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Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem

A cultural gene algorithm and flexible operation technology, applied in computing, computing models, manufacturing computing systems, etc., can solve the problems of slow computing speed, long time consumption, and many iterations of genetic algorithms, achieve strong global search capabilities, and speed up the solution. Effects of speed, high convergence performance

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

However, the genetic algorithm also has problems such as slow calculation speed and premature convergence.
Local search algorithm is an effective method to solve optimization problems, but it faces the problem of too many iterations and too long time consumption

Method used

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  • Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem
  • Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem
  • Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem

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Embodiment Construction

[0028] The cultural gene algorithm has good practicability for solving the multi-objective flexible job shop scheduling problem. reach a local optimum. The evolutionary search performs the global breadth search of the population, and the local search performs the local deep search of the individual.

[0029] Below in conjunction with accompanying drawing and embodiment, the present invention is further described:

[0030] 1. Multi-objective flexible job shop scheduling problem, combined with figure 2

[0031] The description of FJSP is as follows: a processing system has m different machines M={M j |j=1, 2,..., m}, to process n workpieces J={J i |i=1,2,...,n} let K i Indicates the total number of processes of the workpiece, O ik Indicates the workpiece J i The k-th process k=1,2,...,K i , the total number of operations of all workpieces is P ikj Indicates the process O ik on machine M j The processing time on the above, the processing sequence of the workpiece is...

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Abstract

The invention relates to the technical field of job shop scheduling, in particular to an improved culture gene algorithm for solving a multi-objective flexible job shop scheduling problem. The algorithm comprises the following steps of performing process-based encoding; generating an initialized population; performing local search by a hill-climbing method; calculating fitness; judging whether an optimization criterion is met or not (if yes, generating an optimal individual and ending the algorithm, otherwise, executing the next step); performing selection; performing SPX crossover; performing mutation; performing local search by the hill-climbing method; generating a new-generation population; calculating fitness; and circulating the process. The algorithm is improved as follows: the local search is performed by utilizing the hill-climbing method, so that local optimum can be escaped for obtaining a better solution, and the calculation time can be shortened; and in addition, the crossover and mutation modes of the algorithm are improved, the SPX crossover method is adopted, and one of two methods of insertion mutation and replacement mutation is randomly selected for mutating individuals in the population by an equal probability Pm during mutation.

Description

[0001] Technical field [0002] The invention relates to the technical field of job shop scheduling, in particular to solving the problem of multi-objective flexible job shop scheduling with an algorithm. Background technique [0003] In the manufacturing industry, there are many kinds of production scheduling problems and various methods. Among them, the job shop scheduling problem (JSP) is the most basic and important machine scheduling problem, and it is also the most difficult NP-hard problem. The Flexible Job Shop Scheduling Problem (FJSP) is an extension of JSP that allows each process to be processed on a given number of machines instead of one. At present, the research on FJSP has made some progress, and various heuristic algorithms, evolutionary algorithms, etc. have been applied to this field. There is no lack of genetic algorithm (GA), particle swarm optimization (PSO), local search algorithm, simulated annealing algorithm, ant colony algorithm, etc. Although thes...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/04G06N3/00
CPCY02P90/30G06Q50/04G06N3/006
Inventor 汤琴胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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