Joint scheduling method, system and equipment for flexible processing and assembling, and medium

Abstract

The invention relates to the technical field of workshop production and manufacturing, and discloses a joint scheduling method, system and equipment for flexible processing and assembling and a medium. The method comprises the following steps: constructing a joint scheduling model according to a plurality of processing procedures and a plurality of assembling procedures of a plurality of workpieces in a production and manufacturing process and a cooperative scheduling process between each processing procedure and each assembling procedure; representing a solution of the joint scheduling model by adopting batch coding, process coding and machine coding; solving the joint scheduling model by adopting a genetic algorithm by taking the minimum maximum completion time of all the workpieces as a target to obtain a target batch number of each workpiece, a target distribution sequence of each machining process and each assembly process of each workpiece and a target distribution machine of each machining process and each assembly process of each workpiece; the scheduling relation between processing and assembling in the flexible job shop is clearly described, a better joint scheduling scheme is generated, and the production and manufacturing efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop production and manufacturing, and particularly to a joint scheduling method, system, device and medium for flexible machining and assembly. Background Art

[0002] Due to the fact that market demands are gradually becoming more diverse and personalized, enterprises are facing problems such as large process changes and uncertain delivery dates. The multi-variety and variable-batch production mode has strong flexibility and can adapt to changes in product types and order quantities. It has become one of the main production organization modes in the workshop. In the small-batch production mode, that is, the order-oriented production method, a model for batch division and equipment allocation for the divided sub-batches has emerged. At the same time, with the development of the customization and personalized mode, the flexibility of the factory is also getting higher and higher. By adopting a suitable batch division method, the machine idle rate and inventory time can be effectively reduced, the preparation time before processing on the process machine (such as a series of times for tool replacement, cleaning, and equipment reset) can be reduced, the overall overlap degree can be improved, and thus the production and manufacturing efficiency and the overall economic benefits of the enterprise can be improved.

[0003] The workpiece machining link in actual production is only the bottom layer link of the manufacturing process. After the workpiece is processed on the machine, component assembly is required. After the component assembly is completed, it needs to be assembled into a finished product. Each assembly stage needs to refer to the specific Bill of Materials (BOM); and the assembly link directly affects the production efficiency and quality of the finished product. Product assembly is located at the end of the manufacturing process, and the time consumed for assembly accounts for 40%-60% of the total product manufacturing time. Therefore, considering the assembly relationship of workpieces on the basis of a flexible job shop is a typical hierarchical coupling constraint optimization problem, and designing a targeted optimization algorithm for this scenario is the focus of research.

[0004] However, at present, some studies have considered workpiece assembly constraints, but most of them focus on mixed-flow workshops and job shops, and there is less research on flexible job shops; moreover, the scheduling problems of machining processes and assembly processes in flexible job shops have not been combined for research, and there is a lack of targeted algorithm design in this regard. Summary of the Invention

[0005] The purpose of the present invention is to provide a joint scheduling method, system, device and medium for flexible machining and assembly, which can solve the technical problem that the scheduling of machining processes and assembly processes in a flexible job shop cannot be combined at present.

[0006] To solve the above technical problems, an embodiment of the present invention provides a joint scheduling method for flexible machining and assembly, including the following steps: According to the multiple processing steps, multiple assembly steps and the collaborative scheduling process between the processing steps and the assembly steps of multiple workpieces in the manufacturing process, a joint scheduling model is constructed; Batch code, process code and machine code are used to represent the batch quantity of each workpiece, the allocation order of each processing process and each assembly process of each workpiece, and the allocated machine of each processing process and each assembly process of each workpiece in the joint scheduling model; wherein the process code includes the first process code corresponding to all processing processes and the second process code corresponding to all assembly processes, and the second process code is inserted into the first process code according to the assembly dependency relationship between the assembly processes; With the goal of minimizing the maximum completion time of all workpieces, a genetic algorithm is used to solve the joint scheduling model to obtain the target batch quantity of each workpiece, the target allocation order of each processing procedure and each assembly procedure of each workpiece, and the target allocation machine of each processing procedure and each assembly procedure of each workpiece; wherein, before the parent individuals in the genetic algorithm perform crossover mutation, the second process code in the corresponding process code is first eliminated, and then crossover mutation is performed, and after crossover mutation, the process code is restored to include the first process code and the second process code.

