{"title":"一种平衡先序后的RMS设计算法:平衡可重构制造系统的性能保证数学方法","authors":"Youssef Lahrichi, Laurent Deroussi, Nathalie Grangeon, Sylvie Norre","doi":"10.1007/s10732-021-09473-1","DOIUrl":null,"url":null,"abstract":"<p>The Reconfigurable Transfer Line Balancing Problem (RTLB) is considered in this paper. This problem is quite recent and motivated by the growing need of reconfigurability in the new industry 4.0 context. The problem consists into allocating a set of operations necessary to machine a single part to different workstations placed into a serial line. Each workstation can contain multiple machines operating in parallel and the tasks allocated to a workstation should be sequenced since sequence-dependent setup times between operations are needed to perform tool changes. Besides, precedence constraints, inclusion, exclusion and accessibility constraints between operations are considered. In this article we propose an efficient matheuristic of type Balance First, Sequence Last (BFSL). This method is a two-step heuristic with a constructive phase and an improvement phase. It contains several components from exact methods (linear programming, constraint generation and dynamic programming) and metaheuristics (simulated annealing). In addition, we show that the constructive algorithm approximates the optimal solution when the setup times are bounded by the processing times and give an approximation ratio. The obtained results show the effectiveness of the proposed approach. The matheuristic clearly outperforms a genetic algorithm from literature on quite large benchmark instances.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"19 3","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A balance-first sequence-last algorithm to design RMS: a matheuristic with performance guaranty to balance reconfigurable manufacturing systems\",\"authors\":\"Youssef Lahrichi, Laurent Deroussi, Nathalie Grangeon, Sylvie Norre\",\"doi\":\"10.1007/s10732-021-09473-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The Reconfigurable Transfer Line Balancing Problem (RTLB) is considered in this paper. This problem is quite recent and motivated by the growing need of reconfigurability in the new industry 4.0 context. The problem consists into allocating a set of operations necessary to machine a single part to different workstations placed into a serial line. Each workstation can contain multiple machines operating in parallel and the tasks allocated to a workstation should be sequenced since sequence-dependent setup times between operations are needed to perform tool changes. Besides, precedence constraints, inclusion, exclusion and accessibility constraints between operations are considered. In this article we propose an efficient matheuristic of type Balance First, Sequence Last (BFSL). This method is a two-step heuristic with a constructive phase and an improvement phase. It contains several components from exact methods (linear programming, constraint generation and dynamic programming) and metaheuristics (simulated annealing). In addition, we show that the constructive algorithm approximates the optimal solution when the setup times are bounded by the processing times and give an approximation ratio. The obtained results show the effectiveness of the proposed approach. The matheuristic clearly outperforms a genetic algorithm from literature on quite large benchmark instances.</p>\",\"PeriodicalId\":54810,\"journal\":{\"name\":\"Journal of Heuristics\",\"volume\":\"19 3\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2021-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Heuristics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10732-021-09473-1\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Heuristics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10732-021-09473-1","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A balance-first sequence-last algorithm to design RMS: a matheuristic with performance guaranty to balance reconfigurable manufacturing systems
The Reconfigurable Transfer Line Balancing Problem (RTLB) is considered in this paper. This problem is quite recent and motivated by the growing need of reconfigurability in the new industry 4.0 context. The problem consists into allocating a set of operations necessary to machine a single part to different workstations placed into a serial line. Each workstation can contain multiple machines operating in parallel and the tasks allocated to a workstation should be sequenced since sequence-dependent setup times between operations are needed to perform tool changes. Besides, precedence constraints, inclusion, exclusion and accessibility constraints between operations are considered. In this article we propose an efficient matheuristic of type Balance First, Sequence Last (BFSL). This method is a two-step heuristic with a constructive phase and an improvement phase. It contains several components from exact methods (linear programming, constraint generation and dynamic programming) and metaheuristics (simulated annealing). In addition, we show that the constructive algorithm approximates the optimal solution when the setup times are bounded by the processing times and give an approximation ratio. The obtained results show the effectiveness of the proposed approach. The matheuristic clearly outperforms a genetic algorithm from literature on quite large benchmark instances.
期刊介绍:
The Journal of Heuristics provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly. It fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems. It considers the importance of theoretical, empirical, and experimental work related to the development of heuristics.
The journal presents practical applications, theoretical developments, decision analysis models that consider issues of rational decision making with limited information, artificial intelligence-based heuristics applied to a wide variety of problems, learning paradigms, and computational experimentation.
Officially cited as: J Heuristics
Provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly.
Fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems.
Considers the importance of theoretical, empirical, and experimental work related to the development of heuristics.