{"title":"Reinforcement learning for disassembly sequence planning optimization","authors":"Amal Allagui , Imen Belhadj , Régis Plateaux , Moncef Hammadi , Olivia Penas , Nizar Aifaoui","doi":"10.1016/j.compind.2023.103992","DOIUrl":null,"url":null,"abstract":"<div><p>The disassembly process is one of the most expensive phases in the product life cycle for both maintenance and the End of Life dismantling process. Industry must optimize the disassembly sequence to ensure time-cost-efficiency. This paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. This research work focuses on two types of dismantling: partial and full disassembly. By introducing a fitness function within the Reinforcement Learning algorithm, it is aimed at implementing optimized Disassembly Sequence Planning for five disassembly parameters or goals: (1) minimizing disassembly tool changes, (2) minimizing disassembly direction changes, (3) optimizing dismantling time including preparation and processing time, (4) prioritizing the dismantling of the smallest parts, and (5) facilitating access to wear parts. The proposed approach is applied to a demonstrative example. Finally, a comparison with other approaches from the literature is provided to demonstrate the efficiency of the new approach.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Industry","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166361523001422","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The disassembly process is one of the most expensive phases in the product life cycle for both maintenance and the End of Life dismantling process. Industry must optimize the disassembly sequence to ensure time-cost-efficiency. This paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. This research work focuses on two types of dismantling: partial and full disassembly. By introducing a fitness function within the Reinforcement Learning algorithm, it is aimed at implementing optimized Disassembly Sequence Planning for five disassembly parameters or goals: (1) minimizing disassembly tool changes, (2) minimizing disassembly direction changes, (3) optimizing dismantling time including preparation and processing time, (4) prioritizing the dismantling of the smallest parts, and (5) facilitating access to wear parts. The proposed approach is applied to a demonstrative example. Finally, a comparison with other approaches from the literature is provided to demonstrate the efficiency of the new approach.
期刊介绍:
The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that:
• Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry;
• Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry;
• Foster connections or integrations across diverse application areas of ICT in industry.