A Study of Large-scale Job Shop Scheduling Problem Based on TOC
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摘要: 针对大规模Job Shop调度问题,提出了一种基于TOC(theory of constraints)的免疫遗传算法。该算法依据TOC理论中瓶颈机约束生产系统性能的思想,利用瓶颈机器的特性,在染色体编码及遗传操作过程中,对瓶颈机与非瓶颈机采用不同的处理方式,以使瓶颈工序得到最优化调度。而非瓶颈工序在满足瓶颈工序的调度方案的基础上进行快速调度,降低大规模作业车间调度问题的复杂度,提高算法的求解效率。为提高算法求解质量,克服遗传算法的随机性及迭代退化问题,将TOC理论中的瓶颈机器拓展至瓶颈工件,提出候选瓶颈工件集及瓶颈工件的定义。通过对瓶颈机接种“瓶颈工件邻域对换”免疫算子,充分利用种群中个体的特征信息,辅助遗传算法的优化过程。仿真结果表明:瓶颈特征的应用以及免疫算子的融入是有效的,免疫遗传算法可以在较短的时间内求得令人满意的解。
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关键词:
- 大规模 /
- Job Shop调度问题 /
- 瓶颈机器 /
- 瓶颈工件
Abstract: An immune genetic algorithm for a large-scale job shop scheduling problem is proposed based on the Theory of Constraints (TOC). According to the theory, bottleneck mechanisms constrain the performance of a manufacturing system, so we use an immune genetic algorithm to encode the bottleneck mechanisms and non-bottleneck mechanisms respectively. The bottleneck mechanisms are scheduled deeply, and the non-bottleneck mechanisms are scheduled quickly to enhance the computational efficiency of the algorithm. To do so, we define a set of candidate bottleneck jobs and add them to the bottleneck mechanisms according to the TOC. The characteristic information on chromosomes is used to assist the optimization of the immune genetic algorithm with the operation of interchanging the sequences of the job shop scheduling problem on the bottleneck mechanisms. The simulation results show that the applications of bottleneck mechanisms and immune operators are effective, and the immune genetic algorithm can obtain satisfactory solutions within acceptable time.-
Key words:
- bottleneck job /
- bottleneck mechanism /
- chromosomes /
- computational efficiency
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