【Titles】
1. Designing and Optimizing an Integrated Car-and-ride Sharing System for Mobilizing Underserved Populations
2. Distributionally Robust Expectation using Dominance Information
【Speakers】
1. Dr. Siqian Shen, Associate Professor, Industrial and Operations Engineering, University of Michigan
2. Dr. Ruiwei Jiang, Assistant Professor, Industrial and Operations Engineering, University of Michigan.
【Host】Dr. Fang He
【Time】14:00-15:30, May 24th (Thursday), 2018
【Venue】Room N512, Shunde Building
【Abstract】
1. The fast-growing carsharing and ridesharing businesses are generating economic benefits and societal impacts in the modern society. However, they possess limitations to cover diverse populations and areas. In this paper, we consider an integrated car-and-ride sharing system and optimize its operations to improve the mobility of underserved populations under transportation scarcity. We consider two types of demands: Type 1 drivers who rent shared cars and Type 2 users who need rides from Type 1 drivers. We propose a two-phase model to maximize demand coverage. In Phase I, we match Type 1 drivers and Type 2 users based on a spatial-temporal network; in Phase II, we optimize pick-up and delivery routes and schedules for matched Type 1 and Type 2 users under random driving time via solving a two-stage stochastic mixed-integer programming model. We minimize the total travel cost plus expected penalty cost of users' waiting time and system overtime, and develop a decomposition algorithm for improving computational efficiency. We conduct computational studies on various instances using census data of underserved populations in Washtenaw County, Michigan. Our results show high demand fulfillment rates and effective matching and scheduling with low risk of waiting and overtime. The integrated system also achieves better performance if we allow vehicle relocation.
2. This talk discusses the expectation of a random function when the distributional information of the uncertain parameters consists of moment (e.g., mean, covariance, support) and probabilistic dominance information. We find that the expectation in this setting can be bounded using conic programming. Finally, we shall demonstrate the theoretical results via case studies on appointment scheduling.
 
【Short Bio】
1. Siqian Shen is an Associate Professor of Industrial and Operations Engineering at the University of Michigan and also serves as an Associate Director in the Michigan Institute for Computational Discovery & Engineering (MICDE). She obtained a B.S. degree from Tsinghua University in 2007 and Ph.D. from the University of Florida in 2011. Her theoretical research interests are in integer programming, stochastic/robust optimization, and network optimization. Applications include optimization and risk analysis of energy, healthcare, cloud-computing, and transportation systems. Her work has been supported by the National Science Foundation, Army Research Office, Department of Energy, DiDi ChuXing, IBM, and P&G. She has received INFORMS Computing Society Best Student Paper award (runner-up), IIE Pritsker Doctoral Dissertation Award (1st Place), IBM Smarter Planet Innovation Faculty Award, and Department of Energy (DoE) Early Career Award. 
2. Ruiwei Jiang is an Assistant Professor of Industrial and Operations Engineering in the University of Michigan at Ann Arbor. His research interests include stochastic optimization and integer programming. Application areas of his work include power and water systems, healthcare, and transportation systems. Recognition of his research includes the Stochastic Programming Society student paper award, the INFORMS George E. Nicholson student paper award, and the INFORMS Junior Faculty Interest Group paper award (honorable mention).