Optimization method for solving distributed heterogeneous flow shop group scheduling

Abstract

The invention relates to the technical field of intelligent manufacturing workshops, in particular to an optimization method for solving group scheduling of a distributed heterogeneous flow shop, which comprises the following steps of: determining that minimization of product assembly completion time is taken as a problem solving target, and initializing parameters; initializing a solution; generating a probability random number, and judging and selecting to execute product and group operation or product and workpiece operation; and updating the optimal solution, judging whether termination time is reached or not, if yes, ending iteration, and outputting the current optimal solution. According to the application of the method, the problem of group scheduling of the distributed heterogeneous flow shop with the product assembly stage can be effectively solved, and the production efficiency of a manufacturing enterprise is improved by optimizing scheduling.

Description

Technical Field

[0001] The invention relates to the technical field of distributed heterogeneous flow shop group scheduling in intelligent manufacturing workshops, and in particular to an optimization method for solving distributed heterogeneous flow shop group scheduling. Background Art

[0002] With the intensification of global economic integration and competition among enterprises, in order to fully utilize resources in different regions, multi-factory cooperation or distributed scheduling schemes have gradually become active in most industrial operations. Compared with traditional flow shop scheduling, this mechanism can make full use of factory resources in different regions, shorten production cycles, reduce logistics costs, and use intelligent technology to quickly realize the rational allocation of resources, optimize the efficiency of the entire supply chain system, and reduce the degree of regional isomorphism. Therefore, the distributed flow shop scheduling problem (DFSP) in a distributed manufacturing environment has been widely studied. With the continuous optimization of production lines, in some fields of production activities, work should be processed in different groups, such as the semiconductor industry, the automotive industry, the printed circuit board (PCB) or the electronics manufacturing industry. Grouping technology realizes the grouping of workpieces with similar processing attributes into the same group for processing, which not only improves the flexibility of the production system, but also reduces production costs. The distributed flow shop scheduling problem with grouping constraints is called the distributed flow shop group scheduling problem (DFGSP). At present, the research on DFGSP is mainly based on the assumption that all factories are the same. However, in the actual production process, distributed production factories are not always the same. Due to supply chain and procurement strategies, machine brands, wear and tear, etc., there are differences in the processing capabilities of machines in different factories, that is, the factories are heterogeneous. In addition, many manufacturing companies, such as automobiles, furniture, clothing, etc., have processing and assembly production, that is, after the parts are processed in distributed factories, they must be assembled into products. In this model, the types of parts are diverse, the collaborative relationship between processes is extremely complex, and the production difficulty is relatively large. Therefore, considering the actual situation of multi-factory cooperation, it is of great significance for enterprises and researchers to study an effective method to solve the distributed heterogeneous flow shop group scheduling problem with assembly stage. Summary of the invention

[0003] The purpose of the present invention is to provide an optimization method for solving distributed heterogeneous flow shop group scheduling, which solves the problem that the product assembly stage is not considered in the current distributed flow shop group scheduling problem.

[0004] The present invention provides an optimization method for solving distributed heterogeneous flow shop group scheduling, which is characterized by comprising the following steps: Step 1: Analyze the characteristics of the distributed heterogeneous flow shop group scheduling problem, determine the problem-solving goal of minimizing the assembly completion time, and initialize the parameters, including the probability selection parameters. , Temperature coefficient , product damage size , where the above assembly completion time is the time when all products are assembled; Step 2, initialize the solution, design a heuristic method to generate an initial solution, and use it as the optimal solution; Step 3: Generate a random number with probability ; Step 4, , perform product and group operations, including destructive reconstruction of products and groups, local search of groups, local search of products, and acceptance of new solutions using simulated annealing acceptance criteria; , perform product and artifact operations, including artifact destruction and reconstruction, artifact local search, and product local search; Step 5: Update the best solution and determine whether the termination time has been reached. If so, end the iteration and output the current best solution. Otherwise, return to step 3.

