Multi-target workshop scheduling method considering workpiece quality and variable machining time

Abstract

The invention discloses a multi-target workshop scheduling method considering workpiece quality and variable processing time, and belongs to the technical field of workshop production scheduling optimization. The method comprises the following steps: solving a scheduling model by adopting a culture gene algorithm based on collaborative classification; adopting heuristic hybrid initialization to obtain a high-quality initial population; according to the model, dynamic monitoring of the reworking procedure is added on plug-in active decoding, and it is ensured that the reworking procedure meets the immediate scheduling; a collaborative search operator is added on the basis of a hybrid individual updating strategy, and the global search capability in the later stage of iteration is improved; for the characteristic that the processing time is not fixed, plug-in optimization based on an idle time window is provided, and the utilization rate of the idle time in a scheduling scheme is improved; in the local search stage, classified local search is adopted, elite individuals are substituted into multi-neighborhood search based on key procedures to improve the search ability, representative individuals are generated by adopting K-means clustering and substituted into a learning type hybrid redistribution strategy, and individual evolution is accelerated while population diversity is guaranteed.

Description

Technical Field

[0001] The invention belongs to the technical field of workshop production scheduling optimization, and in particular relates to a multi-objective workshop scheduling method considering workpiece quality and variable processing time. Background Art

[0002] According to the requirements of production management and control in Industry 4.0, the production scheduling of flexible job shops is currently facing many challenges. Scheduling planning, resource allocation, and product transportation and maintenance have become more complex and multidimensional than before. The dual resource constrained flexible job shop scheduling problem (DRCFJSP) refers to the mutual influence and constraint relationship between two or more shared resources (such as machines and workers) in the production workshop. The scheduling problem in this case is more in line with the actual production mode of most enterprises. At the same time, the scheduling planning of this production mode is also more complex and difficult.

[0003] In DRCFJSP, different workers often have different autonomous learning abilities, which has an important impact on production efficiency. Considering these factors to design a suitable production scheduling plan is more in line with actual production. Similarly, due to the process flow of the process or the wear and aging of the machine itself, the processing time of the process will also be extended accordingly.

[0004] Due to the participation of worker resources, the finished quality of DRCFJSP's products is relatively high. However, due to the differences in machine processing accuracy and worker operating levels, the quality of products is often uneven, resulting in a waste of resources, which is not in line with the company's ultimate goal of reducing costs, increasing efficiency and achieving profitability. Summary of the invention

[0005] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a multi-objective workshop scheduling method that takes into account workpiece quality and variable processing time, thereby solving the problems in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The multi-objective shop scheduling method considering workpiece quality and variable processing time includes the following steps:

[0008] S1, a multi-objective dual-resource constraint flexible job shop scheduling model considering workpiece quality and variable processing time is established, and an improved cultural gene algorithm based on collaborative classification is proposed to solve it;

[0009] S2, determine the parameters of the improved genetic algorithm based on collaborative classification, including: population size popsize, maximum number of iterations maxgen, crossover probability pc, and mutation probability pm;

[0010] S3, determine the individual coding method based on the processing data, and a single individual includes four layers of gene sequences: process sorting OS, machine selection MS, worker selection WS and process rework PR;

[0011] S4, OS sequence is initialized randomly, MS sequence and WS sequence are initialized with quality-preserving GLR mixture to generate the initial population R1(t) as the parent population;

[0012] S5, for each individual in the parent population R1(t), a real-time monitoring plug-in active decoding considering rework is used to generate an active scheduling plan, and the three target values ​​of the individual are recorded according to the scheduling plan. The individuals in the population R1(t) are quickly non-dominated and sorted, and the individuals with dominance level 1 are saved to construct the frontier solution set;

[0013] S6, the parent population R1(t) is used to generate the branch population R2(t) through the Jaya operator;

[0014] S7, the parent population R1(t) is used to generate the branch population R3(t) through the genetic operator;

[0015] S8, mix R1(t), R2(t) and R3(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time;

[0016] S9, determine whether the individual in the frontier solution set has changed, if yes, go to S11, otherwise go to S10;

[0017] S10, generate branch population R4(t) by collaborative search operators of R2(t) and R3(t), mix R1(t) and R4(t) for fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time;

[0018] S11, implement the idle time window-based insertion optimization for all individuals in the parent population R1(t), and update the population R1(t);

[0019] S12, implement K-means clustering algorithm on the parent population R1(t) to extract the branch population R5(t), apply learning-type hybrid redistribution operation to the individuals in R5(t), and update R5(t); at the same time, perform fast non-dominated sorting on the individuals in the parent population R1(t), select the individuals with the highest dominance level according to the population percentage to generate the branch population R6(t), perform multi-neighborhood search operation based on key processes on R6(t), and update R6(t);

[0020] S13, mix R1(t), R5(t) and R6(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time;

[0021] S14, determine whether the current number of iterations t reaches the set number of iterations, if not, then t = t + 1, repeat S5-S13, if so, then output the frontier solution set in the population as the optimal solution set.

