Identifier,"Start Date / Time","End Date / Time",Title,Subtitle,Type,Description,Permalink,"Building Name",Room,"Location Name",Cost,Tags,Sponsors
65957-16676320,"2019-09-26 15:00:00","2019-09-26 17:00:00","Departmental Seminar (899): Shima Nassiri, University of Michigan","Reference Pricing for Healthcare Services","Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Reference Pricing for Healthcare Services
Abstract:
The traditional payment system between an insurer and providers does not incentivize providers to limit their prices nor patients to choose less expensive providers, hence contributing to high insurer expenditures. Reference pricing has been proposed as a way to better align incentives and control the rising costs of healthcare. In this payment system, the insurer determines the maximum amount that can be reimbursed for a procedure (reference price). If a patient selects a provider charging more than the reference price, the patient is responsible for the entire portion above it. We propose a model to analyze the reference pricing payment scheme. Our model incorporates an insurer who chooses the reference price, multiple competing price-setting providers, and heterogeneous patients who select a provider based on a multinomial logit choice model. Our goal is to understand how reference pricing compares with payment systems where patients pay a fixed or a variable amount. We find that the highest-priced providers under a fixed payment system cut their prices under reference pricing. Moreover, reference pricing often outperforms the fixed and the variable payment system both in terms of expected patient utility and insurer cost, unless the procedure cost is high in relation to the reference price (i.e., the reference price is low). The entire system also benefits from reference pricing despite a loss in provider profit due to lower prices. Furthermore, reference pricing with a variable portion below the reference price tends to perform worse than reference pricing with a fixed payment below the reference price.
Bio:
Dr. Shima Nassiri is an assistant professor of technology and operations at the University of Michigan Ross School of Business. Her research interests involve (a) designing coordination mechanisms in supply chain and its applications in healthcare and public health policy using game theory and optimization techniques, and (b) studying the behavioral aspects of healthcare operations using econometrics and data-driven methods. She is particularly interested in studying the healthcare policies that are aiming to reduce healthcare expenditure by moving towards performance-based care. She received her Ph.D. from the Foster School of Business at the University of Washington.",https://events.umich.edu/event/65957,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66534-16744983,"2019-10-03 15:00:00","2019-10-03 17:00:00","Departmental Seminar (899): Eunhye Song, Pennsylvania State University",,"Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title: Sequential Bayesian risk set estimation for robust simulation optimization under input model uncertainty
Abstract:
This talk discusses a new way of approaching a discrete simulation optimization
problem when the input distributions of the simulation model are estimated with error from real-world observations. This problem is known as ‘simulation optimization under input uncertainty’ and has been studied actively in recent years. Most approaches provide either asymptotic guarantee that the selected solution is the real-world optimum as the real-world sample size increases or find the optimum to an alternative formulation such as the distributionally robust optimum. This work focuses on finite-sample inference on the relative performances of the solutions while uncertainty about the input models are captured by their Bayesian posteriors. A user-specified smallest optimality gap of interest is reflected to control conservativeness of the procedure, so that two solutions whose expected performances are within is considered practically indistinguishable. The -level risk set of solution is defined as the set of solutions whose expected performance is practically better () than with significant probability () under the posterior on the input models. The size of the risk set shows robustness of solution; an empty risk set implies that there is no practically better solution than even with input uncertainty. For efficient estimation of the risk set, the expected performance is modeled
as a Gaussian process (GP) that takes a solution and a collection of input distributions generated from their posterior as inputs. A one-step look-ahead sampling rule is proposed to choose which solution-distributions pair to simulate in the next iteration to minimize the estimation error of the risk set.
