Electric vehicles fast charger location-routing problem under ambient temperature
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% Doc_abstract.tex 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 EV can travel. EV 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 research develops deterministic and two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use EVs while considering the variability inherent in climate and customer demand. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a tested. For the first chapter, we formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC’s in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations. For the second chapter, 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. We observe that the location-routing decisions are susceptible to the EV logistic's underlying climate, signifying that decision-makers of the DCFC EV 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.