考虑运输时间的混合柔性流水车间分解启发式算法

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Michele Garraffa , Helmut Simonis , Barry O’Sullivan , Eddie Armstrong
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引用次数: 0

摘要

本文提出了具有运输时间的混合柔性流车间(HFFTT)的有效启发式方法,它是混合柔性流车间(HFP)和混合柔性流车间(HFF)问题的扩展。介绍了两类启发式算法:基于约束规划的启发式算法和分解启发式算法。虽然基于cp的启发式方法可以应用于HFFTT的任何实例,但分解启发式方法是专门为“矩形”实例设计的,其中每个阶段的机器数量相同。这两种方法都与两种迭代贪婪算法进行了比较,其中一种算法是专门为矩形实例量身定制的。结果表明,对于非矩形实例,基于cp的启发式算法获得了最佳性能,而对于矩形实例,只要考虑的实例足够大,分解启发式算法就会强烈地支配所有其他方法。结果表明,所得结果大部分可以推广到没有运输时间的情况下,其中HFFTT问题简化为HFF问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decomposition heuristics for the Hybrid Flexible Flowshop with transportation times
This paper proposes efficient heuristic approaches for the Hybrid Flexible Flowshop with Transportation Times (HFFTT), an extension of both the Hybrid Flowshop (HFP) and Hybrid Flexible Flowshop (HFF) problems. Two classes of heuristics are introduced: Constraint Programming (CP)-based heuristics and decomposition heuristics. While the CP-based heuristics can be applied to any instance of the HFFTT, the decomposition heuristics are specifically designed for “rectangular” instances, where the number of machines is the same at each stage. Both approaches are compared against two iterated greedy algorithms adapted from the state-of-the-art, one of which is tailored exclusively for rectangular instances. The results show that the CP-based heuristics achieve the best performance for non-rectangular instances, while the decomposition heuristics strongly dominate all other approaches for rectangular instances, as soon as the size of the instances considered is large enough. We show that most of the results obtained can be generalized to the case without transportation times, where the HFFTT problem reduces to the HFF.
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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