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A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities

机译：具有维护活动的多目标柔性作业车间调度问题的离散人工蜂群算法

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摘要

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.
机译：本文提出了一种新颖的离散人工蜂群算法（DABC），用于解决带有维护活动的多目标柔性作业车间调度问题。考虑的性能标准是最大完成时间（所谓的制造时间），计算机的总工作量以及关键计算机的工作量。与原始ABC算法不同，提出的DABC算法提供了唯一的解决方案表示，其中食物来源由两个离散矢量表示，并且禁忌搜索（TS）应用于每个食物来源，以为所用蜜蜂，旁观蜜蜂，和侦察蜂。引入了一种有效的初始化方案来构建具有一定质量和多样性水平的初始种群。采用自适应策略使DABC算法具有学习能力，可以在不同的有希望的区域中生成相邻的解决方案，而设计了一个外部Pareto存档集来记录到目前为止发现的非主导解决方案。此外，还提出了一种新颖的解码方法来解决生成的计划中的维护活动。提出的DABC算法在现有文献中的一组众所周知的基准实例上进行了测试。通过对实验结果的详细分析，与文献中的最佳性能算法相比，所提出的DABC算法具有很高的效率和效率。

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