IE Decision Systems Engineering Seminar: Incentivized Self-Rebalancing Fleet in Electric Vehicle Sharing, April 2

With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this paper, Wang and his team investigate a dynamic fleet management problem for an electric vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. Wang’s team develops a new value function approximation (VFA) with input convex neural networks (ICNNs) to generate high-quality solutions. Through a New York City case study, they compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid. 

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