A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem

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@Article{Xuedong_Zhu:ASE,

• author = "Xuedong Zhu and Xinxing Guo and Weihao Wang and Jianguo Wu",
• title = "A Genetic Programming-Based Iterative Approach for the Integrated Process Planning and Scheduling Problem",
• journal = "IEEE Transactions on Automation Science and Engineering",
• year = "2022",
• volume = "19",
• number = "3",
• pages = "2566--2580",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1558-3783",
• DOI = "doi:10.1109/TASE.2021.3091610",
• abstract = "The integrated process planning and scheduling (IPPS) problem is studied in this article, in which operation sequencing, process plan selection, and machine selection are decided simultaneously. For different scenarios, three mixed-integer linear programming (MILP) models are designed. Then, in view of the workload of machines and processing times of jobs, two machine selection techniques are introduced to simplify the optimization of these MILP models. By exploring the structural properties of the MILP models, we put forward a novel lower bound to act as a measurement for the performance of the related algorithms. Considering the real-time requirement and complexity of instances in practice, we design a hybrid greedy heuristic based on a new decision structure of the problem and dispatching rules. Furthermore, in order to create effective dispatching rules to improve the hybrid greedy heuristic, enhanced genetic programming (GP)-based iterative approach is proposed. Experimental results indicate that our approaches are better than other available approaches for the IPPS problem and can reduce the computational time while providing high-quality solutions.",
• notes = "Also known as \cite{9475992}",

}

Genetic Programming entries for Xuedong Zhu Xinxing Guo Weihao Wang Jianguo Wu

Citations