UC Berkeley

This thesis presents models and algorithms that leverage data analytics and optimization approaches to address the challenges in traditional manufacturing systems as well as emerging smart city contexts. The increasing availability of data sources and computing power has created the need for smarter data-driven decision making models, which motivates us to explore various ways of integrating statistics and optimization tools. In particular, we focus on analyzing practical operational problems with real-world data sets. Chapter 2 is devoted to improving yield prediction accuracy in integrated circuit manufacturing using the wafer map data. We propose an innovative yield prediction model, called adjacency-clustering, to capture the neighborhood effect that is often present in the wafer map. The adjacency-clustering model can be solved efficiently and deliver superior prediction performance than the state-of-the-art methods. Chapter 3 studies the order assignment problem in urban last-mile delivery services. We propose a framework to integrate travel time predictors with assignment optimization models to capture the drivers' practical routing behaviors. The proposed framework yields tractable formulations compatible with the existing stochastic and robust optimization tools. We further develop a branch-and-price algorithm to facilitate its real-time application. The real-world case study demonstrates the substantial benefits of applying our framework. Chapter 4 addresses the urban bike lane planning problem based on the real-world GPS trajectory data. We present a flexible optimization model based on the cyclists' utility functions. We analyze the problem structure and propose efficient algorithms to solve the bike lane planning model. We perform extensive numerical experiments on a real-world trajectory data set to validate the performance of our models and algorithms. The numerical study also generates managerial insights to help the city managers improve their bike lane planning decisions.