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Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching

Chen, Xinan; Bai, Ruibin; Qu, Rong; Dong, Haibo

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Authors

Xinan Chen

Ruibin Bai

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RONG QU rong.qu@nottingham.ac.uk
Professor of Computer Science

Haibo Dong



Abstract

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading–unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal.

Journal Article Type Article
Acceptance Date Sep 1, 2022
Online Publication Date Sep 27, 2022
Publication Date 2023-10
Deposit Date Oct 5, 2022
Publicly Available Date Oct 11, 2022
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Electronic ISSN 1941-0026
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 27
Issue 5
Pages 1220-1234
DOI https://doi.org/10.1109/TEVC.2022.3209985
Keywords Computational Theory and Mathematics; Theoretical Computer Science; Software
Public URL https://nottingham-repository.worktribe.com/output/11756174
Publisher URL https://ieeexplore.ieee.org/document/9903916

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