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首页> 外文期刊>Applied Soft Computing >Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods
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Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

机译:流水店调度随着邻近启发式方法的修改和声搜索算法阻塞

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The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem. (C) 2019 Elsevier B.V. All rights reserved.
机译:具有阻止的流程图调度被认为是具有许多真实应用的重要调度问题。本文提出了一种新的算法,它在和声搜索算法(HSA)中应用启发式技术,以最小化总流量时间。该方法称为具有相邻启发式方法的修改和谐搜索算法(MHSNH)。为了改善初始和谐记忆,我们应用了两个启发式技术:最近的邻居(NN)和建设性改进的NEH(MNEH)。和声搜索算法的修改版本演变为探索并生成新的解决方案。然后通过使用邻近的启发式来增强新生成的解决方案。最后,应用另一个邻近的启发式来改善所获得的解决方案。使用12个样本的12个现实世界问题实例评估所提出的算法。使用两个因素完成实验评估:CPU计算时间和迭代次数。对于第一个因素,针对六种既定方法的比较评估表明,该方法几乎实现了六个问题实例的最佳总体成果,其中六个问题的情况下,为他人产生了富有成效的结果。对于第二个因素,针对十二个良好的方法的比较评估表明,该方法在三个问题实例中实现了最佳总体结果,并在其他情况下获得了非常好的结果。简而言之,拟议的MHSNH是解决作业商店调度问题的有效策略。 (c)2019年Elsevier B.V.保留所有权利。

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