[0007] Optionally, the adopting a genetic algorithm to solve the joint scheduling model includes: For the individuals with a preset percentage number in the front of the population in the genetic algorithm, the joint scheduling model is solved with the N5 neighborhood structure as the local search strategy to obtain the target allocation order of each processing procedure and each assembly procedure of each workpiece and the target allocation machine of each processing procedure and each assembly procedure of each workpiece; Among them, the N5 neighborhood structure divides all processing procedures into multiple processing key blocks according to the assigned machines of each processing procedure. For each processing key block, when the processing key block is the first and last processing key block, the first and last processing procedures in the processing key block are exchanged. When the processing key block is not the first and last processing key block, when the number of processing procedures in the processing key block is greater than 2, the first and last processing procedures in the processing key block are exchanged again.

[0008] Optionally, the adopting a genetic algorithm to solve the joint scheduling model includes: The mixed integer linear programming method MILP is used to establish the MILP model of the batch subproblem according to the target allocation order of each processing procedure and each assembly procedure of each workpiece, the target allocation machine of each processing procedure and each assembly procedure of each workpiece, and the following constraints: Constraint 1: ; Constraint 2: ; Constraint 3: ; Constraint 4: ; Constraint 5: ; wherein represents the lot size of the b-th sublot of workpiece i; represents the quantity to be delivered on behalf of workpiece i; is a 0-1 decision variable, indicating that when the b-th sublot of workpiece is a non-empty sublot takes 1, and takes 0 when it is an empty sublot; represents the completion time of the j-th process of the b-th sublot of workpiece i; represents the start time of the j-th process of the b-th sublot of workpiece i; represents the processing time of the j-th process of workpiece i on machine k; represents the setup time of the j-th process of workpiece i on machine k; is a 0-1 decision variable, indicating that when process is processed on the same machine prior to it takes 1, and takes 0 when it is processed after ; takes 0, represents the j-th process of the b-th sublot of workpiece i; The MILP model is solved with the goal of minimizing the maximum completion time of all workpieces to obtain the target lot quantity of each workpiece under the target allocation order and target allocation machine for each processing process and each assembly process of each workpiece.

[0009] Optionally, each gene bit of the lot encoding is the lot quantity of each workpiece; The machine encoding starts from and ends at , and represents the allocation machine of the j-th processing process and assembly process of the b-th sublot of workpiece in ascending order; each gene bit of the process encoding is a pair of numbers representing the b-th sublot of workpiece , and the same pair of numbers will appear multiple times in the process encoding. The

[0010]

[0010] ​​​​​​Optionally, after obtaining the target batch quantity of each workpiece, the target allocation order of each processing operation and each assembly operation of each workpiece, and the target allocation machine of each processing operation and each assembly operation of each workpiece, the method further includes: According to the target batch quantity of each workpiece, the target allocation order of each processing operation and each assembly operation of each workpiece, and the target allocation machine of each processing operation and each assembly operation of each workpiece, determine the start time and completion time of each processing operation and each assembly operation of each workpiece in sequence.

[0011] An embodiment of the present invention further provides a combined scheduling system for flexible processing and assembly, including: A model establishment module, configured to construct a combined scheduling model according to multiple processing operations, multiple assembly operations of multiple workpieces in the production and manufacturing process, and the collaborative scheduling process between each processing operation and each assembly operation; An encoding module, configured to represent the batch quantity of each workpiece, the allocation order of each processing operation and each assembly operation of each workpiece, and the allocation machine of each processing operation and each assembly operation of each workpiece in the combined scheduling model by using batch encoding, operation encoding, and machine encoding; wherein, the operation encoding includes a first operation encoding corresponding to all processing operations and a second operation encoding corresponding to all assembly operations, and the second operation encoding is inserted into the first operation encoding according to the assembly dependency relationship between the assembly operations; A model solving module, configured to take the minimum of the maximum completion time of all workpieces as the goal, and use a genetic algorithm to solve the combined scheduling model to obtain the target batch quantity of each workpiece, the target allocation order of each processing operation and each assembly operation of each workpiece, and the target allocation machine of each processing operation and each assembly operation of each workpiece; wherein, before the parent individuals in the genetic algorithm perform crossover and mutation, first remove the second operation encoding in the corresponding operation encoding, then perform crossover and mutation, and then restore it to the operation encoding including the first operation encoding and the second operation encoding after crossover and mutation.

