Multi-objective flexible job shop scheduling based on feature information optimization algorithm

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zeyin Guo, Lixin Wei, Jinlu Zhang, Ziyu Hu, Hao Sun, Xin Li
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引用次数: 0

Abstract

Multi-objective optimization methods are increasingly used in job shop scheduling optimization strategies. However, in the design process of multi-objective optimization strategies, a neighborhood search is performed on all solutions in the optimization algorithm, resulting in a time-consuming search. In the algorithm selection process, feature information carried by individuals is often ignored, leading to a lack of targeted guidance ability in the algorithm. To address the limitations of the existing methods, a multi-objective flexible job shop scheduling method based on a feature information optimization algorithm (FIOA) was proposed. First, a framework of multiple group optimization algorithms was applied to construct diverse groups. Subsequently, a representative individual selection strategy was applied to mine individual offspring information and accelerate population convergence. To balance the exploration ability and computational resources of the FIOA, multiple neighborhood search rules were used to improve the utilization rate of individual offspring. In this study, the parameter configuration of the proposed algorithm was calibrated using the Taguchi method. To evaluate the effectiveness and superiority of the FIOA, each improvement of the FIOA algorithm was evaluated. In addition, it was compared with state-of-the-art algorithms in benchmark tests, and the results showed that the FIOA outperformed the other algorithms in solving flexible job shop scheduling.
基于特征信息优化算法的多目标柔性作业车间调度
多目标优化方法在作业车间调度优化策略中的应用越来越广泛。然而,在多目标优化策略的设计过程中,优化算法中对所有解进行邻域搜索,导致搜索时间较长。在算法选择过程中,个体携带的特征信息往往被忽略,导致算法缺乏针对性的引导能力。针对现有方法的局限性,提出了一种基于特征信息优化算法(FIOA)的多目标柔性作业车间调度方法。首先,采用多群体优化算法框架构建多群体;随后,采用具有代表性的个体选择策略挖掘个体子代信息,加速种群收敛。为了平衡FIOA的搜索能力和计算资源,采用了多邻域搜索规则来提高个体子代的利用率。在本研究中,采用Taguchi方法对算法的参数配置进行校准。为了评价FIOA算法的有效性和优越性,对FIOA算法的各项改进进行了评价。在基准测试中,将该算法与现有算法进行了比较,结果表明该算法在求解柔性作业车间调度方面优于其他算法。
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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