Genetic Programming Hyper Heuristic With Elitist Mutation for Integrated Order Batching and Picker Routing Problem

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@Article{Wang:2025:TEVC,

• author = "Yuquan Wang and Naiming Xie and Nanlei Chen and Hui Ma and Gang Chen2",
• title = "Genetic Programming Hyper Heuristic With Elitist Mutation for Integrated Order Batching and Picker Routing Problem",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2025",
• volume = "29",
• number = "2",
• pages = "346--359",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Routing, Metaheuristics, Evolutionary computation, Search problems, Layout, Processor scheduling, Job shop scheduling, Heuristic algorithms, Elitist mutation (EM), genetic programming hyper heuristic (GPHH), integrated order batching and picker routing (IOBPR) problem",
• ISSN = "1941-0026",
• DOI = "doi:10.1109/TEVC.2025.3532022",
• abstract = "Integrated order batching and picker routing (IOBPR) is a complex combinatorial optimisation problem in real-world intelligent manufacturing systems. Heuristics are often used for solving such complex scheduling problems. Manually designing scheduling heuristics suffer from two limitations: 1) few problem features can be taken into account and 2) the design process is time consuming. Genetic programming hyper heuristic (GPHH) approaches have been proposed on many scheduling problems to automatically evolve effective heuristics. However, existing GPHH approaches are often problem specific and requires careful design of problem specific terminal sets and evolution operators. The aim of this work is to develop a GPHH approach to evolve heuristics for the IOBPR problem. In particular, we propose a novel terminal set (NT) with three types of terminals, and a GPHH with elitist mutation (GPHH-EM) algorithm. Extensive experiments demonstrate that the heuristics evolved by GPHH-EM can significantly outperform other state-of-the-art competing algorithms designed by human experts. Further analysis indicates that the three types of terminals effectively complement to improve evolved heuristics for decision making. Furthermore, the newly developed elitist mutation operator expedites the evolutionary process for GPHH to find high-quality heuristics.",
• notes = "Also known as \cite{10847907}",

}

Genetic Programming entries for Yuquan Wang Naiming Xie Nanlei Chen Hui Ma Aaron Chen

Citations