A bi-population cooperative scatter search algorithm for distributed hybrid flow shop scheduling with machine breakdown

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-10-09 DOI:10.1016/j.cie.2024.110624
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Abstract

The occurrence of machine breakdowns is a frequent and dynamic phenomenon in the production process. The implementation of effective preventive measures can mitigate such events and result in reduced production costs. This paper investigates the distributed hybrid flow shop scheduling problem with machine breakdown (DHFSSPB) considering short maintenance time. The bi-population cooperative scatter search (BCSS) algorithm is proposed to address the DHFSSPB, wherein the search for the optimal scheduling sequence is transformed into genetic evolution aiming to obtain a gene chain with both minimum lower bound and minimum cost attributes. Firstly, the DHFSSPB problem is modeled through a combination of predictive maintenance strategy and right-shift rescheduling rule. Subsequently, a diversification approach is developed to facilitate attribute inheritance, enhance the efficiency of job allocation, and establish a reference set. The reference set is partitioned into two subpopulations based on lower bound attributes and cost attributes, respectively. The corresponding hybrid search strategies are designed to enhance the efficiency of job sorting and machine selection for subpopulations with distinct attributes. The cooperative evolution between subpopulations occurs through the competitive interaction and fusion of individuals. An enhanced reinforcement learning approach is proposed to expedite the acceleration of individual attribute evolution by leveraging evolutionary knowledge acquired from populations, thereby effectively guiding the evolutionary trajectories of individuals. Additionally, a method for evaluating the population during the learning process is developed based on problem characteristics to enhance learning efficiency. Experimental results demonstrate that BCSS outperforms the comparative algorithm in solving the DHFSSPB.
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带机器故障的分布式混合流程车间调度的双群合作散点搜索算法
机器故障是生产过程中经常发生的动态现象。实施有效的预防措施可以减少此类事件的发生,从而降低生产成本。本文研究了考虑短维护时间的机器故障分布式混合流水车间调度问题(DHFSSPB)。针对 DHFSSPB 问题,本文提出了双群体合作分散搜索(BCSS)算法,将最优调度序列的搜索转化为遗传进化,旨在获得同时具有最小下限和最小成本属性的基因链。首先,DHFSSPB 问题通过预测性维护策略和右班重排规则的组合来建模。随后,开发了一种多样化方法,以促进属性继承、提高工作分配效率并建立参考集。参考集根据下限属性和成本属性分别划分为两个子群。设计了相应的混合搜索策略,以提高具有不同属性的子群的作业排序和机器选择效率。子群之间的合作进化是通过个体之间的竞争互动和融合实现的。我们提出了一种增强型强化学习方法,通过利用从群体中获取的进化知识来加速个体属性的进化,从而有效地指导个体的进化轨迹。此外,还根据问题特征开发了一种在学习过程中评估种群的方法,以提高学习效率。实验结果表明,在求解 DHFSSPB 时,BCSS 优于比较算法。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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