A dynamic multi-objective evolutionary greedy algorithm for distributed hybrid flow shop rescheduling problem

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

Swarm and Evolutionary Computation Pub Date : 2025-07-05 DOI:10.1016/j.swevo.2025.102054

Xin-Rui Tao , Quan-Ke Pan , Xue-Lei Jing , Wei-Min Li

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Abstract

This paper addresses a distributed hybrid flowshop rescheduling problem (DHFRP) with new job insertion and machine breakdowns, which exists widely in modern industry. The objective is to minimize makespan and total tardiness time. A dynamic multi-objective evolutionary greedy algorithm is used to solve this rescheduling problem. An initialization strategy is designed to generate a high-quality initial population. An adaptive perturbation process and a local search procedure further enhance the quality of the population. For dynamic changes in the processing environment, the valid information in the original non-dominated solution set is utilized in order to efficiently obtain a rescheduling solution. In addition, a deep reinforcement learning algorithm is used to make decisions on the rescheduling strategy to be adopted. This operation maintains the stability of the production process and effectively reduces the transportation cost during processing. Finally, a series of numerical experiments demonstrate the effectiveness of the proposed algorithm in solving DHFRP. This is further supported by a case study based on real industrial production.

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分布式混合流水车间重调度问题的动态多目标进化贪婪算法

本文研究了现代工业中广泛存在的具有新作业插入和机器故障的分布式混合流水车间重调度问题。目标是最小化完工时间和总延误时间。提出了一种动态多目标进化贪婪算法来解决这一问题。初始化策略旨在生成高质量的初始种群。自适应扰动过程和局部搜索过程进一步提高了种群的质量。对于处理环境的动态变化，利用原始非支配解集中的有效信息，有效地获得重调度解。此外，采用深度强化学习算法来决定采用何种重调度策略。这种操作保持了生产过程的稳定性，有效降低了加工过程中的运输成本。最后，通过一系列数值实验验证了该算法在求解DHFRP问题中的有效性。基于实际工业生产的案例研究进一步支持了这一观点。

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.