[0022] Beneficial effects of the present invention:

[0023] 1. Based on DRCFJSP, the present invention further considers the learning effect of workers, the deterioration effect of machines and the rework mechanism of the process, and specifically designs an improved cultural gene algorithm based on collaborative classification for solving the problem; firstly, in view of the fact that there is secondary rework in the precision process in the model, a rework process processing mechanism is added based on active decoding to avoid the generation of illegal solutions.

[0024] 2. Based on the hybrid individual update based on the improved Jaya operator and genetic operator, a collaborative search mechanism is constructed to expand the population search range while improving the continuous search efficiency.

[0025] 3. In view of the dynamic changes of processing time in the model, a plug-in optimization based on idle time windows is designed to improve resource utilization by compressing idle time windows.

[0026] 4. In the local search phase, the individuals in the population are classified and searched. K-means clustering is used to generate representative individuals for all individuals in the population, and they are optimized through a learning redistribution strategy to reduce individual homogeneity while improving the individual evolution level. The individuals with a dominance level of 1 are promoted to further convergence through multi-neighborhood search of key processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 It is a schematic diagram of the scheduling method flow of the present invention;

[0029] Figure 2 It is a schematic diagram of an encoding example of the present invention;

[0030] Figure 3 It is a schematic diagram of IPOX crossover of the present invention;

[0031] Figure 4 It is a schematic diagram of the RPX crossover of the present invention;

[0032] Figure 5 It is a schematic diagram of OS sequence update of discrete Jaya of the present invention;

[0033] Figure 6 It is a schematic diagram of updating the MS and WS sequences of discrete Jaya of the present invention;

[0034] Figure 7 It is a schematic diagram of OS sequence collaborative update of the present invention;

[0035] Figure 8 It is a schematic diagram of the coordinated update of MS and WS sequences of the present invention;

[0036] Fig. 9 It is a schematic diagram of the plug-in optimization update based on the idle time window of the present invention;

[0037] Fig.10 Schematic diagram of the optimal solution set of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] like Figure 1 As shown in FIG. 1 , the multi-objective shop scheduling method considering workpiece quality and variable processing time includes the following steps:

[0041] S1, establish a multi-objective dual-resource constraint flexible job shop scheduling model considering workpiece quality and variable processing time, and propose an improved cultural gene algorithm based on collaborative classification to solve it;

[0042] The multi-objective DRCFJSP considering workpiece quality and variable processing time can be described as: w workers operate m machines to process n workpieces, and any workpiece J i There is a certain η i The processing of a single process requires the allocation of machines and workers at the same time. There are differences between different machines and workers. When the machine and worker resources are allocated, the process can be processed. The standard processing time of the process varies from machine to machine. The actual processing time of the process is determined by three factors: the standard processing time, the deterioration effect of the machine, and the skill level of the worker with learning effect.

[0043] First, the process determines the standard processing time through the assigned machine, and the degree of deterioration of the machine is judged to obtain the processing time after deterioration. The deterioration effect of the machine refers to the phenomenon that in actual processing, the processing time of the process is extended due to the process flow of the process or the wear and aging of the machine itself. Considering that in actual production, the linear deterioration effect will cause the processing time of subsequent processes to be extended indefinitely, the deterioration effect model adopts a step-by-step deterioration effect and a piecewise function with an upper limit to describe the actual processing time, which converts the deteriorated machine processing time into It can be expressed as the standard processing time T ijk With penalty time PT ijk The specific expression is as follows:

[0044]

[0045] In the formula, the cumulative processing time AT is recorded after the machine starts processing k , the cumulative processing time reaches the lower limit of the deterioration threshold Previous penalty time PT ijk is 0, within the upper and lower limits of the deterioration threshold, the penalty time PT ijk It increases linearly and reaches the upper limit of the deterioration threshold After that, the penalty time PT ijk Constant.

[0046] Secondly, the worker determines the current skill level of the worker through the learning effect, and the actual processing time is obtained according to the deteriorated machine processing time and the worker's current skill level. The present invention obtains the following improved learning effect model based on five factors, namely the worker's initial skill level, work difficulty, learning ability, operation repetition number, and skill upper limit:

[0047] β=lg(γ kl ) / lg2

[0048]

[0049] Among them, β represents the learning factor, γ kl W l To M k The learning rate, γ kl ∈[0,1],T ijkl Indicates process O ij In M k W l The actual processing time of the operation, T ijk Indicates process O ij In M k Standard processing time on W l Operation M k Initial skill level, W l Operation M k The skill limit, F is the incompressible factor, expressed as the man-machine ratio, r kl W l Operation M k The cumulative number of times;

[0050] Since the performance of machines decreases in a step-by-step manner and the skill level of workers increases dynamically, the actual processing time of a process changes dynamically. There are precision workpieces and ordinary workpieces in the same batch of processing tasks. Among them, all processes of precision workpieces have quality requirements. If the quality requirements are not met, the original process will be reworked immediately after completion, and the workers and machines used are the same as the original process. Processing machines are also divided into CNC machines and ordinary machines. The processing accuracy of CNC machines is only related to the machine itself, while the processing accuracy of ordinary machines is related to both the machine and the workers.