[0005] Furthermore, in step 2, a complete solution is defined as ,in , represents the processing sequence of the group in each factory, Represents a factory index, is the number of factories, Indicates factory The processing sequence of the middle group, , Representative Factory A group in Representative Factory The number of processing groups, Representative Factory Processing groups; , represents the processing sequence of workpieces in each group, Represents a group, represents the number of groups, represents the processing sequence of the workpieces contained in group k, Representative Group The number of workpieces in Representative Group The Workpieces; , representing the assembly sequence of the product.

[0006] Furthermore, in step 2, the heuristic method steps designed are as follows, (1) For each factory, an initial group sequence containing all groups is generated and the processing order of the workpieces in the group is determined. The workpieces in the group are sorted using the maximum processing time method. The maximum processing time method is described as calculating the processing time of each workpiece in the current factory and then sorting them in descending order. The group sequence is generated using the key group method. The key group method is described as finding the group with the longest processing time as the key group. The groups with processing time less than the key group are inserted before the key group in ascending order of processing time, and the remaining groups are inserted after the key group in descending order of processing time. (2) Generate an initial product assembly sequence by sorting the products in descending order of assembly time; (3) Set the processing sequence of all groups in the factory to empty; (4) Insert the first group of all initial group sequences into all processing positions of all factories, retain the insertion that produces the minimum completion time, and delete this inserted group from all initial group sequences; repeat the above process until the initial group sequence is empty. The above completion time refers to the maximum completion time of all factories; (5) Take out the products in the initial product assembly sequence one by one and insert them into the position that minimizes the assembly completion time.

[0007] Furthermore, in step 4, the destruction and reconstruction rules of products and groups are: randomly selected products and randomly select this half of the workpieces in a product; determine the groups to which these workpieces belong and extract them from the factory processing sequence of the current solution; and insert the extracted groups in sequence into the position that minimizes the assembly completion time.

[0008] Furthermore, in step 4, in the local search phase of the group, a variable neighborhood descent strategy including two group neighborhood search operators is designed to optimize the solution, wherein the group neighborhood search operator includes, Insertion of the last workpiece group, determine the last workpiece to be processed in each product during the assembly phase; extract the groups of these workpieces from the factory processing sequence; insert the extracted groups in turn to the position that minimizes the assembly completion time; Insertion of key factory groups, extract each group in the key factory in turn and insert it to the position that minimizes the assembly completion time.

[0009] Furthermore, in step 4, during the local search phase of the product, an insertion neighborhood search operator is used to optimize the solution, extracting each product in the assembly sequence in turn and inserting it to a position that minimizes the assembly completion time.

[0010] Furthermore, in step 4, the simulated annealing acceptance criterion is described as: the new solution is better than the current solution or the random number Less than When the new solution is accepted, the temperature involved in the simulated annealing acceptance criterion is It is expressed as, Among them, the temperature coefficient is the algorithm control parameter, is the number of factories, is the number of workpieces, is the number of machines, Representative workpiece In the factory Machine The processing time on represents the target value of the current solution, Represents the target value of the new solution.

[0011] Furthermore, in step 4, the destruction and reconstruction rule of the workpiece is to extract half of the workpieces in all groups in the key factory and insert them into the position in the group that minimizes the assembly completion time.

[0012] Furthermore, in step 4, during the local search phase of the workpiece, a variable neighborhood descent strategy including two workpiece neighborhood search operators is designed to optimize the solution, wherein the workpiece neighborhood search operator includes: Insertion of the last workpiece: determine the last workpiece to be processed in each product during the assembly phase; extract these workpieces from the factory processing sequence; insert the extracted workpieces into the group in sequence to the position that minimizes the assembly completion time; Referring to the insertion of the optimal solution sequence, the workpiece sequence in the current optimal solution is used as the reference sequence. Each workpiece of each group in the key factory is extracted in sequence according to the order of the reference sequence and inserted into the position in this group that minimizes the assembly completion time.