[0012] An embodiment of the present invention further provides a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, instructions executable by the at least one processor are stored in the memory, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the above-mentioned combined scheduling method for flexible processing and assembly.

[0013] An embodiment of the present invention further provides a computer-readable storage medium, storing a computer program, and when the computer program is executed by a processor, the above-mentioned combined scheduling method for flexible processing and assembly is implemented.

[0014] The joint scheduling method for flexible machining and assembly provided by the present invention has at least the following beneficial effects: First, a joint scheduling model for machining and assembly in the production and manufacturing process of workpieces is established. Among them, the solution of the model is represented by three-layer coding (batch coding, process coding, and machine coding). In the process coding, the coordinated scheduling between machining processes and assembly processes is considered. In the process coding, the assembly process coding (i.e., the second process coding) is inserted into the machining process coding (i.e., the first process coding) according to the assembly dependence relationship between each assembly process. At the same time, when using the genetic algorithm to solve the model, the crossover and mutation operators are improved. Considering the continuity and integrity of machining processes during the crossover and mutation process, before performing crossover and mutation on the parent individuals, the second process coding in the corresponding process coding is first removed, and then crossover and mutation are performed. After crossover and mutation, it is restored to include the original process coding for the execution of the next crossover and mutation operation.

[0015] The present invention not only constructs a joint scheduling model for machining and assembly in the production and manufacturing process of workpieces, considers the coordinated scheduling between machining processes and assembly processes, and based on this model, solves how to achieve joint scheduling of machining processes and assembly processes when the makespan of workpieces is minimized. Moreover, the combination of three-layer coding and improved crossover and mutation operators can accurately represent the joint scheduling problem of machining and assembly, avoiding the appearance of infeasible solutions during the operation of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. These exemplary illustrations do not constitute a limitation on the embodiments.

[0017] Figure 1 is a flowchart of a method for a joint scheduling system of flexible machining and assembly according to an embodiment of the present invention; Figure 2 is a schematic diagram of a three-layer coding scheme according to an embodiment of the present invention; Figure 3 is a flowchart of generating process coding in a coding scheme according to an embodiment of the present invention; Figure 4 is a flowchart of an algorithm framework according to an embodiment of the present invention; Figure 5 is a schematic diagram of two neighborhood structures according to an embodiment of the present invention; Figure 6 is a flowchart of a local search algorithm based on the MILP model for optimizing batch sub-problems according to an embodiment of the present invention; Figure 7A Gantt chart of the optimal solution after decoding according to an encoding scheme provided by an embodiment of the present invention. Detailed implementation manners

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be elaborated in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in the embodiments of the present invention, many technical details are provided for readers to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions required to be protected by the present invention can still be implemented. The following division of each embodiment is for convenience of description and should not constitute any limitation to the specific implementation manner of the present invention. Each embodiment can be combined and cross-referenced with each other on the premise of not being contradictory.

[0019] An embodiment of the present invention relates to a combined scheduling method for flexible machining and assembly. The implementation details of the combined scheduling method for flexible machining and assembly in this embodiment will be specifically described below. The following content is only the implementation details provided for convenience of understanding and is not necessary for implementing this solution.

[0020] The specific process of the combined scheduling method for flexible machining and assembly in this embodiment can be as Figure 1 shown and includes: Step 101: Construct a combined scheduling model according to multiple processing procedures, multiple assembly procedures of multiple workpieces during the production and manufacturing process, and the collaborative scheduling process between each processing procedure and each assembly procedure.

[0021] Specifically, taking the following production and manufacturing scenario as an example, a combined scheduling model is constructed: P types of workpieces are processed and assembled on M machines, where there are machines used to arrange assembly procedures, including assembling parts into sub-assemblies and sub-assemblies into finished products, and the processing and assembly routes are fixed. Each type of workpiece has a certain batch and can be divided into several batches. Each process can be processed on several machines and the processing times are different. After the workpiece completes the processing procedure, it is assembled on the assembly machine according to the bill of materials to obtain the final finished product. The assembly procedure can also be processed on several machines and the assembly times are different. In addition, a series of preparation times such as machine maintenance and tool setting need to be considered between the procedures of two different workpieces.

