{"title":"An enhanced memetic algorithm for energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart","authors":"Wentao Wang, Jing Zhao","doi":"10.1016/j.jmsy.2025.03.013","DOIUrl":null,"url":null,"abstract":"<div><div>The technological advancements of Industry 5.0 place greater emphasis on environmental sustainability and resilience for production scheduling. The flexible job shop scheduling problem (FJSP) effectively adapts to complex production environments and diverse scheduling requirements, which has made it an essential tool for studying modern production scenarios. Against this backdrop, this paper proposes an energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart (ELFJSP-MR), aiming to minimize the makespan and total carbon emissions of the system. To solve ELFJSP-MR, we present an enhanced memetic algorithm (EMA) and design machine restart strategy to balance energy consumption and equipment lifespan. A multi-population hybrid model initialization based on logistic population growth model is used to enhance initial population diversity. Two novel neighborhood search methods are developed to improve convergence speed and explore the solution space more thoroughly. To enhance the flexibility and efficiency of local search, an adaptive operator selection model is designed. Finally, EMA and four well-known algorithms are evaluated on various benchmark problem instances. Experimental results demonstrate that EMA achieves faster convergence and greater stability for ELFJSP-MR. Furthermore, EMA exhibits exceptional performance across eight instances of aerospace composite material processing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 457-478"},"PeriodicalIF":12.2000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525000743","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
The technological advancements of Industry 5.0 place greater emphasis on environmental sustainability and resilience for production scheduling. The flexible job shop scheduling problem (FJSP) effectively adapts to complex production environments and diverse scheduling requirements, which has made it an essential tool for studying modern production scenarios. Against this backdrop, this paper proposes an energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart (ELFJSP-MR), aiming to minimize the makespan and total carbon emissions of the system. To solve ELFJSP-MR, we present an enhanced memetic algorithm (EMA) and design machine restart strategy to balance energy consumption and equipment lifespan. A multi-population hybrid model initialization based on logistic population growth model is used to enhance initial population diversity. Two novel neighborhood search methods are developed to improve convergence speed and explore the solution space more thoroughly. To enhance the flexibility and efficiency of local search, an adaptive operator selection model is designed. Finally, EMA and four well-known algorithms are evaluated on various benchmark problem instances. Experimental results demonstrate that EMA achieves faster convergence and greater stability for ELFJSP-MR. Furthermore, EMA exhibits exceptional performance across eight instances of aerospace composite material processing.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.