Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Xue Han;Yuyan Han;Qingda Chen;Junqing Li;Hongyan Sang;Yiping Liu;Quanke Pan;Yusuke Nojima
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引用次数: 49

Abstract

To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.
基于改进迭代贪心算法的序列依赖的分布式流水车间调度
为满足企业多协同生产需求,分布式排列流水车间调度问题已成为制造系统领域的前沿研究问题。在序列相关的设置时间约束下,我们通过最小化最大跨度准则来研究DPFSP。为了解决dpfp问题,一些元启发式算法的重大发展是必要的。在此背景下,提出了一种简单有效的改进迭代贪婪(NIG)算法来最小化dpfsp的最大完工时间。根据dpfsp的特点,设计了一种基于关键工厂内单任务交换和任务块交换的两阶段局部搜索算法。我们将所提出的算法与最先进的算法进行了比较,包括迭代贪婪算法(2019)、Ruiz和Pan提出的迭代贪婪算法(2019)、离散微分进化算法(2018)、离散人工蜂群(2018)和人工化学反应优化(2017)。仿真结果表明,NIG算法优于比较算法。
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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CiteScore
7.80
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