[0022] In the combined scheduling model, the completion time of each processing procedure , the start time and completion time of the assembly procedure are calculated according to the following formulas: ; ; ; ; ; ; wherein, i and a are the indexes of the workpiece, and their variation ranges are ; is the process index, and its variation range is ; is the machine index, and its variation range is ; is the sub-batch index, and its variation range is ; represents the start time of the -th -th process of the -th sub-batch of the workpiece, represents the start time of the -th -th process of the -th sub-batch of the assembled workpiece, represents the start time of the -th -th process of the -th sub-batch of the workpiece, represents the start time of the -th -th process of the -th sub-batch of the workpiece, represents the batch size of the -th -th sub-batch of the workpiece, represents the processing time of the process on ; represents the set of batches of the lower-level processes that have an assembly dependency on the -th -th process of the -th sub-batch of the assembled workpiece, represents the completion time of the previous process of the -th current sub-batch of the processed workpiece -th -th process, represents the current idle time of the machine ; represents the setup time of the process on ; represents the completion time of the last process of this sub-batch process.

[0023] As can be seen from the above formula, the completion time of a process is equal to the start time of the process plus the processing time of the sub-batch process on the machine, where the processing time is equal to the sub-batch size multiplied by the processing time of the process on the machine; if the previous process on the machine and the current process to be processed do not belong to the same type of workpiece, then the start time needs to be added with the setup time of the machine; the makespan is the maximum value of the completion times of all processes, and the formula for calculating the makespan in the model is: .

[0024] Step 102, use batch coding, process coding, and machine coding to represent the batch quantity of each workpiece, the allocation order of each processing process and each assembly process of each workpiece, and the allocation machines of each processing process and each assembly process of each workpiece in the joint scheduling model; among them, the process coding includes the first process coding corresponding to all processing processes and the second process coding corresponding to all assembly processes, and the second process coding is inserted into the first process coding according to the assembly dependency relationship between the assembly processes.

[0025] Specifically, each gene bit of the batch coding is the batch quantity of each workpiece; the machine coding starts from and ends at , and in ascending order, it represents the allocation machine of the -th sub-batch and the -th processing process and assembly process of the workpiece; each gene bit of the process coding is a pair of numbers , representing the -th sub-batch of the workpiece . The same pair of numbers will appear multiple times in the process coding. The -th appearance represents the -th sub-batch and the -th processing process and assembly process of the workpiece . As shown in

[0026] , a three-layer coding is used to represent the feasible solution of the integrated scheduling problem. The first layer of the three-layer coding is the batch coding, which records the batching scheme of each underlying workpiece; the second layer is the process coding, which records the allocation order of the processes; the third layer is the machine coding, which records the machines allocated to each process. Among them, each gene bit of the batch coding represents the batch quantity of the workpiece, and the batching method adopts equal quantity and consistent batching. If there is a situation where it cannot be divided evenly, then a rounding operation is performed; the machine coding starts from Figure 2 and, in ascending order, allocates machines to each process of each sub-batch of each workpiece; the process coding represents the processing order of the process, and each gene bit of the process coding is a pair of numbers , representing the -th sub-batch of the workpiece ​ For a sub - batch, the same pair of numbers in the process code will appear multiple times. The first occurrence represents the first process of the th sub - batch of the workpiece. The second occurrence represents the second process of the th sub - batch of the workpiece, and so on. During the generation of the process code, the underlying processing process code is generated first, and then the assembly process code is inserted, that is, based on the coding rule of inserting on the right of the assembly process.

[0027] The coding rule of inserting on the right of the assembly process is carried out in two steps. The first step is to randomly generate the codes of all underlying processes according to the conventional process coding generation method. The second step is to traverse the generated process codes from left to right according to the specific assembly dependency relationship, and insert the assembly processes in turn to obtain a complete process code. The specific process is as Figure 3 shown, including the following sub - steps: Step1: Parameter initialization, , the current process code length; Step2: Obtain the number of assembled workpieces ; Step3: Obtain the subordinate assembly relationship of the workpiece, and obtain the number of completed workpieces of all subordinate workpieces; Step4: Judge whether the assembly process of the workpiece can be arranged. If so, insert the assembly process gene position, otherwise directly jump to Step5; Step5: , compare with . If then jump to Step6, otherwise jump to Step4; Step6: , compare with . If then jump to Step7, otherwise jump to Step2; Step7: Obtain the complete process code.

