Solving the permutation flow shop scheduling problem with sequence-dependent setup time via iterative greedy algorithm and imitation learning

IF 4.4 2区数学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematics and Computers in Simulation Pub Date : 2025-03-05 DOI:10.1016/j.matcom.2025.02.026

Zhao-sheng Du , Jun-qing Li , Hao-nan Song , Kai-zhou Gao , Ying Xu , Jia-ke Li , Zhi-xin Zheng

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引用次数: 0

Abstract

In recent years, the application of learning-based methods in flow shop scheduling problem has gained considerable attention. However, there are gaps in the quality of their solution due to the difficulty of fully exploring the huge search space faced by learning-based methods and the difficulty of reward function design. In this paper, a hybrid approach of meta-heuristic algorithm and imitation learning (IL) is proposed to solve the permutation flow shop scheduling problem with sequence-dependent setup times (PFSP-SDST). Firstly, jobs are treated as nodes, and the processing time and setup times of PFSP-SDST are considered as features of the nodes, respectively. Secondly, a graph neural network based on an attention feature fusion (AFF) mechanism is designed as an encoder to embed the feature information of the problem. Finally, an iterative greedy algorithm based on critical path is proposed to provide high-quality expert solutions for the IL algorithm. The running results on randomly generated datasets and benchmark datasets demonstrate the effectiveness of the proposed method.

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利用迭代贪心算法和模仿学习方法解决了设置时间序列依赖的置换流水车间调度问题

近年来，基于学习的方法在流水车间调度问题中的应用受到了广泛的关注。然而，由于难以充分探索基于学习的方法所面临的巨大搜索空间以及奖励函数设计的困难，它们的解的质量存在差距。本文提出了一种元启发式算法和模仿学习（IL）的混合方法来解决具有序列依赖的设置时间的置换流水车间调度问题（PFSP-SDST）。首先，将作业视为节点，将PFSP-SDST的加工时间和设置时间分别视为节点的特征。其次，设计了基于注意特征融合（AFF）机制的图神经网络作为编码器，嵌入问题的特征信息；最后，提出了一种基于关键路径的迭代贪心算法，为IL算法提供了高质量的专家解。在随机生成的数据集和基准数据集上的运行结果证明了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mathematics and Computers in Simulation 数学-计算机：跨学科应用

CiteScore

8.90

自引率

4.30%

发文量

335

审稿时长

54 days

期刊介绍： The aim of the journal is to provide an international forum for the dissemination of up-to-date information in the fields of the mathematics and computers, in particular (but not exclusively) as they apply to the dynamics of systems, their simulation and scientific computation in general. Published material ranges from short, concise research papers to more general tutorial articles. Mathematics and Computers in Simulation, published monthly, is the official organ of IMACS, the International Association for Mathematics and Computers in Simulation (Formerly AICA). This Association, founded in 1955 and legally incorporated in 1956 is a member of FIACC (the Five International Associations Coordinating Committee), together with IFIP, IFAV, IFORS and IMEKO. Topics covered by the journal include mathematical tools in: •The foundations of systems modelling •Numerical analysis and the development of algorithms for simulation They also include considerations about computer hardware for simulation and about special software and compilers. The journal also publishes articles concerned with specific applications of modelling and simulation in science and engineering, with relevant applied mathematics, the general philosophy of systems simulation, and their impact on disciplinary and interdisciplinary research. The journal includes a Book Review section -- and a "News on IMACS" section that contains a Calendar of future Conferences/Events and other information about the Association.