Computer Science ›› 2018, Vol. 45 ›› Issue (4): 233-239.doi: 10.11896/j.issn.1002-137X.2018.04.039

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Study on Flexible Job-shop Scheduling Problem Based on Improved Discrete Particle Swarm Optimization Algorithm

DING Shu-yang, LI Bing and SHI Hong-bo   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Flexible job-shop scheduling problem is an extension of the classical job-shop scheduling problem.The former is much closer to the practical production. Aiming at minimizing the maximum completion time,this paper proposed an improved discrete particle swarm optimization algorithm.The traditional particle swarm optimization algorithm is applicable to optimize the continuous models.As a combinatorial optimization problem with high complexity,FJSP is a typically discrete model.The proposed algorithm utilizes the load balancing mechanism for the machines to initiate the population,and introduces three operators into the procedure of updating the individuals’ status in the population.The three operators are respectively described as follows:the mutation based on the operation sequencing or the machine assignment,the precedence preserving order based crossover between current particle and the individual optimal,and rand-point preservation crossover between current particle and the global optimal.A particle is completely updated by using three operators sucessively.This method makes the population converge to the optimal solution very fast.The experimental results of benchmark instances show that the proposed algorithm can practically solve the flexible job-shop scheduling problem and search the near optimal solutions very fast.The proposed algorithm outperforms the other similar algorithms with respect to searching efficiency and convergence speed.

Key words: Job-shop scheduling,Discrete optimization problem,Flexibility,Particle swarm optimization

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