[0051] The multi-objective solution of scheduling objectives considering workpiece quality and variable processing time is to determine the processing order of all processes, efficiently allocate machines and workers to each process within the range of optional flexible resource sets, and avoid secondary rework of processes as much as possible to obtain the best scheduling solution. The mathematical description is as follows:

[0052] Construct a flexible job shop scheduling model with dynamic processing time and dual resource constraints considering workpiece quality. Objective function:

[0053] min(f1)=min(max(C i ))

[0054]

[0055] The above three formulas represent the three objective functions of the scheduling model, which respectively take the minimum maximum completion time of all workpieces, the minimum total energy consumption of machines and the minimum total worker cost as the goals; where f1, f2, f3 represent the first, second and third objective functions of the scheduling model, n represents the total number of workpieces, m represents the total number of machines, w represents the total number of workers, i = 1, 2, 3, ..., n represents the workpiece index, j = 1, 2, 3, ..., η i represents the process index, k=1,2,3,…,m represents the machine index, l=1,2,3,…,w represents the worker index, η i represents the total number of processes for the i-th job, T ijkl Indicates O ij In M k W l Processing time for processing, Indicates M k No-load energy consumption, Indicates M k Load energy consumption, CM k Indicates M k Completion time, X ijkl =1 indicates process O ij Processed by worker l on machine k, otherwise X ijkl =0,wc l W l The unit cost, C i represents the completion time of job i.

[0056] The constraints of the dual-resource constraint flexible job shop scheduling model with dynamic processing time considering workpiece quality are set as follows:

[0057] Process O ij Mass calculation during machining:

[0058]

[0059] The processing time of the rework operation is constrained by the quality threshold:

[0060]

[0061] The rework process is the subsequent process of the original process and meets the sequence constraints of the processes:

[0062]

[0063] The rework process uses the same machines and workers as the original process:

[0064]

[0065] The machining process of the workpiece cannot be interrupted:

[0066]

[0067] There are sequence constraints for the processes in the same workpiece:

[0068]

[0069] The same machine can only process one workpiece at any one time:

[0070] ST ij +α(1-Y i'j'ijk )≥CT i'j'

[0071] The same worker can only process one workpiece at any one time:

[0072] ST ij +α(1-Z i'j'ijl )≥CT i'j'

[0073] Any process can only be performed by one worker on one machine:

[0074]

[0075] The start time and completion time of any process are greater than or equal to 0:

[0076] ST ij ≥ 0, CT ij ≥0

[0077] Where i'=1,2,3,…,n represents the workpiece index, j'=1,2,3,…,η i Indicates the process index, Indicates O ij Secondary rework process, express In M k W l Processing time for processing, express The start time, express The completion time, ρ k Indicates M k Machining accuracy, ε l W l Processing accuracy, Q ij Indicates O ij Processing quality, QT ij Indicates O in precision workpiece ij The quality threshold, λ ij Indicates O ij Rework factor, ST ijIndicates process O ij Start time, CT ij Indicates process O ij Completion time, ST i(j+1) Indicates process O i(j+1) Start time, CT i'j' Indicates process O i'j' Completion time, α represents an infinite integer, Y i'j'ijk =1 indicates process O ij With O i'j' Processing on machine k and process O i'j' Before O ij Processing, other conditions Y i'j'ijk =0, Z i'j'ijl =1 indicates process O ij With O i'j' Processed by worker l and process O i'j' Before O ij Processing, other cases Z i'j'ijl =0,H k =1 means M k It is a normal machine, otherwise H k =1 / ε l , U i =1 means J i It is a precision workpiece, otherwise U i =∞,Re ij =1 means Q ij U i ≤QT ij , other cases ij =0, express In M k By W l Processing, other situations

[0078] S2, determine the parameters of the improved genetic algorithm based on collaborative classification, including the population size popsize, the maximum number of iterations maxgen, the crossover probability pc, and the mutation probability pm;

[0079] Taking DMK06 of moderate complexity as an example, we designed L9 (4 3 ) level orthogonal experiments were performed, and 9 combinations were run 20 times respectively. The signal-to-noise ratio (S / N) of each parameter combination was compared, and the average signal-to-noise ratio of each parameter was calculated. The algorithm has the best performance when the parameters are set to popsize = 200, pm = 0.1, pc = 0.8, and maxgen = 200.