Bio:
Eunhye Song is the Harold and Inge Marcus Early Career Assistant Professor in Industrial and Manufacturing Engineering at Penn State University. She earned her PhD degree in Industrial Engineering and Management Sciences at Northwestern University in 2017 and MS and BS in Industrial and Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST) in 2012 and 2010, respectively. Her research interests include design of simulation experiments, large-scale discrete simulation optimization, input uncertainty quantification, and simulation optimization in the presence of model risk. She has collaborated
with Simio, a leading discrete-event simulation software company, on developing a statistical tool to quantify input uncertainty for a Simio model, which is now a standard part of Simio’s software product. She also worked with General Motors’ R&D group on global sensitivity analysis of Vehicle Content Optimization simulator, which GM uses to find the optimal vehicle content portfolios of their major vehicle lines to maximize GM’s market share and profit. She is an active member of INFORMS Simulation Society (I-Sim) and currently serving on the I-Sim Diversity Committee chair.",https://events.umich.edu/event/66534,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
65962-16676323,"2019-10-10 15:00:00","2019-10-10 17:00:00","Departmental Seminar (899): Allen Holder, Rose-Hulman Institute of Technology — *Robust Analysis of Metabolic Pathways*","Robust Analysis of Metabolic Pathways","Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Robust Analysis of Metabolic Pathways
Abstract:
Flux balance analysis (FBA) is a widely adopted computational model in the study of whole cell metabolisms, often being used to identify drug targets, to study cancer, and to engineer cells for targeted purposes. The most widely used model is a linear program that maximizes cellular growth rate subject to achieving steady metabolic state and to satisfying environmental bounds. Quadratic and integer modifications are also common. Standard stoichiometry decides the preponderance of data in all instances, and hence, the majority of information defining an optimization model is certain. However, several key parts of a model rely on inferred science and are less certain; indeed, the method of deciding several of these values is opaque in the literature. This prompts the question of how the resulting science might depend on our lack of knowledge. We suggest a robust extension of FBA called Robust Analysis of Metabolic Pathways (RAMP) that accounts for uncertain information. We show that RAMP has several mathematical properties concomitant with our biological understanding, that RAMP performs like a relaxation of FBA in practice, and that RAMP requires special numerical awareness to solve.
Bio:
Allen Holder earned his PhD in applied mathematics from the University of Colorado at Denver in 1998. He has since studied applications of optimization in medicine, economics, production planning, analytics, and computational biology. He is currently a Professor of mathematics at the Rose-Hulman Institute of Technology, where he regularly directs some of the nation's best undergraduates through their first research experiences. He won the 2000 Pierskalla award for his work on the optimal design of radiotherapy treatments, and he won Rose-Hulman's Outstanding Scholar Award in 2015. He has held several editorial positions and has regularly served the INFORMS Health Applications Society and the INFORMS Computing Society, chairing the former in 2005 when it was a section. He recently co-authored a book titled ""An Introduction to Computational Science"" with his friend and colleague Dr. Joseph Eichholz. He is the proud father of two teenagers, and he fiddles with an old guitar in his spare time.",https://events.umich.edu/event/65962,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66535-16744984,"2019-10-17 15:00:00","2019-10-17 17:00:00","Departmental Seminar (899): Suvrajeet Sen, University of Southern California — *Stochastic Hierarchical Planning: A Win-Win Paradigm for Power System Operations*","Stochastic Hierarchical Planning: A Win-Win Paradigm for Power System Operations.","Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Stochastic Hierarchical Planning: A Win-Win Paradigm for Power System Operations
Abstract:
Driven by ambitious renewable portfolio standards, variable energy resources (such as wind and solar) are expected to impose unprecedented levels of uncertainty to power system operations. The current practice of planning operations with deterministic optimization tools may be ill-suited for a future where uncertainty is abundant. To overcome the reliability challenges associated with the large-scale inclusion of renewable resources, we present a stochastic hierarchical planning (SHP) framework. This framework captures operations at day-ahead, short-term and hour-ahead timescales, as well as the interactions between decisions and stochastic processes across these timescales. While stochastic counterparts of individual optimization problems (e.g., unit commitment, economic dispatch etc.) have been studied previously, this presentation is built around a comprehensive computational treatment of planning frameworks that are stitched together in a hierarchical setting. Computational experiments conducted with the NREL118 dataset reveal that, relative to its deterministic counterpart, the SHP framework significantly reduces unmet demand, and can lead to substantial savings in costs and greenhouse gas emissions. Such a ""Win-Win"" paradigm is only possible through new approaches which combine OR and Data Science through Stochastic Programming.
