Modeling Freight-Sharing Platform Operations for Optimal Compensation Strategy Using Markov Decision Processes

Siqi Shu, Zhengqi Chen, Zhe Yu, Shaosheng Cao, Guobin Wu, Donghai Shi, Gaoang Wang, Zuozhu Liu, Xiqun Chen, Xiaoxiang Na, Chao Wu, Simon Hu

Abstract: The urban freight-sharing is one special part of ride-sharing due to its characteristics corresponding to urban freight orders, including the high fragmentation of both demand and supply and geographic concentration. This study has applied a Markov Decision Process framework to determine the static and dynamic optimal compensation strategy offered to shippers and carriers, which aims to maximize the longterm accumulated expected discounted rewards for the freight-sharing platform. More specifically, with the incorporation of stochastic arrival of shippers and carriers, decisions of a shipper placing an order and a carrier accepting an order, the maximum amount of orders and carriers the platform could accommodate, and the current state of the platform regarding the number of unmatched orders and carriers, models are designed to give insights about the optimal compensation-settings under various scenarios with different supply and demand arrival rate. The developed models are tested with the real-world data.