[0080] S3, determine the individual coding method according to the processing data in the production instance, and a single individual includes four layers of gene sequences: process sorting OS, machine selection MS, worker selection WS and process rework PR;

[0081] like Figure 2 As shown, the present invention adopts a four-segment coding method to express individual information. The first segment is the operation sequencing coding sequence (OS), which is used to determine the order of workpiece processing. The elements in each gene bit represent the workpiece number, and the different times of the same element appearing represent different processes in the same workpiece; the second segment is the machine selection coding sequence (MS), which represents the processing machine assigned to the process. Each gene bit represents the processing machine corresponding to each process after all processes are arranged in sequence, and the elements in the gene bit represent the machine number; the third segment is the worker selection coding sequence (WS), which indicates the processing worker selected by the process. Each gene bit represents the operator of the processing machine corresponding to each process after all processes are arranged in sequence, and the elements in the gene bit represent the worker number; the fourth segment is the process rework coding sequence (PR), which indicates the process that needs to be reworked. The gene bit represents the rework information of each process after all processes are arranged in sequence. The element "1" in the gene bit indicates that the machine and worker selected during the processing of the process do not meet the accuracy requirements and need secondary rework, and "0" indicates that the process is processed normally.

[0082] S4, OS sequence is initialized randomly, MS sequence and WS sequence are initialized with quality-preserving GLR mixture to generate the initial population R1(t) as the parent population;

[0083] When generating the initial population R1(t), the OS code is generated by random selection. The MS sequence and WS sequence adopt the quality-preserving GLR initialization method which combines global selection (GS), local selection (LS) and random selection (RS). In the initialization stage, the changes in processing time caused by learning effect and deterioration effect are considered, and the quality of precision workpieces is guaranteed. The usage ratio of the three initialization methods is 6:3:1.

[0084] S5, for each individual in the parent population R1(t), a real-time monitoring plug-in active decoding considering rework is used to generate an active scheduling plan, and the three target values ​​of the individual are recorded according to the scheduling plan. The individuals in the population R1(t) are quickly non-dominated and sorted, and the individuals with dominance level 1 are saved to construct the frontier solution set;

[0085] Real-time monitoring plug-in active decoding considering rework is adopted to distinguish the various idle times brought by dual flexible resources, implement different left-shift insertion strategies, and recalculate the processing time of the process in the current scheduling plan after the left-shift insertion, and perform a left-shift to generate an active scheduling plan, reasonably arrange the rework process for it, and calculate the corresponding multiple objective functions.

[0086] Specifically, the process of real-time monitoring plug-in active decoding includes:

[0087] Step 1: Traverse the process O in the OS sequence in order ij , and O in MS and WS sequences ij Corresponding processing machine M k and worker W l , analyze the PR sequence, if the corresponding value is 1, generate a rework process

[0088] Step 2: Analyze M k and W l There are four types of start-up states, state 1 is M k and W l Not started, status 2 is M k There is an idle period [MFST k ,MFCT k ], state 3 is W l There is an idle period [WFST l ,WFCT l ], state 4 is M k and W l There is free time [MSFT k ,MFCT k ] and [WFST l ,WFCT l ], status 2, 3, 4 need to record M k and W l Processing time [MST k ,MCT k ] and [WST l ,WCT l ], determine whether there is an idle time period available for left shift insertion, if so, go to step 3, otherwise, go to step 4.

[0089] Step 3: According to M k and W l The state is O ij Provide the earliest start time, if O ij The first process of the workpiece is to make its CT i(j-1) Exists and is 0, state 1 in ST ij =CT i(j-1), State 2, ST ij =max(MFST k ,WCT l ,CT i(j-1) ), in state 3, ST ij =max(WFST l ,MCT k ,CT i(j-1) ), in state 4, ST ij =max(max(MFST k ,WFST l ),CT i(j-1) ), O ij The completion time is CT ij =ST ij +T ijkl After the left shift insertion is executed, the processing time of the completed process is recalculated according to the learning and deterioration effect formula, and the immediate left shift is performed to update ST and CT;

[0090] Step 4: According to M k and W l The state is O ij Provide the earliest start time ST ij =max(MCT k ,WCT l ,CT i(j-1) ), if O ij The first process of the workpiece is to make its CT i(j-1) Exists and is 0, the completion time is CT ij =ST ij +T ijkl .

[0091] Step 5: Determine whether there is a rework process. If so, arrange it to be processed after the original process.

[0092] Step 6: Record O ij The start time of ST ij =ST(O ij ) and completion time CT ij =CT(O ij ), if the rework process exists, record it together. Determine whether the OS sequence is traversed. If so, go to step 7, otherwise go to step 1.

[0093] Step 7: Set ST and ST according to the start time of all processes (including rework processes) re With completion time set CT and CT re , calculate the three objective functions of the scheduling plan and output them.

[0094] S6, the parent population R1(t) is used to generate the branch population R2(t) through the Jaya operator;

[0095] In the process of using discrete Jaya operator to generate branch population R2(t) from population R1(t), X best is a randomly selected individual from the frontier solution set, X worst is a randomly selected individual from the worst Pareto rank individuals in the current population;

[0096] like Figure 5 As shown: The update process of the OS sequence is: delete the parent individual P i With X worst The same elements at the same position generate the intermediate individual temp, and delete X best The same elements as temp, and finally X best The remaining elements in are assigned to temp in sequence to generate the descendant individual C i .