[0013] Further, in step 4, the variable neighborhood descent strategy is to use the first neighborhood structure as the current neighborhood structure. If a new solution better than the original solution is obtained by searching with the current neighborhood structure, the original solution is updated; if no solution better than the original solution is obtained, the next neighborhood structure is used as the current neighborhood structure, otherwise the current neighborhood structure is continued to be used, and the above process is repeated. When all neighborhood structures cannot update the current solution, the local search process ends; the execution order of the two group neighborhood search operators included in the variable neighborhood descent strategy for the group is the insertion of the group where the last workpiece is located and the insertion of the key factory group; the execution order of the two workpiece neighborhood search operators included in the variable neighborhood descent strategy for the workpiece is the insertion of the last workpiece and the insertion of the reference optimal solution sequence; Among them, in the variable neighborhood descent strategy of the group, each time a group neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution; in the variable neighborhood descent strategy of the workpiece, each time a workpiece neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution.

[0014] Further, in step 5, the end time is set to , that is, the longest operation time is milliseconds, where Represents the number of groups in the current example. Represents the number of machines in the current instance.

[0015] The optimization method for solving the distributed heterogeneous assembly workshop group scheduling provided by the present invention is more in line with the actual production scenarios of some enterprises in reality. According to the characteristics and scale of the problem, the present invention proposes an improved iterative greedy algorithm to obtain a high-quality solution. Compared with some existing algorithms, the present invention solves the cooperation mechanism of the two sub-problems in the distributed heterogeneous assembly workshop group scheduling problem, and adopts a probabilistic selection method to select the next step of product and group operation or product and workpiece operation. In addition, the present invention utilizes product assembly characteristics and adopts an effective group and workpiece processing method to further improve the quality of the solution. In summary, the present invention has the positive effect of reducing the completion time of product assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart for realizing the present invention; Figure 2 It is the assembly completion time convergence diagram of the present invention; Figure 3 This is an average scatter plot comparing the present invention with the existing algorithms. DETAILED DESCRIPTION

[0017] like Figure 1 As shown, the optimization method for solving distributed heterogeneous assembly line group scheduling provided by the present invention is mainly implemented through the following steps.

[0018] Step 1: Analyze the characteristics of the distributed heterogeneous flow shop group scheduling problem, determine the problem-solving goal of minimizing the assembly completion time, and initialize the parameters, including the probability selection parameters. , Temperature coefficient , product damage size , where the above assembly completion time is the time when all products are assembled.

[0019] Step 2, initializing the solution, involves a heuristic method to generate an initial solution, which is taken as the optimal solution.

[0020] Specifically, a complete solution is defined as ,in , represents the processing sequence of the group in each factory, Represents a factory index, is the number of factories, Indicates factory The processing sequence of the middle group, , Representative Factory A group in Representative Factory The number of processing groups, Representative Factory Processing groups; , represents the processing sequence of workpieces in each group, Represents a group, represents the number of groups, represents the processing sequence of the workpieces contained in group k, Representative Group The number of workpieces in Representative Group The Workpieces; , representing the assembly sequence of the product.

[0021] The steps of the designed heuristic method are as follows: (1) For each factory, an initial group sequence containing all groups is generated and the processing order of the workpieces in the group is determined. The workpieces in the group are sorted using the maximum processing time method. The maximum processing time method is described as calculating the processing time of each workpiece in the current factory and then sorting them in descending order. The group sequence is generated using the key group method. The key group method is described as finding the group with the longest processing time as the key group. The groups with processing time less than the key group are inserted before the key group in ascending order of processing time, and the remaining groups are inserted after the key group in descending order of processing time. (2) Generate an initial product assembly sequence by sorting the products in descending order of assembly time; (3) Set the processing sequence of all groups in the factory to empty; (4) Insert the first group of all initial group sequences into all processing positions of all factories, retain the insertion that produces the minimum completion time, and delete this inserted group from all initial group sequences; repeat the above process until the initial group sequence is empty. The above completion time refers to the maximum completion time of all factories; (5) Take out the products in the initial product assembly sequence one by one and insert them into the position that minimizes the assembly completion time.

