A learning-based approach for dynamic freight brokerages with transfer and territory-based assignment
- 주제(키워드) Reinforcement learning , Freight brokerage , Dynamic assignment problem , Territory-based assignment , Transferring
- 주제(기타) Computer Science, Interdisciplinary Applications
- 주제(기타) Engineering, Industrial
- 설명문(일반) [Min, Daiki; Kang, Yuncheol] Ewha Womans Univ, Sch Business, Seoul, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 PERGAMON-ELSEVIER SCIENCE LTD
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000181509
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.cie.2020.107042
초록/요약
The recent evolution of information technology in logistics has facilitated the digital freight brokerage, which allows shippers and trucks to match needs and services in a short-term period. In the context of freight brokerages, this paper considers a dynamic fleet assignment problem related to matching demand and supply. We particularly integrate practical operational characteristics, such as territory-based assignment and transferring, which have not been considered in the dynamic fleet assignment problem. We first formulate the problem as a Markov Decision Process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, to overcome the dimensionality and ambiguity of the MDP model, we proposed a reinforcement learning(RL) approach with function approximation for solving the MDP model. Finally, numerical experiments are carried out to illustrate the superiority of the RL method and analyze the effects of operational characteristics. A numerical analysis shows that the proposed RL-based method provides more rewards than other policies, such as myopic policy and first-come-first-served (FCFS) policy, for all test scenarios. The RL-based method is even better for a situation in which the delivery territories are highly overlapped and customer demand exceeds to supply capacity, which requires precise capacity control. In addition, we observe that significant achievement can be attained by allowing trucks to deliver with transfer.
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