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A meta-heuristic approach for solving the no-wait flow-shop problem

机译：一种解决无等待流水车间问题的元启发式方法

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摘要

No-wait flow-shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a flow-shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. The problem is strongly NP-hard. In this paper, the considered performance measure is the makespan. In order to explore the feasible region of the problem, a hybrid algorithm of Tabu Search and Particle Swarm Optimisation (PSO) is proposed. In the proposed approach, PSO algorithm is used in order to move from one solution to a neighbourhood solution. We first employ a new coding and decoding technique to efficiently map the discrete feasible space to the set of integer numbers. The proposed PSO will further use this coding technique to explore the solution space and move from one solution to a neighbourhood solution. Afterwards, the algorithm decodes the solutions to its respective feasible solution in the discrete feasible space and returns the new solutions to the TS. The algorithm is tested by solving a large number of problems available in the literature. Computational results show that the proposed algorithm is able to outperform competitive methods and improves some of the best-known solutions of the considered test problems.
机译：无等待流水车间调度问题是指这样的问题集：在流水车间上下文中，许多作业可用于在多台机器上进行处理，并且增加了以下约束条件：流水车间的连续操作之间应该没有等待时间工作。问题是NP很难解决的。在本文中，考虑的性能指标是制造期。为了探索问题的可行区域，提出了禁忌搜索和粒子群优化算法的混合算法。在所提出的方法中，使用PSO算法以便从一种解决方案移动到邻域解决方案。我们首先采用一种新的编码和解码技术，以将离散的可行空间有效地映射到整数集。拟议的PSO将进一步使用此编码技术来探索解决方案空间，并从一种解决方案移至邻域解决方案。然后，该算法在离散可行空间中解码其相应可行解的解，并将新解返回给TS。通过解决文献中存在的大量问题来测试该算法。计算结果表明，所提出的算法能够胜过竞争方法，并改进了所考虑的测试问题的一些最著名的解决方案。

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