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A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems

机译：基于混合Pareto的局部搜索的多目标柔性作业车间调度问题

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This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimisation objectives are considered simultaneously, i.e. the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine. In this study, several well-designed neighbouring approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep the population with a certain level of quality and diversity. Moreover, a variable neighbourhood search (VNS) based self-adaptive strategy is embedded in the hybrid algorithm to utilise the neighbouring approaches efficiently. Then, an external Pareto archive is developed to record the non-dominated solutions found so far. In addition, a speed-up method is devised to update the Pareto archive set. Experimental results on several well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms, in term of both search quality and computational efficiency.
机译：本文提出了一种基于帕累托混合的本地搜索（PLS）算法，用于解决多目标柔性作业车间调度问题。同时考虑了三个最小化目标，即最大完成时间（makespan），所有机器的总工作量以及关键机器的工作量。在这项研究中，提出了几种设计良好的相邻方法，这些方法考虑了问题的特征，因此可以保持快速收敛的能力，同时又可以使人口保持一定的质量和多样性。此外，在混合算法中嵌入了基于可变邻域搜索（VNS）的自适应策略，以有效利用相邻方法。然后，将开发一个外部Pareto存档，以记录迄今为止发现的非主要解决方案。另外，设计了一种加速方法来更新Pareto存档集。在几个著名基准测试上的实验结果表明了该混合算法的有效性。结论是，就搜索质量和计算效率而言，PLS算法优于最新算法。

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