Automated design of heuristics for the container relocation problem using genetic programming

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@Article{DURASEVIC:2022:asoc,

• author = "Marko Durasevic and Mateja Dumic",
• title = "Automated design of heuristics for the container relocation problem using genetic programming",
• journal = "Applied Soft Computing",
• volume = "130",
• pages = "109696",
• year = "2022",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2022.109696",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494622007451",
• keywords = "genetic algorithms, genetic programming, Container relocation problem, Hyper-heuristics, Relocation rules",
• abstract = "The container relocation problem is a challenging combinatorial optimisation problem tasked with finding a sequence of container relocations required to retrieve all containers by a given order. Due to the complexity of this problem, heuristic methods are often applied to obtain acceptable solutions in a small amount of time. These include relocation rules (RRs) that determine the relocation moves that need to be performed to efficiently retrieve the next container based on certain yard properties. Such rules are often designed manually by domain experts, which is a time-consuming and challenging task. This paper investigates the application of genetic programming (GP) to design effective RRs automatically. Experimental results show that RRs evolved by GP outperform several existing manually designed RRs. Additional analyses of the proposed approach demonstrate that the evolved rules generalise well across a wide range of unseen problems and that their performance can be further enhanced. Therefore, the proposed method presents a viable alternative to existing manually designed RRs and opens a new research direction in the area of container relocation problems",
• notes = "See also \cite{DURASEVIC:2023:asoc}",

}

Genetic Programming entries for Marko Durasevic Mateja Dumic

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