[0028] Step 103: Taking the minimum of the maximum completion time of all workpieces as the goal, use the genetic algorithm to solve the joint scheduling model to obtain the target batch quantity of each workpiece, the target allocation order of each processing process and each assembly process of each workpiece, and the target allocation machine of each processing process and each assembly process of each workpiece; among them, before the parent individuals in the genetic algorithm perform crossover and mutation, first remove the second process code in the corresponding process code, then perform crossover and mutation, and then restore it to the process code including the first process code and the second process code after crossover and mutation.

[0029] In specific implementation, the above improved genetic algorithm framework is as follows: Step1: Setting and initializing the population; Step2: Setting the parameters of the improved genetic algorithm; Step3: Setting the algorithm operation operator (improved crossover and mutation operator); Step4: Invoking the neighborhood structure; Step5: Updating the population; Step6: If the current iteration number of the improved genetic algorithm reaches , then output the global optimal solution of the population; otherwise, jump to Step5 to continue iterating until the algorithm termination condition is met.

[0030] Among them, the improved crossover and mutation operator mainly includes the following three sub-steps to implement crossover and mutation: Step1: For the parent individuals of crossover and mutation, retrieve the gene positions corresponding to the assembly processes in the current code, temporarily remove these gene positions, and obtain two process code chains with exactly the same length; Step2: Execute the crossover and mutation operator to obtain new offspring individuals; Step3: Execute the coding repair operation, and the repair process is the same as the right-insert operation during coding generation.

[0031] In an example, the genetic algorithm framework is adopted at the global search level. In addition, according to the problem characteristics, a neighborhood structure based on the critical path and a local search strategy based on the Mixed Integer Linear Programming (MILP) model are designed at the local search level, which can effectively improve the algorithm solving quality. As Figure 4 shown, the improved genetic algorithm includes the following sub-steps:

[0032] Step1: Parameter setting and initialization; The number of population individuals is , and the initialization method of individuals is random generation. Each individual in the population has a three-layer coding structure, and then the objective value of each individual is calculated according to the encoding and decoding scheme.

[0033] Step 2: Execute the selection operator and the improved crossover and mutation operators; Step 3: Execute the neighborhood structure for the first 30% of the individuals in the population; Step 4: Execute the neighborhood structure based on the MILP model for some high-quality solutions; Step 5: Output the global optimal solution.

[0034] Among them, the neighborhood structure described in the algorithm framework includes: First, for the neighborhood structure based on the critical path, it is improved from the classical N5 neighborhood structure. As Figure 5 shown, when using the genetic algorithm to solve the joint scheduling model, for a preset percentage of individuals in the population in the genetic algorithm, with the N5 neighborhood structure as the local search strategy, solve the joint scheduling model to obtain the target allocation order of each processing operation and each assembly operation of each workpiece and the target allocation machines of each processing operation and each assembly operation of each workpiece.

[0035] The N5 neighborhood structure is to find the critical path of a scheduling solution, divide the operations on the critical path into several critical blocks according to different machines, and then exchange the first and last operations of each critical block. For non-first and non-last critical blocks, the exchange operation is only performed when the length of the critical block is greater than 2.

[0036] In the joint scheduling problem considered in the present invention, due to the existence of assembly operations, there is an assembly dependence relationship between operations. Therefore, the exchange operation of operations cannot be performed arbitrarily. Therefore, in particular, since assembly operations are all carried out on assembly machines, assembly operations will form independent critical blocks, defined as assembly critical blocks. For processing critical blocks, the exchange operation of the first and last operations is normally performed, and no exchange operation is performed on assembly critical blocks. Therefore, the N5 neighborhood structure divides all processing operations into multiple processing critical blocks according to the allocation machines of each processing operation. For each processing critical block, when the processing critical block is the first and last processing critical block, exchange the first and last two processing operations in the processing critical block. When the processing critical block is a non-first and non-last processing critical block, when the number of processing operations in the processing critical block is greater than 2, exchange the first and last two processing operations in the processing critical block.

[0037] Second, the neighborhood structure based on the MILP model: First, establish the MILP model of the batching sub-problem, add constraints according to the machine configuration and processing order in the initial solution, and then call the solver to solve the model to obtain the optimal batching scheme under the current machine configuration and processing order. Finally, encode the solution direction given by the MILP model as an individual and add it to the algorithm to continue the iteration.

