An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yarong Chen, Jinhao Du, Jabir Mumtaz, Jingyan Zhong, Mudassar Rauf
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Abstract

Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop scheduling problem dominant in real-world printed circuit board assembly shops. A novel multi-objective hyper-heuristic combining Q-learning, i.e., two-stage improved spider monkey optimization (TS-ISMO), is tailored to address the complexities of the flow shop scheduling problems. The proposed method aims to simultaneously optimize conflicting objectives such as minimizing makespan, total energy consumption, and total tardiness time while incorporating lot streaming considerations. For multi-objective hyper-heuristic techniques, the algorithm dynamically selects and adapts a diverse set of low-level heuristics to explore the solution space comprehensively and strike a balance among competing objectives. The proposed TS-ISMO algorithm incorporates several significant features aimed at enhancing its performance. These features encompass hybrid heuristics for solution initialization, a contribution value method for comprehensive convergence and diversity assessment, diverse evolutionary state judgments to promote the algorithm’s balance between exploration and exploitation capabilities, and a Q-learning strategy for self-adaptive parameter tuning. The integration of Q-learning facilitates intelligent parameter control, enabling the algorithm to autonomously adjust its behavior based on past experiences and evolution dynamics. This adaptive mechanism enhances convergence speed and solution quality by effectively guiding the search process toward promising regions of the solution space. Extensive computational experiments are conducted on benchmark instances of hybrid flow shop scheduling problems with lot streaming to evaluate the performance of the proposed algorithm. Comparative analyses against state-of-the-art approaches demonstrate its superior solution quality and computational efficiency.
一种高效的 Q-learning 集成多目标超启发式方法,适用于具有批量流的混合流水车间调度问题
在具有批量流的流水作业环境中进行高效调度,仍然是各种工业环境中的一项重大挑战,需要采用创新方法来优化生产流程。本研究探讨了现实世界中印刷电路板装配车间普遍存在的混合流水车间调度问题。为解决流动车间调度问题的复杂性,本研究采用了一种结合 Q 学习的新型多目标超启发式方法,即两阶段改进蜘蛛猴优化(TS-ISMO)。所提出的方法旨在同时优化相互冲突的目标,如最小化工期、总能耗和总延迟时间,同时将批量流考虑在内。对于多目标超启发式技术,该算法动态地选择和调整各种底层启发式,以全面探索解空间,并在相互竞争的目标之间取得平衡。所提出的 TS-ISMO 算法包含几个旨在提高其性能的重要特征。这些特征包括用于解决方案初始化的混合启发式算法、用于全面收敛和多样性评估的贡献值方法、用于促进算法在探索和利用能力之间取得平衡的多样化进化状态判断,以及用于自适应参数调整的 Q-learning 策略。Q-learning 的集成促进了智能参数控制,使算法能够根据过去的经验和进化动态自主调整其行为。这种自适应机制能有效地引导搜索过程朝向解空间的前景区域,从而提高收敛速度和解质量。为了评估所提出算法的性能,我们在具有批量流的混合流车间调度问题的基准实例上进行了广泛的计算实验。与最先进方法的对比分析表明,该算法具有卓越的求解质量和计算效率。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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