The trucking industry is an important sector of the U.S. economy. However, it is quite fragmented, hindering the efficiency of cargo transportation and the ability for small carriers to identify demands to fill full truck loads. The focus of this research is to study models and algorithms in order to aid online freight marketplaces to identify efficient consolidation strategies. To accomplish this aim, a new mixed integer programming model for the pickup and delivery problem has been developed. The model is geared towards identifying effective freight consolidation opportunities. A branch-and-cut algorithm to solve the model was also developed. The model was applied to several case studies using Transplace, Inc. (a third party logistics company) route data to identify optimized, consolidated routes. Emission impacts were also estimated for both the existing and consolidated routes using monetary equivalent cost (MEC) values. A linear regression model was developed to predict freight movement between metropolitan statistical areas (MSAs). Results of the case studies were then applied to estimate the operation and environment-related costs associated with freight movement from New Orleans MSA to Oklahoma City MSA for all commodities. Finally, the results of the case studies were applied at the national level and projected for future years, to estimate the potential cost savings in freight consolidation. Results indicate that it may be possible to consolidate cargo with only 67% of the currently used number of trucks, which may reduce the operation cost by 23% and MEC by 17%.
Liu, T., & Zhao, C. (2019). Impacts of Freight Consolidation and Truck Sharing on Freight Mobility. Retrieved from https://digitalcommons.lsu.edu/transet_pubs/31