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A hybrid crow search algorithm to minimise the weighted sum of makespan and total flow time in a flow shop environment

机译:一种混合乌鸦搜索算法,可最大程度地减少流水车间环境中的工期和总流时间的加权总和

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摘要

In this paper, flow shop scheduling problems which were proved to be strongly NP-hard (non-deterministic polynomial time hard) are considered. The objective is to minimise the weighted sum of makespan and total flow time. For solving this problem, a recently developed meta-heuristics algorithm called as crow search algorithm is proposed. Moreover, the dispatching rules are hybridised with the crow search algorithm to improve the solution quality. An evaluation of the performance of the proposed algorithm is carried out by industrial scheduling problem and the results are compared with many dispatching rules and constructive heuristics. The results obtained by the proposed algorithm are much better than the dispatching rules and constructive heuristics. Random problem instances are also used to validate the performance of the proposed algorithm. The results are compared with many other meta-heuristics addressed in the literature and the results indicate the effectiveness of the proposed algorithm in terms of solution quality and computational time. To the best of our knowledge this is the first reported application of crow search algorithm to solve the scheduling problems.
机译:在本文中,考虑了流水车间调度问题,这些问题被证明具有很强的NP困难性(非确定性多项式时间困难)。目的是使制造期和总流动时间的加权总和最小化。为了解决这个问题,提出了最近开发的称为启发式搜索算法的元启发式算法。此外,将调度规则与乌鸦搜索算法混合以提高解决方案的质量。通过工业调度问题对所提算法的性能进行了评估,并将结果与​​许多调度规则和建设性启发式方法进行了比较。所提出的算法获得的结果比调度规则和构造启发式方法要好得多。随机问题实例也用于验证所提出算法的性能。将结果与文献中提到的许多其他元启发式方法进行比较,结果表明了所提算法在解决方案质量和计算时间方面的有效性。据我们所知,这是乌鸦搜索算法首次用于解决调度问题。

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