[0038] That is, using the MILP method, according to the target allocation order of each processing operation and each assembly operation of each workpiece, the target allocation machines for each processing operation and each assembly operation of each workpiece, and the following constraints, a MILP model is established to optimize the batching scheme of workpieces: Constraint 1: ; Constraint 2: ; Constraint 3: ; Constraint 4: ; Constraint 5: ; In the formula, represents the batch size of the b-th sub-batch of workpiece i; represents the quantity of workpieces i to be delivered; is a 0-1 decision variable, indicating that when the b-th sub-batch of workpiece is a non-empty sub-batch takes 1, and takes 0 when it is an empty sub-batch; represents the completion time of the j-th operation of the b-th sub-batch of workpiece i; represents the start time of the j-th operation of the b-th sub-batch of workpiece i; represents the processing time of the j-th operation of workpiece i on machine k; represents the setup time of the j-th operation of workpiece i on machine k; is a 0-1 decision variable, indicating that when operation is processed before on the same machine takes 1, and takes 0 when it is processed after ; takes 0, represents the j-th operation of the b-th sub-batch of workpiece i.

[0039] Then, aiming at minimizing the maximum completion time of all workpieces, the MILP model is solved to obtain the target batching quantity of each workpiece under the target allocation order and target allocation machines of each processing operation and each assembly operation of each workpiece.

[0040] As Figure 6 shown, the establishment of the neighborhood structure based on the MILP model includes the following sub-steps: Step1: ; Step2: Add Constraint 2 to the non-empty sub-batches in the initial solution; Step3: ; Step4: Determine whether it is 0. If so, jump to Step5; otherwise, add Constraint 1 to the sub-batch of Step5: Step6: Determine the machine configuration in the initial solution Step7: Determine whether it is 1. If so, add Constraint 3 to the starting time of ; otherwise, add Constraint 4 to the starting time of ; Step8: , determine whether it is equal to . If so, jump to Step9; otherwise, jump to Step6; Step9: , determine whether it is equal to . If so, jump to Step10; otherwise, jump to Step4; Step10: , determine whether it is equal to . If so, jump to Ste11; otherwise, jump to Step2; Step11: The loop ends.

[0041] According to the above steps, to verify the performance of the algorithm, this embodiment selects an example for experiment. Among them, the number of jobs is 9, including 5 machining jobs and 4 assembly jobs. The number of operations for each job is 4, 3, 2, 4, 4, 1, 1, 1, 1. The number of machines is 7, including 4 machining machines and 3 assembly machines. In addition, the population size , the elite selection probability is set to 0.2, the crossover probability is 0.85, the mutation probability is 0.15, and the maximum number of iterations is set to 200. Using the above algorithm framework to solve the flexible machining and assembly integrated scheduling problem considering batching, a Gantt chart as shown in Figure 7 can be obtained. The minimum makespan of the optimal solution solved in this embodiment is 1230.

[0042] The combined scheduling method for flexible machining and assembly of the present invention has the following beneficial effects: (1) According to the problem characteristics of the integrated scheduling of machining and assembly, the present invention establishes an integrated scheduling model for flexible machining and assembly considering batching, accurately describes the coupling relationship between machining operations and assembly operations in the production manufacturing process, and can overcome the problems of ignoring the setup time between operations, low machine overlap degree, and low machine utilization rate in traditional scheduling schemes.​

[0043] (2) Two neighborhood structures are designed in the local search stage of the present invention, which have good ability to guide the search direction. One is to change the positions of the processing operations on the critical path of the solution, and the other is to establish a MILP model for the batch sub-problem based on the MILP model, and call the solver to solve the model to obtain the optimal batch plan under the current machine configuration and processing sequence. The two enhance the local search ability of the algorithm on the basis of the efficient global search of the genetic algorithm, and can achieve the efficient exploration of high-quality solutions.

[0044] (3) The encoding scheme based on the right-insertion of assembly operations and the improved crossover and mutation operators designed in the present invention can accurately represent the flexible machining and assembly joint scheduling problem considering batching, avoid the occurrence of infeasible solutions during the operation of the algorithm, and at the same time maintain the integrity of the solution space, which is conducive to the algorithm to efficiently explore and search the solution space of the problem.

