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An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines

机译:一种智能混合元启发式算法,用于解决不相关并行机的无等待两阶段柔性流水车间调度问题

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This paper addresses the problem of no-wait two-stage flexible flow shop scheduling problem (NWTSFFSSP) considering unrelated parallel machines, sequence-dependent setup times, probable reworks and different ready times to actualize the problem. The performance measure used in this study is minimizing maximum completion time (makespan). Because of the complexity of addressed problem, we propose a novel intelligent hybrid algorithm [called hybrid algorithm (HA)] based on imperialist competitive algorithm (ICA) which are combined with simulated annealing (SA), variable neighborhood search (VNS) and genetic algorithm (GA) for solving the mentioned problem. The hybridization is carried out to overcome some existing drawbacks of each of these three algorithms and also for increasing the capability of ICA. To achieve reliable results, Taguchi approach is used to define robust parameters' values for our proposed algorithm. A simulation model is developed to study the performance of our proposed algorithm against ICA, SA, VNS, GA and ant colony optimization (ACO). The results of the study reveal the relative superiority of HA studied. In addition, potential areas for further researches are highlighted.
机译:本文解决了无等待的两阶段灵活流水车间调度问题(NWTSFFSSP),该问题考虑了无关的并行机器,与序列有关的设置时间,可能的返工和不同的准备时间以实现该问题。本研究中使用的性能衡量标准是最大最长完成时间(makespan)的最小化。由于所解决问题的复杂性,我们提出了一种基于帝国主义竞争算法(ICA)的新型智能混合算法[称为混合算法(HA)],该算法结合了模拟退火(SA),可变邻域搜索(VNS)和遗传算法(GA)解决上述问题。进行杂交是为了克服这三种算法中每一种的一些现有缺点,并且还用于提高ICA的能力。为了获得可靠的结果,Taguchi方法用于为我们提出的算法定义健壮参数的值。开发了一个仿真模型来研究我们提出的算法对ICA,SA,VNS,GA和蚁群优化(ACO)的性能。研究结果揭示了所研究的HA的相对优势。此外,突出了有待进一步研究的潜在领域。

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