Title Robust Optimization Models for Vehicle Allocation in Electric Vehicle Sharing Systems Considering Uncertain Demand
Year 2019
Degree Master
School Department of Transportation and Logistics Management College of Management National Chiao Tung University
Author Hsiao-Tung Wang
Summary This study addresses the optimal allocation of a fleet of plug-in electric vehicles (EVs) to the stations of an EV-sharing system at the beginning of each day. The objective is to maximize the profit of the system operator. A multi-layer time-space network flow is adopted to describe the movement of EVs in the system. An optimal fleet allocation model for EV-sharing systems is then developed based on the multi-layer time-space network. This study applies robust optimization and chance constraint techniques to deal with this fleet allocation problem with uncertain and stochastic demands, respectively. While small-scale instances of the problem can be optimally solve using commercial software such as Gurobi, a network decomposition-based mathheuristic is developed to efficiently solve large-scale instances.
A set of computational experiments were conducted based on the data provided by the operator of the EV-sharing system deployed in the Sun-Moon Lake National Park in Nantou, Taiwan. The results show that the proposed model is able to effectively generate optimal fleet allocations under determinstic, uncertain an stochastic demand scenarios. Moreover, the model and the heuristic were tested on a larger problem instance extended from the above-mentioned EV-sharing system. The heuristic is able to obtain good quality solutions in a reasonabe amount of time. Sensitivity analyses of the impact of model parameters on solution performances were also conducted. Two measures of effectiveness, robust price and hedge value, were examined to verify the price-paid and value-gained by applying the robust solution. This study contributes to provide a decision support tool that faciliates operators of EV-sharing systems in effectively determing the deployment of their fleets to stations considering uncertain and stochastic demands.
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