Discrete differential evolution algorithm for the job shop scheduling problem

  • Authors:

  • Fang Liu;Yutao Qi;Zhuchang Xia;Hongxia Hao

  • Affiliations:

  • Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, China

  • Venue:
  • Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
  • Year:
  • 2009

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Abstract

Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid convergence rate. However, it does not perform well on dealing with job shop scheduling problems that have discrete decision variables. To remedy this, a Discrete Differential Evolution (DDE) Algorithm with special crossover and mutation operators is proposed to solve this problem. Under the skeleton of DE algorithm, The DDE algorithm inherits the advantage of rapid convergence rate. The experimental results on the well-known benchmark instances show the proposed algorithm is efficient in solving Job Shop Scheduling Problem