Job-shop scheduling method based on multi-population evolution mechanism

A job shop and scheduling method technology, applied in the computer field, can solve the problems of local optima, poor local search ability, shorten production time, etc., and achieve the effect of overcoming premature convergence, avoiding excessive competition, and alleviating the loss of diversity

Inactive Publication Date: 2010-01-20
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the disadvantages that the job shop scheduling method based on multi-population genetic algorithm is easy to fall into local optimum

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  • Job-shop scheduling method based on multi-population evolution mechanism
  • Job-shop scheduling method based on multi-population evolution mechanism
  • Job-shop scheduling method based on multi-population evolution mechanism

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Embodiment Construction

[0024] refer to figure 1 , the implementation steps of the present invention are as follows:

[0025] Step 1, set the parameters and initialize the population.

[0026] Set the upper limit of chromosome evaluation times L, the number of subpopulations S, the size of subpopulations P, and the crossover probability P c , mutation probability P m , communication probability P t , set the initial temperature T of the simulated annealing SA algorithm 0 , annealing coefficient r and stop temperature T t , counter i=0, randomly generate S initial chromosome subpopulations according to the encoding method;

[0027] The encoding method adopts the encoding based on the job number, and the decoding adopts the pre-inserted decoding; each chromosome has a corresponding scheduling time after decoding, and the scheduling time is the time for completing all job processing. Let the completion time of job k be C k , then the scheduling time corresponding to a chromosome is Time=max(C k ...

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Abstract

The invention discloses a job-shop scheduling method based on multi-population evolution mechanism, which pertains to the computer field and mainly solves the problem that the present job-shop scheduling method of multi-population genetic algorithm is easy to be trapped into local optimization and poor local search ability. The method comprises the steps of (1) setting parameters and initializing the population; (2) evaluating the chromosomes and the initial memory bank of the population, optimizing the initial memory bank by means of the simulated annealing algorithm; (3) judging whether the termination condition is satisfied, if so, outputting the currently obtained optimal scheduling plan; otherwise, continuing step 4; (4) carrying out the crossover and mutation operation on the chromosomes in each sub-population; (5) communicating the sub-population with the memory bank; and (6) updating the memory bank and optimizing the memory bank by means of the simulated annealing algorithm, and then returning to step (3). The job-shop scheduling method of the invention can obtain the job-shop scheduling plan with high quality, shorten the production time and can be used for selecting the job-shop scheduling plan.

Description

technical field [0001] The invention belongs to the computer field and relates to job shop scheduling, in particular to a method for intelligent job shop scheduling using computer software, which is used for scheduling management of factory production processes. Background technique [0002] Job shop scheduling problem is the core of advanced manufacturing system operation research and automation technology, which has important theoretical significance and practical value. The application of effective scheduling methods can greatly save resources, improve efficiency, and create considerable economic benefits. On the other hand, the job shop scheduling problem is a typical NP-hard problem, which has attracted extensive attention of researchers and is one of the research hotspots in the field of engineering scheduling. [0003] The research on job shop scheduling problems initially focused on methods such as integer programming, mixed integer programming, dynamic programming ...

Claims

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Application Information

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IPC IPC(8): G06Q10/00G06N3/12G06Q10/06
Inventor 刘芳戚玉涛焦李成夏柱昌郝红侠公茂果尚荣华马文萍
Owner XIDIAN UNIV
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