A two-stage stochastic location-routing problem for electric vehicles fast charging

Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical electric vehicle can travel. Electric vehicle batteries' performance and capacity are affected by many factors. In particular, the decrease in ambient temperature below a certain threshold will adversely affect the battery's efficiency. This paper develops a two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use electric vehicles while considering the variability inherent in climate and customer demand. A novel solution approach based on the progressive hedging algorithm is presented to solve the resulting mathematical model and to provide high-quality solutions within reasonable running times for problems with many scenarios. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a testbed. We observe that the location-routing decisions are susceptible to the electric vehicle logistics underlying climate, signifying that decision-makers of the direct current fast charging electric vehicle logistic network for cities that suffer from high-temperature fluctuations would not overlook the effect of climate to design and manage the respective logistic network efficiently.

Automated summary

This paper develops a two-stage stochastic program model for optimal location of charging stations and routing decisions of delivery services using electric vehicles, considering climate variability. A novel solution approach based on the progressive hedging algorithm is presented to provide high-quality solutions within reasonable running times. The proposed formulation and solution approach's performance is evaluated in Fargo city, North Dakota.