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

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

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.

Citation

Chen, X., Bai, R., Qu, R., & Dong, H. (2023). Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching. IEEE Transactions on Evolutionary Computation, 27(5), 1220-1234. https://doi.org/10.1109/TEVC.2022.3209985

• 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