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Improved Genetic Algorithm for Flexible Job Shop Scheduling with Moving Time

A technology of improved genetic algorithm and moving time, which is applied in the field of flexible job shop scheduling with moving time by improved genetic algorithm, and can solve problems such as the influence of moving time

Active Publication Date: 2021-11-12
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an improved genetic algorithm to solve the flexible job shop scheduling method with moving time, which effectively solves the problem of the impact of objectively existing moving time in the actual production process

Method used

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  • Improved Genetic Algorithm for Flexible Job Shop Scheduling with Moving Time
  • Improved Genetic Algorithm for Flexible Job Shop Scheduling with Moving Time
  • Improved Genetic Algorithm for Flexible Job Shop Scheduling with Moving Time

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Embodiment 1

[0059] The present invention solves the flexible job shop scheduling method with moving time for improving genetic algorithm, is characterized in that, comprises the following steps:

[0060] Step 1, set parameters; determine the population size P, the number of iterations G, and the crossover probability P c , variation probability P m Wait;

[0061] Step 2, population initialization, using random selection method to randomly generate population individuals;

[0062] Step 3: Calculate and evaluate the fitness value of each chromosome in the population, i.e. the target value, and compare its size. If the output condition is met or the approximate optimal solution is completed, the operation is completed; otherwise, step 4 is performed;

[0063] Step 4, use the tournament method to select and select the next generation population;

[0064] Step 5, perform crossover according to the crossover strategy for the chromosomal individuals satisfying the crossover probability in the...

Embodiment 2

[0084] Embodiment two, on the basis of embodiment one, in order to provide a better embodiment, the main parameter of genetic algorithm is set as follows: population size P=40, crossover probability P c =0.8, mutation probability P m = 0.6, the maximum genetic algebra G is 200 generations.

Embodiment 3

[0085] Embodiment three, on the basis of embodiment one,

[0086] The initialization method of FJSP:

[0087] When the genetic algorithm is used to solve the target problem, the quality of the initial solution directly affects the solution quality of the algorithm and the convergence speed of the solution. Because FJSP not only needs to solve the problem of machine selection, but also needs to solve the problem of sequence of processing procedures. Aiming at the characteristics of FJSP, the machine selection part in the chromosome of the present invention adopts an integer random initialization method, that is, the number on each gene position on the chromosome indicates that the processing procedure is randomly generated in the sequence number of the optional machine set. The specific execution steps are as follows:

[0088] 1) In the optional workpiece set, select the first workpiece, and select the first process of the current workpiece;

[0089] 2) Randomly select a mac...

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Abstract

The invention relates to an improved genetic algorithm to solve the flexible job shop scheduling method with moving time, which effectively solves the problem that the existing algorithm ignores the moving time, which may affect the processing quality of products in some specific production fields; the solution technology The scheme includes step 1, setting parameters; step 2, population initialization, using the random selection method to randomly generate population individuals; The optimal solution is completed, otherwise, go to step 4; step 4, use the tournament selection method to select the next generation population; step 5, select the chromosome individuals that meet the crossover probability in the population; step 6, compile the chromosome individuals that meet the mutation probability, Obtain a new population; Step 7, return to Step 3; the present invention realizes the fast optimization of the dispatching scheme.

Description

technical field [0001] The invention relates to the technical field of flexible job shop scheduling, in particular to an improved genetic algorithm to solve the flexible job shop scheduling method with moving time. Background technique [0002] The flexible job shop scheduling problem (Flexible Job Shop Scheduling Problem, FJSP) is a new problem extended on the basis of the traditional job shop scheduling problem. At present, there are many studies on swarm intelligence optimization algorithms for flexible job shop scheduling problems, such as genetic algorithm (Genetic Algorithm, GA), particle swarm optimization (Particle Swarm Optimization, PSO), ant colony algorithm (Ant Colony Optimization, ACO), gray wolf optimization algorithm (Grey Wolf Optimization, GWO), teaching and learning optimization algorithm (Teaching Learning Based Optimization, TLBO). [0003] However, only a few scholars have explored the research on the moving time between processes. In the actual produ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06N3/12G06N3/00G06Q50/04
CPCG06N3/008G06N3/126G06Q10/06316G06Q50/04Y02P90/30
Inventor 张国辉张海军闫琼杨洋洋朱宝英
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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