Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups

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

@Article{DURASEVIC:2024:asoc,

• author = "Marko Durasevic and Mateja Dumic",
• title = "Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups",
• journal = "Applied Soft Computing",
• volume = "150",
• pages = "111104",
• year = "2024",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2023.111104",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494623011225",
• keywords = "genetic algorithms, genetic programming, Container relocation problem, Hyper-heuristics, Relocation rules",
• abstract = "The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases",

}

Genetic Programming entries for Marko Durasevic Mateja Dumic

Citations