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Method for solving flexible job-shop scheduling problem with improved GA based on polychromatic set hierarchical structure

A technology of workshop scheduling and hierarchical structure, applied in the direction of gene model, data processing application, prediction, etc., can solve the problems of many invalid data, large gap, long chromosomes, etc., to improve the solution speed, reduce the scale and data volume, and improve the search The effect of efficiency

Active Publication Date: 2014-04-16
安徽瑞林精科股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the algorithm described in this document can only generate chromosomes in simple cases (choose one of the equipment corresponding to the process), which is far from the actual application; and the segmented coding used by the chromosome is generated based on the maximum number of processes as the benchmark unit, which is As a result, when the algorithm deals with large-scale multi-process scheduling problems in practice, the chromosomes are too long and there are too many invalid data, etc., which need further improvement

Method used

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  • Method for solving flexible job-shop scheduling problem with improved GA based on polychromatic set hierarchical structure
  • Method for solving flexible job-shop scheduling problem with improved GA based on polychromatic set hierarchical structure
  • Method for solving flexible job-shop scheduling problem with improved GA based on polychromatic set hierarchical structure

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Experimental program
Comparison scheme
Effect test

Embodiment

[0062] 1) Mathematical model of flexible shop scheduling problem

[0063] FJSP can be described as, assuming that M is the number of processing equipment, N is the number of workpieces to be processed, P is the number of processes, and I is the set of all equipment; I eg Represents the set of available equipment for the gth process of the workpiece e, J e is the process number of workpiece e; X is the processing sequence of all workpieces, S egk Indicates the start time of the gth process of workpiece e processed on equipment k; E egk is the processing end time of the gth process of the workpiece e on the equipment k; T egk is the continuous processing time of the gth process of workpiece e on equipment k, and k∈I eg then there is E egk =S egk +T egk ;E p Indicates the completion time of the last process; MS indicates the final completion time of all workpieces;

[0064] When the j-th process of workpiece i and the g-th process of workpiece e are executed on the same...

example 1

[0163] Example 1 simulation:

[0164] For the calculation example in Table 1, the parameters of the genetic algorithm are set as follows: the population size is 50, the crossover rate is 0.6, the mutation rate is 0.8, and the maximum number of evolutionary bands is 100. The GA evolution curve is obtained by simulation in the MATILAB7.0 environment: image 3 shown by image 3 It can be seen that the improved GA algorithm can quickly converge from 147 to 134 in the 70th generation, and the corresponding scheduling result Gantt diagram is as follows Figure 4 shown.

example 2

[0165] Example 2 simulation and comparison:

[0166] In order to further verify the correctness of the algorithm, choose Example 2 for simulation, set 2 process benchmarks, 4 equipment numbers, and 8 specific equipment, and the optimal solution is 121 minutes. Depend on Figure 5 The genetic evolution curve shows that the improved genetic algorithm can quickly converge from 130 to 121 in the 32nd generation, and its solution speed is obviously faster. The corresponding Gantt diagram of the scheduling result is as follows Figure 6 shown.

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Abstract

The invention discloses a method for solving the flexible job-shop scheduling problem with an improved GA based on a polychromatic set hierarchical structure. According to the method, an original process-machine tool contour matrix is split into matrixes of relation of process-benchmark, benchmark-equipment model, equipment model-asset number by establishing equipment benchmarks and setting process constraint, equipment constraint, machine tool constraint and unique constraint, and the data size of a constraint model is effectively reduced; furthermore, by optimizing chromosome lengths reasonably and setting blending operation of batch benchmarks, the time and space complexity of chromosomes is effectively reduced, and then the solving speed and practicality of the algorithm can be greatly improved.

Description

technical field [0001] The invention belongs to the technical field of flexible workshop scheduling, and relates to an improved genetic algorithm, in particular to a method for solving flexible workshop scheduling based on an improved GA of a multi-color set hierarchical structure. Background technique [0002] The core idea of ​​Flexible Job-Shop Scheduling Problem (FJSP) is that multiple batches of multiple types of parts can be processed and produced on the same type of equipment. Before formal processing, the process of each component is uniquely determined, but the process route is uncertain. Each process has a variety of processing equipment options, that is, each component to be processed has multiple process routes to choose from. And the selection of equipment should be based on the balance of equipment capabilities. Compared with the traditional workshop scheduling problem, the FJSP problem is a more complicated NP-hard problem. Solving this kind of problem requir...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06N3/12
Inventor 栾飞曹巨江傅卫平宝昱彤
Owner 安徽瑞林精科股份有限公司
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