Student： Yi, Julian
Thesis Advisor：Professor Hung, Ying-Chao
Thesis Topic：Utilizing Asymmetric Dissimilarity Measures for Optimizing Electric Vehicle Charging Operations and Other Applications
Abstract：
In this research, we consider a stochastic electric vehicle (EV) charging system with random demand locations and arrival times. The objective is to determine the optimal locations for charging stations and the corresponding EV charging station routing policy to minimize the mean travel time or distance for charging demands. By considering a location-based EV charging station routing policy and utilizing real traffic information from Google Maps, we formulate this as an asymmetric clustering problem aimed at minimizing the sum of dissimilarities from data points to their respective cluster centers. This model provides a data-driven approach that not only enables the incorporation of various operational concerns but also can be applied to other similar real-world applications. Two novel asymmetric clustering algorithms are developed to address the problem, illustrated using several real-world scenarios. However, the robustness testing on synthetic asymmetric data reveals that while one algorithm demonstrates strong performance, the other exhibits limitations
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