[0097] like Figure 6 As shown: The update process of MS and WS sequences is: delete the parent individual P i With X worst The same element at the same position generates the intermediate individual temp, and X best The element at the corresponding position is assigned to temp to generate the offspring individual C i .

[0098] Parent individual P i and the worst individual X worst The element values ​​of all gene positions may be completely identical or completely different. When they are completely identical, the parent individual P is reset using the initialization rule S4 of the present invention to generate the child individual C. i ; When completely different, P i With X worst and X best Each performs an improved precedence operation crossover (IPOX) and selects the best offspring individual as C i ; When they are not exactly the same, the Jaya update operator is executed.

[0099] S7, the parent population R1(t) is used to generate the branch population R3(t) through the genetic operator;

[0100] like Figure 3 and Figure 4As shown in the figure, the genetic operators include selection, random parent crossover and multi-level mutation, among which the ternary tournament selection is used as the selection operator. Regarding the crossover operator, IPOX is used for the OS sequence, and random probability crossover (RPX) is used for the MS and WS sequences. Regarding the mutation operator, the exchange mutation method is used for the OS sequence, and the random replacement mutation is used for the MS and WS sequences.

[0101] S8, mix R1(t), R2(t) and R3(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time;

[0102] When saving the first popsize individuals to form the offspring population R1'(t), the selection rule is: give priority to individuals with high Pareto levels, and give priority to individuals with high crowding in the same level. After the selection is completed, R1'(t) is assigned to the parent population R1(t); the population sizes of R1(t), R2(t), R3(t) and R1'(t) are the same.

[0103] S9, determine whether the individuals in the frontier solution set have changed, if so, go to S11, otherwise go to S10.

[0104] S10, such as Figure 7 and Figure 8 As shown: R2(t) and R3(t) are used in a collaborative search operator to generate a branch population R4(t), R1(t) and R4(t) are mixed for fast non-dominated sorting, all individuals are arranged in order of dominance, the first popsize individuals are saved to form a child population R1'(t) and replace the parent population, the updated parent population R1(t) is obtained, and the frontier solution set is updated at the same time;

[0105] S11, implement idle time window-based insertion optimization for all individuals in the parent population R1(t), and update the population R1(t);

[0106] like Fig. 9 As shown in the figure: when the insertion optimization based on idle time windows is adopted for all individuals in the population R1(t), the separated idle time windows are integrated by right shifting the non-critical processes in the scheduling scheme, and then all processes are traversed to search for the processes that can be inserted and shifted left to perform left shift insertion. After the traversal is completed, all processes are implemented with tight front scheduling, which can effectively achieve the reduction of idle time and the improvement of resource utilization.

[0107] S12, implement K-means clustering algorithm on the parent population R1(t) to extract the branch population R5(t), apply learning-type hybrid redistribution operation to the individuals in R5(t), and update R5(t); at the same time, perform fast non-dominated sorting on the individuals in the parent population R1(t), select the individuals with the highest dominance level according to the population percentage to generate the branch population R6(t), perform multi-neighborhood search operation based on key processes on R6(t), and update R6(t);

[0108] When the learning hybrid redistribution operation is used for individuals in R5(t), there are four redistribution operators: L1-L4:

[0109] L1: Randomly select a process on the machine with the largest maximum completion time, traverse the optional machines of the process in ascending order of completion time, replace the element value of the corresponding position of the process in the MS sequence, and select the optional worker with the shortest processing time for the process, replace the element value of the corresponding position of the WS sequence, and update the PR sequence to obtain a new individual;

[0110] L2: Randomly select a process from the workers with the largest cumulative processing time, traverse the optional workers of the process in ascending order of cumulative processing time, replace the element value of the corresponding position of the process in the WS sequence, and select the optional machine with the shortest processing time, update the element value of the corresponding position of the MS sequence, and update the PR sequence to obtain a new individual;

[0111] L3: Extract the machine with the lowest energy consumption, randomly select a process that can be processed by the machine with the lowest energy consumption on the machines other than this machine, replace the optional machine of this process with the machine with the lowest energy consumption, replace the element value of the corresponding position of this process in the MS sequence, and select the optional worker with the shortest processing time for this process, replace the element value of the corresponding position of the WS sequence, and update the PR sequence to obtain a new individual;

[0112] L4: Extract the worker with the minimum total worker cost, randomly select a process that can be processed by the worker with the minimum cost from the workers other than this worker, replace the optional worker of this process with the worker with the minimum total worker cost, replace the element value of the corresponding position of this process in the WS sequence, select the optional machine with the shortest processing time for this process, replace the element value of the corresponding position of the MS sequence, and update the PR sequence to obtain a new individual.

