{"title":"增强型多目标进化算法与强化学习用于灵活作业车间的节能调度","authors":"Jinfa Shi, Wei Liu, Jie Yang","doi":"10.3390/pr12091976","DOIUrl":null,"url":null,"abstract":"The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Enhanced Multi-Objective Evolutionary Algorithm with Reinforcement Learning for Energy-Efficient Scheduling in the Flexible Job Shop\",\"authors\":\"Jinfa Shi, Wei Liu, Jie Yang\",\"doi\":\"10.3390/pr12091976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops.\",\"PeriodicalId\":20597,\"journal\":{\"name\":\"Processes\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Processes\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/pr12091976\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Processes","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/pr12091976","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
An Enhanced Multi-Objective Evolutionary Algorithm with Reinforcement Learning for Energy-Efficient Scheduling in the Flexible Job Shop
The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops.
期刊介绍:
Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.