A learning-based approach for dynamic freight brokerages with transfer and territory-based assignment

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.