CAEE Seminar by Xin Wang: Smart Pricing and Operation in Electric Vehicle Sharing

Abstract: A smart city needs smart decisions. One of the critical challenges to achieving such smartness is to enable quick decisions under a fast-changing, stochastic environment, especially within the field of transportation. With the rising need for green, efficient, and flexible urban transportation, electric vehicle sharing services promise to be the new star of next-generation transportation. However, the adoption of electric vehicle sharing is not smooth due to the complexity of its high-dimensional time-space-energy network structure and various uncertainties. Aiming at conquering the complex nature of electric vehicle sharing systems, we present a new framework to embrace the deep neural network and the Markov decision process. Under this framework, a time-varying stochastic environment can be easily incorporated to make smart system decisions, such as considering the stochastic customer behavior under dynamic service price to self-balance the fleet. This framework is also flexible to be integrated with multiple systems beyond transportation, such as handling the interaction between the fleet and the power grid.

Biography: Xin Wang is an assistant professor in the Department of Industrial and Systems Engineering, affiliated with the Department of Civil and Environmental Engineering, at the University of Wisconsin-Madison. Wang’s research expertise lies in developing mathematical models and solution methods for sustainable and resilient logistics systems, interconnected systems of systems, vehicle sharing for urban mobility, supply chain management, and traffic flow modeling and analysis. He has been applying his research on many pressing transportation challenges where uncertainties and competitions are critical, such as sustainable logistics system planning, electric vehicle sharing, smart parking, and integration of electric vehicle and power grid system. His work has been supported by National Science Foundation and Industry Collaborators.

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