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Heuristics and metaheuristics for the mixed no-idle flowshop with sequence-dependent setup times and total tardiness minimisation
Swarm and Evolutionary Computation ( IF 8.2 ) Pub Date : 2020-04-03 , DOI: 10.1016/j.swevo.2020.100689
Fernando Luis Rossi , Marcelo Seido Nagano

This paper analyses the mixed no-idle flowshop scheduling problem by adding the sequence-dependent setup times constraint and focusing on minimising the total tardiness criterion. In the mixed no-idle flowshop environment, machines that allow idleness, and also those that do not, coexist. In this paper, an extension of this problem in which dependent setup times are included in machines that permit idleness is studied. In order to do so, a mixed integer linear programming (MILP) model is developed. Furthermore, a method to assess the total tardiness of a permutation sequence is provided. Moreover, a partial acceleration method is devised so as to compute the total tardiness in an insertion neighbourhood. Since this problem has not yet been studied in the literature, this research adapted the best known heuristics and metaheuristics available from related problems. Moreover, a new heuristic was developed in order to provide an efficient solving mechanism for the problem. The proposed heuristic was incorporated in the best metaheuristics found in the literature. The adapted methods, as well as our proposal, were tested and compared through statistical and computational experimentation in an extensive benchmark. The MILP was also evaluated and the optimal solutions were compared to those found by the new proposed heuristic. The results show that the proposed heuristic perform extremely well in terms of solution quality and computational efficiency, providing near optimal solutions in some cases.



中文翻译:

混合无怠速流水车间的启发式和元启发式,其序列依赖的建立时间和总拖延时间最小化

本文通过添加与序列有关的建立时间约束并着重于使总拖延准则最小化,来分析混合的无空闲Flowshop调度问题。在混合的无空闲流水作业环境中,允许闲置的机器与不允许闲置的机器共存。在本文中,对这一问题的扩展进行了研究,其中在允许闲置的机器中包括相关的设置时间。为此,开发了混合整数线性规划(MILP)模型。此外,提供了一种评估置换序列的总延迟的方法。此外,设计了一种局部加速方法,以便计算插入邻域中的总延迟。由于该问题尚未在文献中进行研究,这项研究采用了从相关问题中可获得的最著名的启发式和元启发式。此外,开发了一种新的启发式方法,以便为问题提供有效的解决机制。提议的启发式方法已并入文献中发现的最佳元启发式方法。通过统计和计算实验,在广泛的基准测试中对经过改编的方法以及我们的建议进行了测试和比较。还评估了MILP,并将最佳解决方案与新提议的启发式算法找到的解决方案进行了比较。结果表明,所提出的启发式方法在解决方案质量和计算效率方面表现非常出色,在某些情况下提供了接近最优的解决方案。为了提供一种有效的问题解决机制,开发了一种新的启发式方法。提议的启发式方法已并入文献中发现的最佳元启发式方法。通过统计和计算实验,在广泛的基准测试中对经过改编的方法以及我们的建议进行了测试和比较。还评估了MILP,并将最佳解决方案与新提议的启发式算法找到的解决方案进行了比较。结果表明,所提出的启发式方法在解决方案质量和计算效率方面表现非常出色,在某些情况下提供了接近最优的解决方案。为了提供一种有效的问题解决机制,开发了一种新的启发式方法。提议的启发式方法已并入文献中发现的最佳元启发式方法。通过统计和计算实验,在广泛的基准测试中对经过改编的方法以及我们的建议进行了测试和比较。还评估了MILP,并将最佳解决方案与新提议的启发式算法找到的解决方案进行了比较。结果表明,所提出的启发式方法在解决方案质量和计算效率方面表现非常出色,在某些情况下提供了接近最优的解决方案。在广泛的基准测试中通过统计和计算实验进行了测试和比较。还对MILP进行了评估,并将最佳解决方案与新提议的启发式算法找到的解决方案进行了比较。结果表明,所提出的启发式方法在解决方案质量和计算效率方面表现非常出色,在某些情况下提供了接近最优的解决方案。在广泛的基准测试中通过统计和计算实验进行了测试和比较。还评估了MILP,并将最佳解决方案与新提议的启发式算法找到的解决方案进行了比较。结果表明,所提出的启发式方法在解决方案质量和计算效率方面表现非常出色,在某些情况下提供了接近最优的解决方案。

更新日期:2020-04-03
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