Truck scheduling in a multi-door cross-docking center with partial unloading – Reinforcement learning-based simulated annealing approaches. (January 2020)

• Record Type: Journal Article
• Authors: Shahmardan, Amin; Sajadieh, Mohsen S.
• Is Part Of: Computers & industrial engineering. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Logistics -- Cross docking -- Truck scheduling -- Simulated annealing -- Reinforcement learning; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.106134
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A truck scheduling problem in a multi-door cross-docking center is studied. Inbound trucks can be partially unloaded and can be used as outbound trucks. A new heuristic algorithm is developed to find initial solutions in a short time. An RL-based SA algorithm with some tailor-made neighborhood structures is proposed. Numerical results show that partial unloading can reduce makespan tangibly. 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.