The Speakers

In this talk, we present new perspectives on distributionally robust optimization (DRO) by considering multimodal ambiguity sets and decision-dependent uncertainties with models, solution algorithms, and applications. We first provide a two-stage DRO model with multimodal uncertainty, where both the mode probabilities and uncertainty distributions could be affected by the first-stage decisions. To address this setting, we propose a generic framework by introducing a phi-divergence based ambiguity set to characterize the decision-dependent mode probabilities and consider both moment-based and Wasserstein distance-based ambiguity sets to characterize the uncertainty distribution under each mode. We identify two special phi-divergence examples (variation distance and χ2-distance) and provide specific forms of decision dependence relationships under which we can derive tractable reformulations. Furthermore, we investigate the benefits of considering multimodality in a DRO model compared to a single-modal counterpart through an analytical analysis. Additionally, we develop a separation-based decomposition algorithm to solve the resulting DRO models with finite convergence and optimality guarantee under certain settings. We provide a detailed computational study over two example problem settings, facility location problem and shipment planning problem with pricing, to illustrate our results, which demonstrate that omission of multimodality or decision-dependent uncertainties within DRO frameworks result in inadequately performing solutions with worse in-sample and out-of-sample performances under various settings. We further demonstrate the speed-ups obtained by the solution algorithm against the off-the-shelf solver over various instances. Additionally, we present another application of DRO under decision-dependent uncertainty through a capacity expansion planning problem considering the chicken-and-egg dilemma under uncertain market conditions. We derive tractable reformulations of this problem under certain settings and develop a tailored algorithm based on the column-and-constraint generation approach providing computationally efficient solution approaches. 

Beste Basciftci is an Assistant Professor at the Department of Business Analytics at the Tippie College of Business at the University of Iowa. She is broadly interested in data-driven decision-making problems under uncertainty by developing mixed-integer/discrete optimization, stochastic programming, and distributionally robust optimization approaches to address methodological and computational challenges arising in operations research and management related problems. Main application areas of her research include energy systems and sustainability, supply chains and facility location problems, and emerging transportation and sharing systems. She obtained her PhD in operations research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology and obtained bachelor's degrees in industrial engineering and computer engineering (double major) from Bogazici University with High Honors. She has received various recognitions for her research including INFORMS ENRE (Energy, Natural Resources and the Environment Section) Early Career Best Paper Award Runner-up, IISE Transactions Best Paper Award in Focus Issue of Operations Engineering & Analytics, Tippie College of Business Social Impact Research Award, University of Iowa Early Career Scholar Award, and Georgia Tech ISyE Alice and John Jarvis Research Award. Her research is supported by the National Science Foundation and is honored to be elected to the Board of the INFORMS Computing Society and to the Committee on Stochastic Programming of the Mathematical Optimization Society.