1174 Etcheverry Hall, 3:30 p.m. - 4:30 p.m.
Abstract: Most of the real-life problems involve uncertainty, which need to be delicately integrated into the decision-making processes. In this talk, we present various stochastic optimization techniques motivated by maintenance, operations and capacity expansion planning problems in energy systems. In the first part of the talk, our aim is to effectively model and solve the integrated condition-based maintenance and operations scheduling problem of a fleet of generators. We develop a data-driven optimization framework that explicitly considers the effect of the sensor-driven generator failure scenarios and operations schedules on the generators’ degradation levels to construct a reliable and cost-efficient plan. In the second part of the talk, we shift our focus to a more generic problem setting in sequential decision-making under uncertainty. Although two-stage and multi-stage stochastic programming are among the key methodologies to address multi-period problems under uncertainty, they might not provide adequate solutions under limited flexibility by resulting in either fully static or dynamic policies. We propose a novel adaptive stochastic programming approach, in which we optimize the time to revise decisions. We provide theoretical bounds on the performance of the proposed approach compared to the static and dynamic approaches, and present practical implications of the choice of the revision time. We also tailor solution algorithms using our analytical analyses and derive their approximation guarantees. To illustrate our results, we study a generation expansion planning problem demonstrating the advantages of the adaptive approach over existing policies.
Bio: Beste Basciftci is currently a PhD candidate in Operations Research at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, with a minor in Statistics. She received her bachelor's degrees in Industrial Engineering and Computer Engineering from Boğaziçi University with High Honors. She also hold a master's degree in Industrial Engineering from Boğaziçi University. She is broadly interested in data-driven decision making problems under uncertainty. Methodologically, her research focuses on developing mixed-integer, stochastic programming and distributionally robust optimization approaches to address operations research/management related problems, specifically for applications in energy, supply chains, production systems, and healthcare operations. Her research also involves developing and integrating statistical modeling and business analytics approaches to the subsequent decision-making processes.