My work in this area was resulted from my collaborations  with an online freight exchange platform in China between 2017 and 2019.  When I began to work with the firm in 2017, through Xiaobo Liu at SWJTU,  it was called Truck Gang (货车帮）.  Soon after that it was merged with Yunmanman (运满满）,  and the merged company was named Manbang （满帮).   When Manbang eventually went public in 2021, it was valued at nearly $24B.    The results reported in this paper were produced using data provided by Truck Gang, and the paper was published in Transportation Science a couple of years ago, co-authored by my former student John Miller and Xiaobo.

Abstract:  Online freight exchange (OFEX) platforms serve the purpose of matching demand and supply for freight in real time. This paper studies a truck routing problem that aims to leverage the power of an OFEX platform. The OFEX routing problem is formulated as a Markov decision problem, which we solve by finding the bidding strategy at each possible location and time along the route that maximizes the expected profit. At the core of the OFEX routing problem is a combined pricing and bidding model that simultaneously (1) considers the probability of winning a load at a given bid price and current market competition, (2) anticipates the future profit corresponding to the current decision, and (3) prioritizes the bidding order among possible load options. Results from numerical experiments constructed using real-world data from a Chinese OFEX platform indicate that the proposed routing model could (1) improve a truck’s expected profit substantially, compared with the benchmark solutions built to represent the state of the practice, and (2) enhance the robustness of the overall profitability against the impact of market competition and spatial variations.