Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems - Mi Gan; Xinyuan Li; Fadong Zhang; Zhenggang He (Journal of Advanced Transportation, 2020)
Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.