.

[0117] The memory 302 and the processor 301 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 301 and the memory 302 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 301 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 301.

[0118] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 302 can be used to store data used by the processor when performing operations.

[0119] An embodiment of the present application relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the above method embodiment is implemented.

[0120] An embodiment of the present application relates to a computer program product, including a computer program, and when the computer program is executed by a processor, the above-mentioned process scheduling method is implemented.

[0121] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0122] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A process scheduling method, characterized in that: include: Obtain the process path of the object to be produced and obtain the team member information; Obtaining a first optimal solution according to the process path and the team member information; The first optimal solution represents the process information with the highest utilization rate of the first personnel of the team and / or the shortest total completion time; The second optimal solution is obtained according to the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated; the target team is the team that meets the team personnel information; the second optimal solution represents the workstation and team allocation information of the team with the highest second personnel utilization rate and / or the earliest process end time; The scheduling process information and the shift scheduling process information are obtained according to the second optimal solution.

2. The process scheduling method according to claim 1, characterized in that: The obtaining of the first optimal solution according to the process path and the team member information includes: Acquire a first initialization population according to the process path and the team member information, wherein the first initialization population includes a plurality of solutions, and each solution in the first initialization population represents a type of process information; Obtaining the first personnel utilization rate and / or the total completion time corresponding to each solution in the first initialization population; Obtaining a first target solution in which the first personnel utilization rate satisfies a first preset condition and / or the total completion time satisfies a second preset condition; Performing a first evolutionary process on the first target solution to obtain a first evolved population; Update the first initialized population to the first evolved population, and re-execute the step of obtaining the personnel utilization rate and / or the total completion time corresponding to each solution in the first initialized population until the first evolution number reaches a first preset threshold or the first target solution is no longer updated; obtain the solution with the highest first personnel utilization rate and / or the shortest total completion time in the first initialized population as the first optimal solution.

3. The process scheduling method according to claim 2, characterized in that: The process path includes process execution rules; the first evolutionary processing includes: performing a first crossover processing and a first mutation processing on the first target solution, and retaining the solutions that meet the process execution rules among the solutions after the first crossover processing and the first mutation processing as the solutions in the first evolved population.

4. The process scheduling method according to claim 1, characterized in that: The obtaining of team member information includes: Obtaining multiple different team member information; The obtaining of the first optimal solution according to the process path and the team member information includes: Obtaining a first optimal solution corresponding to the process path and each of the team member information; Selecting the first optimal solution with the highest utilization rate of the first personnel and / or the shortest total completion time from among the multiple first optimal solutions as the target optimal solution; The obtaining of the second optimal solution according to the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated includes: A second optimal solution is obtained according to the target optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated.

5. The process scheduling method according to claim 1, characterized in that: The obtaining of the second optimal solution according to the first optimal solution, the workstation information, the scheduling rules of the target team, and the production task of the object to be generated includes: A second initialization population is obtained according to the workstation information, the scheduling rules of the target team, the first optimal solution, and the production task of the object to be generated; the second initialization population includes multiple solutions, and each solution in the second initialization population represents the workstation and team allocation information for completing the production task according to the process information of the first optimal solution; Obtaining process time information corresponding to each solution in the second initialized population according to process scheduling constraints and team scheduling constraints; Acquire the second personnel utilization rate and / or the process end time of each solution in the second initialization population according to the process time information; Acquire multiple second target solutions in which the second personnel utilization rate satisfies a third preset condition and / or the process end time satisfies a fourth preset condition; Performing a second evolutionary process on the second objective solution to obtain a second evolved population; Updating the second initialized population to the second evolved population, and re-performing the step of obtaining the second evolved population until the second evolution number reaches a second preset threshold or the second target solution is no longer updated; The solution in which the process end time is the earliest and / or the second personnel utilization rate is the highest in the second initialized population is obtained as the second optimal solution.

6. The process scheduling method according to claim 5, characterized in that: The second evolutionary process includes: a second crossover process and a second mutation process; the second crossover process and the second mutation process are executed according to a preset crossover mutation rule; the preset crossover mutation rule is: each workstation is selected with the same probability, and / or each team is selected with the same probability.

7. The process scheduling method according to claim 5, characterized in that: The process scheduling constraints are implemented by mixed integer programming, including at least one of the following: processes at the same station do not overlap, processes satisfy parent-child relationships, the simultaneous start time of processes is earlier than the simultaneous end time of processes, and some processes must start at the same time; The shift scheduling constraints are implemented through mixed integer programming, including at least one of the following: the working hours do not exceed the prescribed working hours and overtime hours, the rest time of the team is not less than the fixed time, and the team must work on the specified date.

8. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the process scheduling method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the process scheduling method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the process scheduling method according to any one of claims 1 to 7.

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