A genetic programming based cooperative evolutionary algorithm for flexible job shop with crane transportation and setup times

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@Article{Chen:2025:asoc,

• author = "Xiaolong Chen and Junqing Li and Zunxun Wang and Jiake Li and Kaizhou Gao",
• title = "A genetic programming based cooperative evolutionary algorithm for flexible job shop with crane transportation and setup times",
• journal = "Applied Soft Computing",
• year = "2025",
• volume = "169",
• pages = "112614",
• keywords = "genetic algorithms, genetic programming, Genetic programming hyper heuristic, Dispatching rules, Cooperative evolutionary algorithm, Flexible job shop scheduling problem, Crane transportation",
• ISSN = "1568-4946",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494624013887",
• DOI = "doi:10.1016/j.asoc.2024.112614",
• abstract = "Confronted with increasingly complex industrial scenarios, limited transportation resources and complicated time constraints introduce significant challenges to production efficiency, requiring more robust and adaptive scheduling heuristics. In this study, a flexible job shop scheduling problem with single crane transportation and sequence-dependent setup time is considered. To address the problem, a mixed integer linear programming model is established, where two objectives, including the maximum completion time and total energy consumption, are determined simultaneously. Additionally, a genetic programming (GP) based cooperative evolutionary algorithm is developed to address the problem, in which GP is investigated as a hyper-heuristic to construct a set of problem-specific dispatching rules (DRs). The GP-based hyper-heuristic (GPHH) first evolves a set of DRs during iterations and then applies these DRs to the initialization of the population. Next, four critical path-based neighbourhood structures combined with an adaptive local search mechanism are used to enhance the exploitation capability of the algorithm. The simulation results demonstrate that the GPHH used for initialization significantly outperforms other classical heuristics in convergence capability, while the proposed GP-CEA algorithm also surpasses six state-of-the-art algorithms in exploration and exploitation, achieving superior performance on the HV, IGD, and SC metrics in approximately 59.4percent, 46.9percent, and 50.0percent of instances, respectively",

}

Genetic Programming entries for Xiaolong Chen Junqing Li Zunxun Wang Jiake Li Kaizhou Gao

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