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首页> 外文会议>Second ICSC Symposium on Engineering of Intelligent Systems, Jun 27-30, 2000, Scotland, U.K. >ENHANCED EVOLUTIONARY APPROACHES TO THE FLOW SHOP SCHEDULING PROBLEM
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ENHANCED EVOLUTIONARY APPROACHES TO THE FLOW SHOP SCHEDULING PROBLEM

机译：流程商店调度问题的改进进化方法

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摘要

In many production systems a number of operations must be done on every job and often these operations have to be done in the same order on all jobs. This scheduling approach is known as the Flow Shop Scheduling Problem (FSSP). Frequently, the main objective to be minimized here is the completion time of the last job to abandon the system. This problem has been proved as NP-hard for even a very small number of resources. Evolutionary Computation, as an emergent research field, is continuously showing its own evolution and enhanced approaches include latest multi-recombinative methods involving multiple crossovers per couple (MCPC) and multiple crossovers on multiple parents (MCMP). Because of the flow shop is in essence a permutation schedule problem, a permutation of jobs can be used as the representation scheme of chromosomes. This implies the use of well-suited genetic operators (crossover and mutation) in order to provide always valid offspring. The present paper discusses the new multi-recombinative methods and shows the performance of enhanced evolutionary approaches under permutation representation. In the case of MCMP, a new controlled uniform scanning crossover (CUSX) method was required, to manage permutations ensuring the birth of feasible offspring. Results of the methods proposed here are contrasted with those obtained under previous evolutionary approaches to the FSSP.
机译：在许多生产系统中，必须对每个作业执行许多操作，并且通常必须对所有作业以相同的顺序执行这些操作。这种调度方法称为流水车间调度问题（FSSP）。通常，这里要最小化的主要目标是放弃系统的最后一项工作的完成时间。即使对于非常少的资源，也已经证明此问题是NP困难的。进化计算作为一个新兴的研究领域，正在不断展示其自身的发展，增强的方法包括最新的多重组方法，其中涉及每对夫妇多个交叉（MCPC）和多个父母上的多个交叉（MCMP）。由于流水车间本质上是置换计划问题，因此可以将作业的置换用作染色体的表示方案。这意味着要使用合适的遗传算子（交叉和突变）来提供始终有效的后代。本文讨论了新的多重组方法，并展示了置换表示下增强进化方法的性能。对于MCMP，需要一种新的受控统一扫描交叉（CUSX）方法来管理排列，以确保可行后代的诞生。这里提出的方法的结果与以前的FSSP进化方法获得的结果形成对比。

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