Solving Flexible Job-shop Problem with Sequence-dependent Setup Times by Using Selection Hyper-heuristics

M. Luo, Jian Lin, Li Xu
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引用次数: 2

Abstract

Flexible job shop problem (FJSP), which is inspired by the actual production process, is the most popular research direction in the field of intelligence production. In recent years, a more complex model with sequence-independent setup times (SDST) is proposed to make the solution of the problem in the specific production situation. In this paper, we investigate an FJSB model with SDST which is generated based on replacement of molds and the objective of this model is to minimize the makespan. We also design a selection hyper-heuristic algorithm for the problem. In order to evaluate the algorithm, we solve two problems, classical FJSP and the FJSP with SDST, and compare the results with other methods in other existing literature respectively. The experimental results prove that the approach proposed in this paper overmatch other algorithms in optimization ability and stability.
用选择超启发式方法求解具有序列依赖设置时间的柔性作业车间问题
柔性作业车间问题(FJSP)是智能生产领域中最热门的研究方向,它受到实际生产过程的启发。近年来,人们提出了一种更复杂的序列无关设置时间(SDST)模型来解决具体生产情况下的问题。在本文中,我们研究了一个基于模具更换生成的具有SDST的FJSB模型,该模型的目标是最小化完工时间。我们还针对该问题设计了一种选择超启发式算法。为了对算法进行评价,我们分别求解了经典FJSP和带SDST的FJSP两个问题,并将结果与其他文献中的其他方法进行了比较。实验结果表明,本文提出的方法在优化能力和稳定性方面优于其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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