An improved quantum genetic algorithm for stochastic job shop problem

  • Authors:

  • Jinwei Gu;Cuiwen Cao;Bin Jiao;Xingsheng Gu

  • Affiliations:

  • East China University of Science and Technology, ShangHai, China;East China University of Science and Technology, ShangHai, China;Shanghai Dianji University, ShangHai, China;East China University of Science and Technology, ShangHai, China

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

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Abstract

This paper considers the stochastic job shop scheduling problem with the objective of minimizing the expected value of makespan and the processing times of jobs being subject to independent normal distributions. In order to solve this problem, we devise an Improved Quantum Genetic Algorithm (IQGA) and develop a stochastic expected value model. Different from traditional genetic algorithms, IQGA employs the idea of quantum theory, devises a converting mechanism of quantum representation aiming at job shop code, and proposes a new rotation angle table as the update mechanism of populatio. In addition, three crossover operators and three mutation operators are compared in order to obtain the best combination to improve algorithm performance. Compared with standard Genetic Algorithm (GA), experimental results achieved by IQGA demonstrate its feasibility and effectiveness while dealing with the stochastic job shop problem.