Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates
Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates
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DOI：
10.1016/j.apm.2013.10.061
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发表时间：
2014-05
影响因子：
5
通讯作者：
M. Ebrahimi;S. F. Ghomi;B. Karimi
中科院分区：
文献类型：
--
作者：
M. Ebrahimi;S. F. Ghomi;B. Karimi
本文研究混合流水车间（HFS）环境下的调度问题。序列相关族设置时间 (SDFST) 涉及最小化完工时间和总延迟。现实世界中的生产环境包括无数事件的不确定性和随机性情况，合适的调度模型应该考虑它们。因此，在本文中，假设预产期是不确定的，其数据服从正态分布。由于所提出的问题是NP-hard问题，因此在遗传算法的基础上提出了两种元启发式算法，即：非支配排序遗传算法（NSGAII）和多目标遗传算法（MOGA）。文献综述中使用的多阶段遗传算法（MPGA）在不同维度上对这两种算法的定量和定性结果进行了比较。实验结果表明，与 MOGA 和 MPGA 相比，NSGAII 在相当短的时间内表现得非常好。
This paper studies the scheduling problem in hybrid flow shop (HFS) environment. The sequence dependent family setup time (SDFST) is concerned with minimization of makespan and total tardiness. Production environments in real world include innumerable cases of uncertainty and stochasticity of events and a suitable scheduling model should consider them. Hence, in this paper, due date is assumed to be uncertain and its data follow a normal distribution. Since the proposed problem isNP-hard, two metaheuristic algorithms are presented based on genetic algorithm, namely: Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Genetic Algorithm (MOGA). The quantitative and qualitative results of these two algorithms have been compared in different dimensions with multi phase genetic algorithm (MPGA) used in literature review. Experimental results indicate that the NSGAII performs very well when compared against MOGA and MPGA in a considerably shorter time.