[0113] The population R1(t) is quickly non-dominated sorted. After sorting R1(t), the individuals with dominance level 1 are subjected to knowledge-driven variable neighborhood search. The maximum number of iterations of local search is set. The variable neighborhood search is implemented using the threshold method. First, multiple neighborhood structures are defined, each corresponding to a method of changing individual information. During the search, the current individual is substituted into different neighborhood structures to try to generate a new individual, which is then evaluated by non-adaptive sorting after being substituted into the decoding operator. If the new individual is better than the current individual, the current individual is accepted and updated. Otherwise, another neighborhood structure is selected for trial. After all neighborhood structures are tried once, the number of iterations is increased by one. When the maximum number of iterations is met, the search ends and the optimal individual found is output. There are three neighborhood structures N1-N3 in total. The operations performed by each neighborhood structure are as follows:

[0114] N1: Shift the non-critical processes in the scheduling plan rightward to integrate idle time; after disrupting the order of processes on the critical path, traverse all the optional workers of the critical processes to check whether the process can be inserted by the worker by shifting left. If so, stop traversal after the insertion and generate a new individual; otherwise, select the worker with the smallest worker cost to replace and generate a new individual.

[0115] N2: Traverse the key blocks in the critical path. If the key block contains more than two processes, randomly extract the middle process of the key block to replace the first process and the last process of the block to generate a new individual.

[0116] N3: Randomly select one of the machine and worker constraints, randomly extract a key process on the machine (or worker) with the largest maximum completion time, analyze the optional machines (or workers) of the selected process, replace the processing machine (or worker) of the process with the optional machine (or worker) with the smallest maximum completion time, and generate a new individual.

[0117] S13, mix R1(t), R5(t) and R6(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time;

[0118] S14, determine whether the current number of iterations t reaches the set number of iterations, if not, then t = t + 1, repeat S5-S13, if so, then output the frontier solution set in the population as the optimal solution set.

[0119] Example 2

[0120] In this embodiment, a specific production example is given, and the scheduling method of the present invention is used to solve the production example;

[0121] Production example: Based on the actual production data of an aviation equipment manufacturing enterprise, the aviation structural parts production example DHKQ01 was obtained after simplification and organization. Table 1 gives the resource constraint information of the process in the DHKQ01 example. There are 6 workers and 8 equipment in the workshop, which are divided into 4 equipment groups. The equipment and workers corresponding to each group are different. This production workshop needs to process 8 aviation structural parts. The process flow of each structural part is different. The specific process and equipment processing information are shown in Table 2. The workers in this production workshop have different initial skill levels for operating different equipment, and the learning rate and skill level upper limit of different workers are also different. The corresponding relationship between machines and workers is shown in Table 3. The 8 equipment in the workshop have different no-load energy consumption and load energy consumption, and the unit time cost of the 6 workers is also high and low, as shown in Table 4.

[0122] Table 1 Optional machine and worker information for the process

[0123]

[0124] Table 2DHKQ01 workpiece processing information

[0125]

[0126]

[0127] Table 2DHKQ01 processing information (continued)

[0128]

[0129] Table 3 Worker attributes

[0130]

[0131] Table 4 Machine attribute table

[0132]

[0133]

[0134] The scheduling method of the present invention solves the frontier solution set generated by the production instance to obtain the corresponding three-dimensional point diagram as shown in Fig.10 As shown, the x-axis represents the maximum completion time, the y-axis represents the total energy consumption of the machine, and the z-axis represents the total cost of the workers. Fig.10 It can be seen that the solutions obtained by the algorithm of the present invention are uniformly dense and closer to the origin than other algorithms, which verifies the effectiveness of ICMA in solving actual production cases.

[0135] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0136] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A multi-objective shop scheduling method considering workpiece quality and variable processing time, characterized in that: The following steps are involved: S1, a multi-objective dual-resource constraint flexible job shop scheduling model considering workpiece quality and variable processing time is established, and an improved cultural gene algorithm based on collaborative classification is proposed to solve it; S2, determine the parameters of the improved genetic algorithm based on collaborative classification, including: population size popsize, maximum number of iterations maxgen, crossover probability pc, and mutation probability pm; S3, determine the individual coding method based on the processing data, and a single individual includes four layers of gene sequences: process sorting OS, machine selection MS, worker selection WS and process rework PR; S4, OS sequence is initialized randomly, MS sequence and WS sequence are initialized with quality-preserving GLR mixture to generate the initial population R1(t) as the parent population; S5, for each individual in the parent population R1(t), a real-time monitoring plug-in active decoding considering rework is used to generate an active scheduling plan, and the three target values ​​of the individual are recorded according to the scheduling plan. The individuals in the population R1(t) are quickly non-dominated and sorted, and the individuals with dominance level 1 are saved to construct the frontier solution set; S6, the parent population R1(t) is used to generate the branch population R2(t) through the Jaya operator; S7, the parent population R1(t) is used to generate the branch population R3(t) through the genetic operator; S8, mix R1(t), R2(t) and R3(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time; S9, determine whether the individual in the frontier solution set has changed, if yes, go to S11, otherwise go to S10; S10, generate branch population R4(t) by collaborative search operators of R2(t) and R3(t), mix R1(t) and R4(t) for fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time; S11, implement the idle time window-based insertion optimization for all individuals in the parent population R1(t), and update the population R1(t); S12, implement K-means clustering algorithm on the parent population R1(t) to extract the branch population R5(t), apply learning-type hybrid redistribution operation to the individuals in R5(t), and update R5(t); at the same time, perform fast non-dominated sorting on the individuals in the parent population R1(t), select the individuals with the highest dominance level according to the population percentage to generate the branch population R6(t), perform multi-neighborhood search operation based on key processes on R6(t), and update R6(t); S13, mix R1(t), R5(t) and R6(t) and perform fast non-dominated sorting, arrange all individuals in order of dominance level, save the first popsize individuals to form the child population R1'(t) and replace the parent population, obtain the updated parent population R1(t), and update the frontier solution set at the same time; S14, determine whether the current number of iterations t reaches the set number of iterations, if not, then t = t + 1, repeat S5-S13, if so, then output the frontier solution set in the population as the optimal solution set.

2. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: The objective function of the multi-objective dual-resource flexible job shop scheduling model considering workpiece quality and variable processing time is: min(f1)=min(max(C i )) Where f1, f2, f3 represent the first, second and third objective functions of the scheduling model respectively, n represents the total number of workpieces, m represents the total number of machines, w represents the total number of workers, i = 1, 2, 3, ..., n represents the workpiece index, j = 1, 2, 3, ..., η i represents the process index, k=1,2,3,…,m represents the machine index, l=1,2,3,…,w represents the worker index, η i represents the total number of processes for the i-th job, T ijkl Indicates O ij In M k W l Processing time for processing, Indicates M k No-load energy consumption, Indicates M k Load energy consumption, CM k Indicates M k Completion time, X ijkl =1 indicates process O ij Processed by worker l on machine k, otherwise X ijkl =0,wc l W l The unit cost, C i represents the completion time of workpiece i; The constraints of the multi-objective dual-resource flexible job shop scheduling model considering workpiece quality and variable processing time include: Process O ij Mass calculation during machining: The processing time of the rework operation is constrained by the quality threshold: The rework process is the subsequent process of the original process and meets the sequence constraints of the processes: The rework process uses the same machines and workers as the original process: The machining process of the workpiece cannot be interrupted: There are sequence constraints for the processes in the same workpiece: The same machine can only process one workpiece at any one time: ST ij +α(1-Y i'j'ijk )≥CT i'j' The same worker can only process one workpiece at any one time: ST ij +α(1-Z i'j'ijl )≥CT i'j' Any process can only be performed by one worker on one machine: The start time and completion time of any process are greater than or equal to 0: ST ij ≥0,CT ij ≥0 Where i'=1,2,3,…,n represents the workpiece index, j'=1,2,3,…,η i Indicates the process index, Indicates O ij Secondary rework process, express In M k W l Processing time for processing, express The start time, express The completion time, ρ k Indicates M k Machining accuracy, ε l W l Processing accuracy, Q ij Indicates O ij Processing quality, QT ij Indicates O in precision workpiece ij The quality threshold, λ ij Indicates O ij Rework factor, ST ij Indicates process O ij Start time, CT ij Indicates process O ij Completion time, ST i(j+1) Indicates process O i(j+1) Start time, CT i'j' Indicates process O i'j' Completion time, α represents an infinite integer, Y i'j'ijk =1 indicates process O ij With O i'j' Processing on machine k and process O i'j' Before O ij Processing, other conditions Y i'j'ijk =0, Z i'j'ijl =1 indicates process O ij With O i'j' Processed by worker l and process O i'j' Before O ij Processing, other cases Z i'j'ijl =0,H k =1 means M k It is a normal machine, otherwise H k =1 / ε l , U i =1 means J i It is a precision workpiece, otherwise U i =∞,Re ij =1 means Q ij U i ≤QT ij , other cases ij =0, express In M k By W l Processing, other situations 3. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: S3 uses a four-segment coding method to express individual information in the population. The first segment is the process sorting coding sequence OS, which is used to determine the order of workpiece processing; the second segment is the machine selection coding sequence MS, which indicates the processing machine assigned to the process; the third segment is the worker selection coding sequence WS, which indicates the processing worker selected by the process; the fourth segment is the process rework sequence PR, which indicates whether the process needs secondary rework.

4. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: In S4, the MS sequence and WS sequence adopt the improved GLR initialization method based on global selection GS, local selection LS and random selection RS, with a ratio of 6:3:

1.

5. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: In S5, real-time monitoring plug-in active decoding considering rework is adopted to distinguish the various idle times brought by dual flexible resources, implement different left-shift insertion strategies, and recalculate the processing time of the process in the current scheduling plan after the left-shift insertion of the process, and perform a left shift immediately ahead, thereby generating an active scheduling plan, reasonably arranging the rework process for it, and calculating the corresponding multiple objective functions.

6. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: In the process of using discrete Jaya operator to generate branch population R2(t) from population R1(t), X best is a randomly selected individual from the frontier solution set, X worst is an individual randomly selected from the individuals with the worst Pareto rank in the current population; The update process of OS sequence is as follows: delete the parent individual P i With X worst The same elements at the same position generate the intermediate individual temp, and delete X best The same elements as temp, and finally X best The remaining elements in are assigned to temp in sequence to generate the descendant individual C i ; The update process of MS and WS sequences is as follows: delete the parent individual P i With X worst The same element at the same position generates the intermediate individual temp, and X best The element at the corresponding position is assigned to temp to generate the offspring individual C i ; Update process for extreme cases: parent individual P i and the worst individual X worst The element values ​​of all gene positions may be completely identical or completely different. When they are completely identical, the parent individual P is reset using the initialization rule in S4 to generate the child individual C. i ; When completely different, P i With X worst and X best Execute IPOX once each and select the best offspring individual as C i ; When they are not exactly the same, the Jaya update operator is executed.

7. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: The genetic operators include three operations: selection, random parent crossover and multi-level mutation. Among them, ternary tournament selection is used as the selection operator. Regarding the random parent crossover operator, IPOX is used for the OS sequence, and RPX is used for the MS sequence and WS sequence. Regarding the multi-level mutation operator, the interchange mutation method is used for the OS sequence, and the random replacement mutation is used for the MS sequence and WS sequence.

8. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1 is characterized in that: When the insertion optimization based on idle time windows is adopted for all individuals in the population R1(t), the separated idle time windows are integrated by right shifting the non-critical processes in the scheduling scheme, and then all processes are traversed to search for the processes that can be inserted and shifted left to perform left shift insertion. After the traversal is completed, all processes are implemented with tight front scheduling.

9. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 1, characterized in that: When the learning hybrid redistribution operation is used for individuals in R5(t), the following four redistribution operators are included: L1: Randomly select a process on the machine with the largest maximum completion time, traverse the optional machines of the process in ascending order of completion time, replace the element value of the corresponding position of the process in the MS sequence, and select the optional worker with the shortest processing time for the process, replace the element value of the corresponding position of the WS sequence, and update the PR sequence to obtain a new individual; L2: Randomly select a process from the workers with the largest cumulative processing time, traverse the optional workers of the process in ascending order of cumulative processing time, replace the element value of the corresponding position of the process in the WS sequence, and select the optional machine with the shortest processing time, update the element value of the corresponding position of the MS sequence, and update the PR sequence to obtain a new individual; L3: Extract the machine with the lowest energy consumption, randomly select a process that can be processed by the machine with the lowest energy consumption on the machines other than this machine, replace the optional machine of this process with the machine with the lowest energy consumption, replace the element value of the corresponding position of this process in the MS sequence, and select the optional worker with the shortest processing time for this process, replace the element value of the corresponding position of the WS sequence, and update the PR sequence to obtain a new individual; L4: Extract the worker with the minimum total worker cost, randomly select a process that can be processed by the worker with the minimum cost from the workers other than this worker, replace the optional worker of this process with the worker with the minimum total worker cost, replace the element value of the corresponding position of this process in the WS sequence, select the optional machine with the shortest processing time for this process, replace the element value of the corresponding position of the MS sequence, and update the PR sequence to obtain a new individual.

10. The multi-objective shop scheduling method considering workpiece quality and variable processing time according to claim 9, characterized in that: In S12, the population R1(t) is quickly non-dominated sorted, and the individuals with dominance level 1 after sorting R1(t) are subjected to knowledge-driven variable neighborhood search. The maximum number of iterations of local search is set, and the variable neighborhood search is implemented using the threshold method. First, multiple neighborhood structures are defined, each corresponding to a method of changing individual information. During the search process, the current individual is substituted into different neighborhood structures to try to generate a new individual, which is then evaluated by non-adaptive sorting after being substituted into the decoding operator. If the new individual is better than the current individual, the current individual is accepted and updated. Otherwise, another neighborhood structure is selected for trial. After all neighborhood structures are tried once, the number of iterations is increased by one. When the maximum number of iterations is met, the search ends and the best individual found is output. The neighborhood structures include the following three types: N1: Shift the non-critical processes in the scheduling plan rightward to integrate idle time; after disrupting the process sequence on the critical path, traverse all the optional workers of the critical processes to check whether the process can be inserted by the worker to the left. If so, stop traversal after the insertion and generate a new individual; Otherwise, the worker with the smallest worker cost is selected for replacement to generate a new individual; N2: Traverse the key blocks in the critical path. If the key block contains more than two processes, randomly extract the middle process of the key block to replace the first process and the last process of the block to generate a new individual. N3: Randomly select one of the machine and worker constraints, randomly extract a key process on the machine or worker with the largest maximum completion time, analyze the optional machines or workers of the selected process, replace the processing machine or worker of the process with the optional machine or worker with the smallest maximum completion time, and generate a new individual.

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