[0045] The step division of the above various methods is only for clear description. When implemented, they can be combined into one step or some steps can be split into multiple steps. As long as the same logical relationship is included, they are all within the protection scope of the present invention; adding insignificant modifications or introducing insignificant designs to the algorithm or process, but not changing the core design of its algorithm and process are all within the protection scope of the invention.

[0046] Another embodiment of the present invention relates to a flexible machining and assembly joint scheduling system. The implementation details of the flexible machining and assembly joint scheduling system of this embodiment will be specifically described below. The following content is only the implementation details provided for convenient understanding and is not necessary for implementing the solution. The flexible machining and assembly joint scheduling system of this embodiment includes: A model establishment module, which is used to construct a joint scheduling model according to multiple processing operations, multiple assembly operations and the collaborative scheduling process between each processing operation and each assembly operation of multiple workpieces during the production and manufacturing process; An encoding module, which is used to represent the batch quantity of each workpiece, the allocation order of each processing operation and each assembly operation of each workpiece, and the allocation machine of each processing operation and each assembly operation of each workpiece in the joint scheduling model by using batch encoding, operation encoding and machine encoding; wherein, the operation encoding includes the first operation encoding corresponding to all processing operations and the second operation encoding corresponding to all assembly operations, and the second operation encoding is inserted into the first operation encoding according to the assembly dependency relationship between each assembly operation; The model solving module is used to solve the joint scheduling model by using the genetic algorithm with the goal of minimizing the maximum completion time of all workpieces, and obtain the target batching quantity of each workpiece, the target allocation order of each processing process and each assembly process of each workpiece, and the target allocation machine of each processing process and each assembly process of each workpiece; among them, before the parent individuals in the genetic algorithm perform crossover and mutation, the second process code in the corresponding process code is first removed, and then crossover and mutation are performed, and after crossover and mutation, it is restored to the process code including the first process code and the second process code.

[0047] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in cooperation with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiment are still valid in this embodiment. For the sake of reducing repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0048] It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, units that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0049] Another embodiment of the present invention relates to a computer device, including: 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 joint scheduling method of flexible processing and assembly in the above embodiments.

[0050] Among them, the memory and the processor are connected in a bus manner. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and the memory 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, so they will not be 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, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0051] The processor 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 can be used to store data used by the processor during operation.

[0052] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method embodiments described above are implemented.

[0053] That is, those skilled in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs, etc., which can store program codes.

[0054] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present invention, and in actual applications, various changes can be made in form and details without departing from the spirit and scope of the present invention.

Claims

1. A combined scheduling method for flexible machining and assembly, characterized in that The method includes: Constructing a joint scheduling model according to multiple processing procedures, multiple assembly procedures, and the collaborative scheduling process between each processing procedure and each assembly procedure during the production and manufacturing process of multiple workpieces; Using batch coding, process coding, and machine coding to represent the batch quantity of each workpiece, the allocation order of each processing procedure and each assembly procedure of each workpiece, and the allocated machines of each processing procedure and each assembly procedure of each workpiece in the joint scheduling model; wherein, the process coding includes the first process coding corresponding to all processing procedures and the second process coding corresponding to all assembly procedures, and the second process coding is inserted into the first process coding according to the assembly dependency relationship between the assembly procedures; Taking the minimum of the maximum completion time of all workpieces as the goal, using a genetic algorithm to solve the joint scheduling model to obtain the target batch quantity of each workpiece, the target allocation order of each processing procedure and each assembly procedure of each workpiece, and the target allocated machines of each processing procedure and each assembly procedure of each workpiece; wherein, before the parent individuals in the genetic algorithm perform crossover and mutation, the second process coding in the corresponding process coding is first removed, then crossover and mutation are performed, and after crossover and mutation, it is restored to the process coding including the first process coding and the second process coding.

2. The combined scheduling method for flexible machining and assembly according to claim 1, wherein The step of using a genetic algorithm to solve the joint scheduling model includes: For the individuals with a preset percentage quantity in the population in the genetic algorithm, using the N5 neighborhood structure as the local search strategy to solve the joint scheduling model to obtain the target allocation order of each processing procedure and each assembly procedure of each workpiece and the target allocated machines of each processing procedure and each assembly procedure of each workpiece; Among them, the N5 neighborhood structure divides all processing procedures into multiple processing key blocks according to the allocated machines of each processing procedure. For each processing key block, when the processing key block is the first and last processing key block, the first and last two processing procedures in the processing key block are exchanged. When the processing key block is a non-first and last processing key block and the number of processing procedures in the processing key block is greater than 2, the first and last two processing procedures in the processing key block are also exchanged.

