A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

Chen, Binhui, Qu, Rong, Bai, Ruibin and Laesanklang, Wasakorn (2018) A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows. Applied Intelligence, 48 . pp. 4937-4959. ISSN 1573-7497

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In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distance and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance.

Due to the large scale and nonlinear constraints, exact search showed not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this multi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/946464
Keywords: Hyper-heuristic; Mixed-shift vehicle routing problem with time windows; Bi-objective; Container transportation
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/s10489-018-1250-y
Depositing User: Eprints, Support
Date Deposited: 26 Jul 2018 08:19
Last Modified: 01 Jun 2021 06:45
URI: https://eprints.nottingham.ac.uk/id/eprint/53156

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