Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{durasevic:2024:CEC2,

• author = "Marko Durasevic and Mateja Dumic and Francisco Javier Gil-Gala",
• title = "Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Metaheuristics, Evolutionary computation, Containers, online container relocation problem, relocation rules",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611835",
• abstract = "The container relocation problem (CRP) in shipping terminals is becoming increasingly important due to the growing amount of transferred goods. Until now, the most commonly investigated problem variant has been the offline CRP, in which the order in which the containers need to be retrieved is known beforehand. However, in many real-world situations this is not the case, which is modelled using the online CRP variant. In this variant, not all information is available from the beginning, but rather, it becomes available as the problem is being solved. Unfortunately, many traditional metaheuristic solution methods can not be applied to such a problem variant, which prompts the application of problem-specific heuristics called relocation rules (RRs). However, RRs are challenging to design manually, which prompted the application of genetic programming (GP) to design them automatically. Since GP was used only to design RRs for the offline problem variant, we apply GP to design relocation rules for the online variant in this study. The performance of GP is investigated under different levels of information availability to measure its performance. The results demonstrate that GP can evolve RRs that perform better than existing manually designed ones. Furthermore, the results show that in certain cases, rules generated for one level of information availability perform well for other levels, demonstrating that the evolved rules exhibit a good generalisation capability.",
• notes = "also known as \cite{10611835}

  WCCI 2024",

}

Genetic Programming entries for Marko Durasevic Mateja Dumic Francisco Javier Gil Gala

Citations