{"title":"Scheduling in a three-stage remanufacturing system with machine blockage, deterioration and maintenance using metaheuristic algorithm","authors":"Zihao Luo, Wenyu Zhang, Mengfei Liu","doi":"10.1016/j.swevo.2025.102185","DOIUrl":null,"url":null,"abstract":"<div><div>Remanufacturing plays a significant role in sustainable development. A complete remanufacturing process integrates three stages: disassembly, reprocessing, and reassembly. To bring the problem closer to real-world scenarios, this study proposes a scheduling problem for three-stage remanufacturing system considering machine blockage, deterioration and maintenance. Rate-modifying activity (RMA), as a type of maintenance activity, is executed to address the time-dependent deterioration. To solve this problem, first, a new deterioration model with RMAs is proposed to estimate the actual reprocessing time and determine the strategy for RMA execution. Second, a new blocking scheduling model is established to minimize the makespan. To find satisfactory solutions in a reasonable time, a new metaheuristic called modified monarch butterfly optimization (MMBO) algorithm is proposed. In MMBO algorithm, a problem-specific constructive heuristic and a new machine load balancing strategy are proposed to generate high-quality initial solutions. Then, two improved operators, adaptive to the solution representation scheme, are designed for exploring the solution space. Finally, experiments and comparison with state-of-the-art algorithms are made to demonstrate the effectiveness and superiority of the MMBO algorithm for this problem.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"99 ","pages":"Article 102185"},"PeriodicalIF":8.5000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225003426","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Remanufacturing plays a significant role in sustainable development. A complete remanufacturing process integrates three stages: disassembly, reprocessing, and reassembly. To bring the problem closer to real-world scenarios, this study proposes a scheduling problem for three-stage remanufacturing system considering machine blockage, deterioration and maintenance. Rate-modifying activity (RMA), as a type of maintenance activity, is executed to address the time-dependent deterioration. To solve this problem, first, a new deterioration model with RMAs is proposed to estimate the actual reprocessing time and determine the strategy for RMA execution. Second, a new blocking scheduling model is established to minimize the makespan. To find satisfactory solutions in a reasonable time, a new metaheuristic called modified monarch butterfly optimization (MMBO) algorithm is proposed. In MMBO algorithm, a problem-specific constructive heuristic and a new machine load balancing strategy are proposed to generate high-quality initial solutions. Then, two improved operators, adaptive to the solution representation scheme, are designed for exploring the solution space. Finally, experiments and comparison with state-of-the-art algorithms are made to demonstrate the effectiveness and superiority of the MMBO algorithm for this problem.
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.