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Dynamic Trucking Equilibrium in a Freight Exchange Platform with Hyperpath Routing

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This dissertation proposes a new hyperpath-based truck equilibrium assignment model with bidding. Unique from other trucking assignment models that examine assigning trucks for a large fleet of vehicles under a company's control, this model considers the case of many independent but homogeneous carriers that are competing for loads by bidding in auctions administered by an online freight exchange. The competitors follow optimal bidding hyperpaths to maximize their expected profit. The carriers all utilize an Online freight exchange (OFEX) platform that allows shippers to advertise loads to a large number of freight operators in search of loads. Such platforms often serve the purpose of matching demand and supply for freight in real-time. The dissertation starts by developing a hyperpath truck routing problem that leverages the power of an OFEX platform. The OFEX routing problem seeks to determine a hyperpath in a space-time expanded network that maximizes the expected profit for a given origin-destination pair and tour duration. 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 present 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 trucks' expected profits up to 400%, compared to the myopic and recursive 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. The interference of the hyperpath routing model is that as more trucks utilize the method, the less advantageous the hyperpaths become due to the increased competition. Therefore, the dissertation continues to explain an equilibrium model that leverages the hyperpath truck routing solution to enable a solution for all trucks. This dissertation proposes a new hyperpath-based dynamic trucking equilibrium (DTE) assignment model. Unlike existing freight assignment models, this model focuses on the interactions between individual truck operators that solely compete for loads advertised on an online freight exchange. The competing trucks are assumed to follow optimal bidding and routing strategies - represented using a hyperpath - to maximize their expected profit. The proposed DTE model helps (1) predict system-wide truck flows (including empty truck flows), (2) identify efficiency improvements gained by network-wide visibility, and (3) lay the foundation for building a system optimal model. We rewrite the DTE conditions as a variational inequality problem (VIP) and discuss the analytical properties of the formulation, including solution existence. A heuristic solution algorithm is developed to solve the VIP, which consists of three modules: a dynamic network loading procedure for mapping hyperpath flows onto the freight network, a column generation scheme for creating hyperpaths as needed, and a method of successive average for equilibrating profits on existing hyperpaths. The model and the solution algorithm are validated by numerical experiments constructed from empirical data collected in China. The results show that the DTE solutions outperform with wide margin the benchmark solutions that either ignore inter-truck interaction or operate trucks according to suboptimal routing and bidding decisions.

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  • 03/07/2019
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