[0022] Step 3: Generate a random number with probability .

[0023] Step 4, , perform product and group operations, including destructive reconstruction of products and groups, local search of groups, local search of products, and acceptance of new solutions using simulated annealing acceptance criteria; , perform product and artifact operations, including artifact destruction and reconstruction, artifact local search, and product local search.

[0024] Specifically, the destruction and reconstruction rules for products and groups are: randomly selected products and randomly select this d half of the workpieces in a product; determine the groups to which these workpieces belong and extract them from the factory processing sequence of the current solution; and insert the extracted groups in sequence into the position that minimizes the assembly completion time.

[0025] In the local search phase of the group, a variable neighborhood descent strategy containing two group neighborhood search operators is designed to optimize the solution, where the group neighborhood search operators include: Insertion of the last workpiece group, determine the last workpiece to be processed in each product during the assembly phase; extract the groups of these workpieces from the factory processing sequence; insert the extracted groups in turn to the position that minimizes the assembly completion time; Insertion of key factory groups, extract each group in the key factory in turn and insert it to the position that minimizes the assembly completion time.

[0026] In the local search phase of products, an insertion neighborhood search operator is used to optimize the solution, extracting each product in the assembly sequence in turn and inserting it to the position that minimizes the assembly completion time.

[0027] The simulated annealing acceptance criterion is described as: the new solution is better than the current solution or the random number Less than When the new solution is accepted, the temperature involved in the simulated annealing acceptance criterion is It is expressed as, Among them, the temperature coefficient is the algorithm control parameter, is the number of factories, is the number of workpieces, is the number of machines, Representative workpiece In the factory Machine The processing time on represents the target value of the current solution, Represents the target value of the new solution.

[0028] The rule for destroying and reconstructing artifacts is to extract half of the artifacts from all groups in the critical factory and insert them into the position in the group that minimizes the assembly completion time.

[0029] In the local search phase of the workpiece, a variable neighborhood descent strategy containing two workpiece neighborhood search operators is designed to optimize the solution. The workpiece neighborhood search operators include: Insertion of the last workpiece: determine the last workpiece to be processed in each product during the assembly phase; extract these workpieces from the factory processing sequence; insert the extracted workpieces into the group in sequence to the position that minimizes the assembly completion time; Referring to the insertion of the optimal solution sequence, the workpiece sequence in the current optimal solution is used as the reference sequence. Each workpiece of each group in the key factory is extracted in sequence according to the order of the reference sequence and inserted into the position in this group that minimizes the assembly completion time.

[0030] The variable neighborhood descent strategy is to use the first neighborhood structure as the current neighborhood structure. If a new solution better than the original solution is obtained by searching with the current neighborhood structure, the original solution is updated; if no solution better than the original solution is obtained, the next neighborhood structure is used as the current neighborhood structure, otherwise the current neighborhood structure is continued to be used and the above process is repeated. When all neighborhood structures cannot update the current solution, the local search process ends; the execution order of the two group neighborhood search operators included in the variable neighborhood descent strategy for groups is the insertion of the group where the last workpiece is located and the insertion of the key factory group; the execution order of the two workpiece neighborhood search operators included in the variable neighborhood descent strategy for workpieces is the insertion of the last workpiece and the insertion of the reference optimal solution sequence; Among them, in the variable neighborhood descent strategy of the group, each time a group neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution; in the variable neighborhood descent strategy of the workpiece, each time a workpiece neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution.

[0031] Step 5: Update the best solution and determine whether the termination time has been reached. If so, end the iteration and output the current best solution. Otherwise, return to step 3.

[0032] Specifically, the end time is fixed to , that is, the maximum evolution time limit is milliseconds, where Represents the number of groups included in the current case. Indicates the number of machines included in the current calculation.