3. The combined scheduling method for flexible machining and assembly according to claim 2, characterized in that, The step of using a genetic algorithm to solve the joint scheduling model includes: Using the mixed integer linear programming method MILP, according to the target allocation order of each processing procedure and each assembly procedure of each workpiece, the target allocated machines of each processing procedure and each assembly procedure of each workpiece, and the following constraints, establishing a MILP model for the batch sub-problem: Constraint 1: ; Constraint 2: ; Constraint 3: ; Constraint 4: ; Constraint 5: ； wherein, represents the lot size of the b-th sublot of workpiece i; represents the quantity to be delivered for workpiece i; is a 0-1 decision variable, indicating that when the b-th sublot of workpiece is a non-empty sublot takes 1, and takes 0 when it is an empty sublot; represents the completion time of the j-th process of the b-th sublot of workpiece i; represents the start time of the j-th process of the b-th sublot of workpiece i; represents the processing time of the j-th process of workpiece i on machine k; represents the setup time of the j-th process of workpiece i on machine k; is a 0-1 decision variable, indicating that when process is processed on the same machine prior to it takes 1, and takes 0 when it is processed after it; takes 0, represents the j-th process of the b-th sublot of workpiece i; Taking the minimum of the maximum completion time of all workpieces as the goal, solving the MILP model to obtain the target batch quantity of each workpiece under the target allocation order and target allocated machines of each processing procedure and each assembly procedure of each workpiece.

4. The combined scheduling method for flexible machining and assembly according to claim 1, characterized in that Each gene bit of the batch coding is the batch quantity of each workpiece; The machine code starts from to ends, and successively represents the allocation machine for the th sub-batch of the workpiece th processing process and assembly process in ascending order; Each gene position of the process code is a pair of numbers , representing the workpiece 's th sub-batch. The same pair of numbers will appear multiple times in the process code. The th appearance represents the th sub-batch of the workpiece 's th processing and assembly process.

5. The combined scheduling method for flexible machining and assembly according to claim 1, characterized in that, After obtaining the target batch quantity of each workpiece, the target allocation order of each processing procedure and each assembly procedure of each workpiece, and the target allocated machines of each processing procedure and each assembly procedure of each workpiece, it further includes: According to the target batch quantity of each workpiece, the target allocation order of each processing operation and each assembly operation of each workpiece, and the target allocation machines of each processing operation and each assembly operation of each workpiece, determine the start time and completion time of each processing operation and each assembly operation of each workpiece in sequence.

6. A combined scheduling system for flexible machining and assembly, characterized in that, The system includes: A model establishment module, configured to construct a joint scheduling model according to multiple processing operations, multiple assembly operations of multiple workpieces during the production and manufacturing process, and the collaborative scheduling process between each processing operation and each assembly operation. An encoding module, configured to represent the batch quantity of each workpiece, the allocation order of each processing operation and each assembly operation of each workpiece, and the allocation machines of each processing operation and each assembly operation of each workpiece in the joint scheduling model by using batch encoding, operation encoding, and machine encoding; wherein, the operation encoding includes a first operation encoding corresponding to all processing operations and a second operation encoding corresponding to all assembly operations, and the second operation encoding is inserted into the first operation encoding according to the assembly dependency relationship between the assembly operations. A model solving module, configured to take the minimum of the maximum completion time of all workpieces as the goal, and use a genetic algorithm to solve the joint scheduling model to obtain the target batch quantity of each workpiece, the target allocation order of each processing operation and each assembly operation of each workpiece, and the target allocation machines of each processing operation and each assembly operation of each workpiece; wherein, before the parent individuals in the genetic algorithm perform crossover and mutation, first remove the second operation encoding in the corresponding operation encoding, then perform crossover and mutation, and then restore it to an operation encoding including the first operation encoding and the second operation encoding after crossover and mutation.

7. A computer device, characterized in that, Including: 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 joint scheduling method for flexible processing and assembly according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the joint scheduling method for flexible processing and assembly according to any one of claims 1 to 5.

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