Isabella Demetz, Roy Lin, Santiago Karam
Abstract: This project addresses the challenge of reducing greenhouse gas emissions from the trucking industry by inte- grating electric vehicles (EVs) into logistics operations. While EVs offer significant environmental benefits, their adoption is constrained by limited range, prolonged charging times, and high infrastructure costs. Our project develops and tests optimization models to balance the trade-offs between gas and electric vehicles in multi-depot vehicle routing scenarios. The models identify optimal depot locations, vehicle routes, and charging station placements while minimizing overall costs, including facility setup, vehicle acquisition, and transportation expenses. Two formulations are proposed: a time-series model incrementally introducing charging stations and a single-shot model optimizing all locations simultaneously. The results demonstrate the feasi- bility of gradual infrastructure expansion while ensuring system efficiency, providing a roadmap for sustainable logistics in the face of growing climate concerns.
Nanshan Jia, Yifu Tang, Zizhao Zhang
Abstract: In this paper, we consider a scenario where a company decides to expand its market to a new city, the company should decide a place to build its factory. As stated before, we have to consider the carbon-emission cost during transportation. Specifically, there are two candidate vehicles: gasoline vehicle and electronic vehicle. Running gasoline vehicles will be punished by additional carbon tax, while electric vehicles are not able to travel very long distance due to the scarcity of charging stations. The model we are considering includes Stochastic Demand, Transportation route, as well as the Long- run average case (SDTL Model).