Dynamic integrated process planning and scheduling under multi-resource constraints in workshops with reconfigurable manufacturing cells: a novel hyper-heuristic approach

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@Article{Guo:2025:eswa,

• author = "Haoxin Guo and Kunping Li and Jianhua Liu and Cunbo Zhuang and Fengque Pei",
• title = "Dynamic integrated process planning and scheduling under multi-resource constraints in workshops with reconfigurable manufacturing cells: a novel hyper-heuristic approach",
• journal = "Expert Systems with Applications",
• year = "2025",
• volume = "289",
• pages = "128337",
• keywords = "genetic algorithms, genetic programming, Integrated Process Planning and Scheduling, Multi-Resource Constraints, Reconfigurable Manufacturing Cells, Hyper-heuristic, Bloat Control",
• ISSN = "0957-4174",
• URL = "https://www.sciencedirect.com/science/article/pii/S0957417425019566",
• DOI = "doi:10.1016/j.eswa.2025.128337",
• abstract = "This study addresses the challenges of hybrid production lines, reconfigurable characteristics, frequent disturbances, and multi-resource constraints in complex aerospace product assembly and testing workshops. We propose a Dynamic Integrated Process Planning and Scheduling under Multi-Resource Constraints in Workshops with Reconfigurable Manufacturing Cells (MRC-DIPPS-RMC). By establishing an integrated mathematical model that combines process planning, cell reconfiguration, task scheduling, and resource allocation, we designed a Genetic Programming Hyper-Heuristic with Bloat Control Mechanism (GPHH-BC) based on multi-heuristic co-evolution. The algorithm employs population segmentation to co-evolve four types of heuristic rules, effectively solving five critical subproblems in dynamic environments while successfully suppressing efficiency degradation caused by rule bloating. Experimental results demonstrate that the proposed method demonstrates a 52.67 percent improvement in computational efficiency compared to conventional baseline approaches while ensuring solution feasibility; when compared to state-of-the-art algorithms, it achieves a further 7.40 percent improvement in computational efficiency",

}

Genetic Programming entries for Haoxin Guo Kunping Li Jianhua Liu Cunbo Zhuang Fengque Pei

Citations