[0033] In order to better demonstrate the effectiveness of the present invention, the present invention will be further described below through experimental analysis of a series of examples of the present invention.

[0034] The test data consists of 540 instances based on factory data. , number of groups , number of machines , number of products Created by, , , , , the workpiece processing time is evenly distributed in Within the range, the assembly completion time of the product is evenly distributed in within the range.

[0035] The CPU time is used as the end time of the comparison algorithm, and the end time is set to milliseconds, where Represents the number of groups in the current example. Represents the number of machines in the current instance.

[0036] In terms of algorithm parameters, in order to better solve and optimize the distributed heterogeneous flow shop scheduling problem, the probability selection parameter is set to =0.7, the temperature coefficient is set to =1.4, the product failure size is set to =0.4.

[0037] like Figure 2 and Figure 3 As shown, the uIG algorithm of the present invention is experimentally compared with three improved iterative greedy algorithms: two-stage iterative greedy algorithm TIG, effective two-stage iterative greedy algorithm tIG, and iterative greedy algorithm IGP with workpiece acceleration. In order to adapt these algorithms to the problems to be solved, necessary modifications need to be made, including using a unified instance and following the details of each original algorithm during the adaptation process. Each example is repeated 10 times to generate statistical results. The present invention uses relative percentage increase (RPI) as a performance evaluation indicator. The calculation formula of RPI is RPI=(CC best ) / C best *100%, where C represents the product assembly completion time obtained when a specific algorithm solves the example, and C best It represents the optimal product assembly completion time obtained by all algorithms in all runs. Obviously, the smaller the RPI value, the better the algorithm performance.

[0038] In summary, the application of the present invention effectively reduces the time required to complete product assembly and successfully solves the group scheduling problem of distributed heterogeneous assembly lines, thereby greatly improving the production efficiency of the factory and providing strong support for optimal scheduling in actual production scenarios.

Claims

1. An optimization method for solving distributed heterogeneous flow shop group scheduling, characterized in that: The following steps are included: Step 1: Analyze the characteristics of the distributed heterogeneous flow shop group scheduling problem, determine the problem-solving goal of minimizing the assembly completion time, and initialize the parameters, including the probability selection parameters. , temperature coefficient , product damage size , where the above assembly completion time is the time when all products are assembled; Step 2, initialize the solution, design a heuristic method to generate an initial solution, and use it as the optimal solution; Step 3: Generate a random number with probability ; Step 4, , perform product and group operations, including destructive reconstruction of products and groups, local search of groups, local search of products, and acceptance of new solutions using simulated annealing acceptance criteria; , perform product and artifact operations, including artifact destruction and reconstruction, artifact local search, and product local search; Step 5: Update the best solution and determine whether the termination time has been reached. If so, end the iteration and output the current best solution. Otherwise, return to step 3.

2. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 1 is further characterized in that: In step 2, a complete solution is defined as ,in , represents the processing sequence of the group in each factory, Represents a factory index, is the number of factories, Indicates factory The processing sequence of the middle group, , Representative Factory A group in Representative Factory The number of processing groups, Representative Factory Processing groups; , represents the processing sequence of workpieces in each group, Represents a group, represents the number of groups, Representative Group The processing sequence of the workpieces included in Representative Group The number of workpieces in Representative Group The Workpieces; , representing the assembly sequence of the product.

3. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 2 is further characterized in that: In step 2, the designed heuristic steps are as follows, (1) For each factory, an initial group sequence containing all groups is generated and the processing order of the workpieces in the group is determined. The workpieces in the group are sorted using the maximum processing time method. The maximum processing time method is described as calculating the processing time of each workpiece in the current factory and then sorting them in descending order. The group sequence is generated using the key group method. The key group method is described as finding the group with the longest processing time as the key group. The groups with processing time less than the key group are inserted before the key group in ascending order of processing time, and the remaining groups are inserted after the key group in descending order of processing time. (2) Generate an initial product assembly sequence by sorting the products in descending order of assembly time; (3) Set the processing sequence of all groups in the factory to empty; (4) Insert the first group of all initial group sequences into all processing positions of all factories, retain the insertion that produces the minimum completion time, and delete this inserted group from all initial group sequences; repeat the above process until the initial group sequence is empty, and the above completion time refers to the maximum completion time of all factories; (5) Take out the products in the initial product assembly sequence one by one and insert them into the position that minimizes the assembly completion time.

4. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 3 is further characterized in that: In step 4, the destruction and reconstruction rules of products and groups are: randomly selected products and randomly select this Half of the workpieces in a product; Determine the groups to which these workpieces belong and extract them from the factory processing sequence of the current solution; insert the extracted groups in sequence to the position that minimizes the assembly completion time.

5. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 4 is further characterized in that: In step 4, during the group local search phase, a variable neighborhood descent strategy containing two group neighborhood search operators is designed to optimize the solution, where the group neighborhood search operators include: Insertion of the last workpiece group, determine the last workpiece to be processed in each product during the assembly phase; extract the groups of these workpieces from the factory processing sequence; insert the extracted groups in turn to the position that minimizes the assembly completion time; Insertion of key factory groups, extract each group in the key factory in turn and insert it to the position that minimizes the assembly completion time.

6. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 5 is further characterized in that: In step 4, during the local search phase of the product, an insertion neighborhood search operator is used to optimize the solution, extracting each product in the assembly sequence in turn and inserting it to the position that minimizes the assembly completion time.

7. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 6 is further characterized in that: In step 4, the simulated annealing acceptance criterion is described as: the new solution is better than the current solution or the random number Less than When the new solution is accepted, the temperature involved in the simulated annealing acceptance criterion is It is expressed as, Among them, the temperature coefficient is the algorithm control parameter, is the number of factories, is the number of workpieces, is the number of machines, Representative workpiece In the factory Machine The processing time on represents the target value of the current solution, Represents the target value of the new solution.

8. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 7 is further characterized in that: In step 4, the destruction and reconstruction rule of the artifacts is to extract half of the artifacts in all groups in the critical factory and insert them into the position in the group that minimizes the assembly completion time.

9. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 8 is further characterized in that: In step 4, during the local search phase of the workpiece, a variable neighborhood descent strategy including two workpiece neighborhood search operators is designed to optimize the solution, wherein the workpiece neighborhood search operator includes: Insertion of the last workpiece: determine the last workpiece to be processed in each product during the assembly phase; extract these workpieces from the factory processing sequence; insert the extracted workpieces into the group in sequence to the position that minimizes the assembly completion time; Referring to the insertion of the optimal solution sequence, the workpiece sequence in the current optimal solution is used as the reference sequence. Each workpiece of each group in the key factory is extracted in sequence according to the order of the reference sequence and inserted into the position in this group that minimizes the assembly completion time.

10. The optimization method for solving distributed heterogeneous flow shop group scheduling according to claim 9, further characterized by: In step 4, the variable neighborhood descent strategy is to use the first neighborhood structure as the current neighborhood structure. If a new solution better than the original solution is obtained by searching with the current neighborhood structure, the original solution is updated; if no solution better than the original solution is obtained, the next neighborhood structure is used as the current neighborhood structure, otherwise the current neighborhood structure is continued to be used, and the above process is repeated. When all neighborhood structures cannot update the current solution, the local search process ends; the execution order of the two group neighborhood search operators included in the variable neighborhood descent strategy for groups is the insertion of the group where the last workpiece is located and the insertion of the key factory group; the execution order of the two workpiece neighborhood search operators included in the variable neighborhood descent strategy for workpieces is the insertion of the last workpiece and the insertion of the reference optimal solution sequence; Among them, in the variable neighborhood descent strategy of the group, each time a group neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution; in the variable neighborhood descent strategy of the workpiece, each time a workpiece neighborhood search operator is used, it is necessary to determine whether the current solution is updated. If updated, a product insertion neighborhood search operator is executed once to optimize the solution.

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