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一种求解作业车间调度问题的协同进化算法 被引量:3

A new coevolutionary genetic algorithm for job-shop scheduling problems
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摘要 提出一种用协同进化遗传算法求解作业车间调度问题的新方法.车间调度问题用传统的启发式算法很难求得最优解.协同进化遗传算法模拟生物界物种之间的竞争、捕食、共生及其相互作用下,各物种协同进化,使整个生态系统由低级向高级进化的过程.协同进化算法与传统的遗传算法相比,不仅加快了算法的收敛速度,且可提高算法的搜索能力,避免算法陷入局部最优.特殊的交叉操作更使所求得的解都为合法解.实例证明协同进化遗传算法是行之有效的算法. A novel genetic algorithm for job-shop problems with coevolutionary model has been presented.Job-shop scheduling is a NP-hard problem. It is demonstrated that coevolutionary genetic algorithm(COGA) simulating the competition of ecosystem is capable of speeding up the computation and finding the best solution easier.An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solution.Simulation results demonstrate the effectiveness of this proposed algorithm. The optimization performance is improved significantly in comparison with the standard genetic algorithm.
出处 《深圳大学学报(理工版)》 EI CAS 2004年第3期272-275,共4页 Journal of Shenzhen University(Science and Engineering)
关键词 协同进化 遗传算法 作业车间调度问题 coevolution genetic algorithm job-shop scheduling
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参考文献8

  • 1Garey E L,Johnson D S,Sethi R.The complexity of flowshop and job-shop scheduling[J].Mathematics of Operations Research,1976,21(1):117-129.
  • 2Holland J H.Adaptation in Nature and Artificial System[M]. Boston:MIT Press,1992.
  • 3Cheng R,Gen M,Tsujimura Y.A tutorial survey of job-shop scheduling problems using genetic algorithms [J].Computers & Industrial Engineering,1996,30(4):983-997.
  • 4Jason M,Franz O.Maintaining genetic diversity in genetic algorithms through co-evolution[A].Canadian Conference on AI[C].Toronto Ontario:1998,128-138.
  • 5GareyEL JohnsonDS SethiR.流动车间与作业车间调度的复杂性[J].数学运算研究,1976,21(1):117-129.
  • 6HollandJH.自然与人工系统的自适应性[M].波士顿:麻省理工学院出版社,1992..
  • 7ChengR GenM TsujimuraY.遗传算法求解作业车间调度问题的研究[J].计算机与工业工程:英文版,1996,30(4):983-997.
  • 8JasonM FranzO.应用协同进化保持遗传算法的遗传多样性[A]..加拿大人工智能会议[C].安大略多伦多,1998.128-138.

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同被引文献22

  • 1孙德轩,赵息,杨黎明.基于提高收敛性能的微粒群优化研究[J].中国制造业信息化(学术版),2007,36(5):78-82. 被引量:2
  • 2张运凯,王方伟,张玉清,马建峰.协同进化遗传算法及其应用[J].计算机工程,2004,30(15):38-40. 被引量:10
  • 3宋华明,马士华.混合装配流水线上最小makespan的协同优化[J].系统工程理论与实践,2007,27(2):153-160. 被引量:11
  • 4金华征.考虑市场环境的多目标输电网规划优化目标[D].上海:上海交通大学,2007:84-94.
  • 5Thomopoulos N T. Line balancing-sequencing for mixed- model assembly[J]. Management Science, 1967,14(2) :59- 75.
  • 6Yano C A, Rakamadugu R. Sequencing to minimize work overload in assembly lines with production options[J]. Management Science, 1991, 37 (5) : 572 - 586.
  • 7Zhao X B, Katsuhisa O. Sequencing problem for a mixed - model assembly line in a JIT production system [ J ]. Computers & Industrial Engineering, 1994,27(1 - 4) : 71 - 74.
  • 8Feo T A, Resende M G C. A probabilistic heuristic for a computationally difficult set covering problem[ J ]. Operations Research Letters, 1989,8(4) :67 - 71.
  • 9Thomopoulos N T. Line balancing - sequencing for mixedmodel assembly line balancing prublem[J]. Computers and Industrial Engineering, 1967,14(2) :859 - 875.
  • 10Kim Y K,Hyun C J, Kim Y. Sequencing in mixed model assembly lines: a genetic algorithm approach[J ]. Computers & Operation Research, 1996,23 ( 12 ) : 1131 - 1145.

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