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- W2981760986 abstract "In this paper, a truck scheduling problem at a cross-docking center is investigated where inbound trucks are also used as outbound. Moreover, inbound trucks do not need to unload and reload the demand of allocated destination, i.e. they can be partially unloaded. The problem is modeled as a mixed integer program to find the optimal dock-door and destination assignments as well as the scheduling of trucks to minimize makespan. Due to model complexity, a hybrid heuristic-simulated annealing is developed. A number of generic and tailor-made neighborhood search structures are also developed to efficiently search solution space. Moreover, some reinforcement learning methods are applied to intellectually learn more suitable neighborhood search structures in different situations. Finally, the numerical study shows that partial unloading of compound trucks has a crucial impact on makespan reduction." @default.
- W2981760986 created "2019-11-01" @default.
- W2981760986 creator A5054078409 @default.
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- W2981760986 date "2020-01-01" @default.
- W2981760986 modified "2023-10-12" @default.
- W2981760986 title "Truck scheduling in a multi-door cross-docking center with partial unloading – Reinforcement learning-based simulated annealing approaches" @default.
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- W2981760986 doi "https://doi.org/10.1016/j.cie.2019.106134" @default.
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