Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

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@Article{Li:2024:TST,

• author = "Xixing Li and Qingqing Zhao and Hongtao Tang and Xing Guo and Mengzhen Zhuang and Yibing Li and Xi Vincent Wang",
• title = "Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm",
• journal = "Tsinghua Science and Technology",
• year = "2024",
• volume = "29",
• number = "5",
• pages = "1390--1408",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Sociology, Binary trees, Deep reinforcement learning, Dispatching, Encoding, flexible job shop scheduling, composite dispatching rule, improved genetic programming algorithm",
• ISSN = "1007-0214",
• DOI = "doi:10.26599/TST.2023.9010141",
• abstract = "To obtain a suitable scheduling scheme in an effective time range, the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems (FJSP) with different scales, and Composite Dispatching Rules (CDRs) are applied to generate feasible solutions. Firstly, the binary tree coding method is adopted, and the constructed function set is normalised. Secondly, a CDR mining approach based on an Improved Genetic Programming Algorithm (IGPA) is designed. Two population initialization methods are introduced to enrich the initial population, and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm. At the same time, two individual mutation methods are introduced to improve the algorithm's local search ability, to achieve the balance between global search and local search. In addition, the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis. Finally, Deep Reinforcement Learning (DRL) is employed to solve the FJSP by incorporating the CDRs as the action set, the selection times are counted to further verify the superiority of CDRs.",
• notes = "Also known as \cite{10517917}",

}

Genetic Programming entries for Xixing Li Qingqing Zhao Hongtao Tang Xing Guo Mengzhen Zhuang Yibing Li Xi Vincent Wang

Citations