Combining meta-heuristics and Q-learning for scheduling lot-streaming hybrid flow shops with consistent sublots

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-09-11 DOI:10.1016/j.swevo.2024.101731

Benxue Lu , Kaizhou Gao , Yaxian Ren , Dachao Li , Adam Slowik

引用次数: 0

Abstract

This study addresses a hybrid flow shop scheduling problem by considering consistent sublots (HFSP_CS) in lot-streaming. The objective is to minimize the maximum completion time (makespan). By mathematically formulating the HFSP_CS, a mathematical model is established. Next, novel combinations of four meta-heuristics and Q-learning-based improvement tactics are proposed for tackling the related problems for the first time. Drawing upon problem-specific characteristics, five local search operators are employed and selected appropriately by utilizing Q-learning throughout the iterations. Furthermore, the model's veracity is demonstrated through the utilization of the CPLEX solver. Then, by resolving 128 instances, the enhanced algorithms showcase their effectiveness. The results show that the artificial bee colony algorithm integrated with Q-learning is the most competitive algorithm among the tested algorithms.

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结合元启发式和 Q-learning 方法，为具有一致子批次的批量流混合流动车间进行调度

本研究通过考虑批量流中的一致子批次（HFSP_CS）来解决混合流水车间调度问题。目标是最小化最大完成时间（makespan）。通过对 HFSP_CS 进行数学计算，建立了一个数学模型。接下来，首次提出了四种元启发式算法和基于 Q 学习的改进策略的新组合来解决相关问题。根据问题的具体特点，采用了五种局部搜索算子，并在整个迭代过程中利用 Q-learning 进行适当选择。此外，还利用 CPLEX 解算器证明了模型的真实性。然后，通过解决 128 个实例，增强算法展示了其有效性。结果表明，与 Q-learning 相结合的人工蜂群算法是测试算法中最具竞争力的算法。

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

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.