Joint work with S. Atakan (formerly USC, and currently at Amazon) and H. Gangammanavar (SMU).
Bio:
Suvrajeet Sen is Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California. Prior to joining USC, he was a Professor at Ohio State University (2006-2012), and University of Arizona (1982-2006). He has also served as the Program Director of OR as well as Service Enterprise Systems at the National Science Foundation. Professor Sen’s research is devoted to many categories of optimization models, and he has published over one hundred papers, with the vast majority of them dealing with models, algorithms and applications of Stochastic Programming problems. He has served on several editorial boards, including Operations Research as Area Editor for Optimization and as Associate Editor for INFORMS Journal on Computing, Journal of Telecommunications Systems, Mathematical Programming B, and Operations Research. He also serves as an Advisory Editor for several newer journals. Professor Sen was instrumental in founding the INFORMS Optimization Society in 1995, and recently served as its Chair (2015-16). Except for his years at NSF, he has received continuous extramural research funding from NSF and other basic research agencies, totaling over ten million dollars as PI over his career. He and his colleagues were jointly recognized by the INFORMS Computing Society for “seminal work” on Stochastic Mixed-Integer Programming. Professor Sen is also a Fellow of INFORMS.",https://events.umich.edu/event/66535,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66536-16744985,"2019-10-31 15:00:00","2019-10-31 17:00:00","Departmental Seminar (899): Santanu Dey, Georgia Tech — *Convexification of substructures in quadratically constrained quadratic program*","Convexification of substructures in quadratically constrained quadratic program","Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Convexification of substructures in quadratically constrained quadratic program
Abstract:
An important approach to solving non-convex quadratically constrained quadratic program (QCQP) to global optimality is to use convex relaxations and branch-and-bound algorithms. In our first result, we show that the exact convex hull of the solutions of a general quadratic equation intersected with any polytope is second-order cone representable. The proof is constructive and relies on the discovery of an interesting property of quadratic functions, which may be of independent interest: A set defined by a single quadratic equation is either (1) the boundary of a convex set, or (2) the boundary of union of two convex sets or (3) it has the property that through every point on the surface, there exists a straight line that is entirely contained in the surface. We next study sets defined for matrix variables that satisfy rank-1 constraint together with different choices of linear side constraints. We identify different conditions on the linear side constraints, under which the convex hull of the rank-1 set is polyhedral or second-order cone representable. Finally, we present results from comprehensive set of computational experiments and show that our convexification results together with discretization significantly help in improving dual bounds for the generalized pooling problem. (This is joint work with Asteroide Santana and Burak Kocuk.)
Bio:
Santanu S. Dey is A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Dey's research interests are in the area of non convex optimization, and in particular mixed integer linear and nonlinear programming. His research is partly motivated by applications of non convex optimization arising in areas such as electrical power engineering, process engineering, civil engineering, logistics, and statistics. Dr. Dey has served as the vice chair for Integer Programming for INFORMS Optimization Society (2011-2013) and has served on the program committees of Mixed Integer Programming Workshop 2013 and Integer Programming and Combinatorial Optimization 2017. He currently serves on the editorial board of Computational Optimization and Applications, MOS-SIAM book series on Optimization, is an area editor for Mathematical Programming C and is an associate editor for Mathematical Programming A, Mathematics of Operations Research and SIAM Journal on Optimization.",https://events.umich.edu/event/66536,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66425-16736298,"2019-11-07 15:00:00","2019-11-07 17:00:00","Departmental Seminar (899): Nicoleta Serban, Georgia Tech","Distributed Computational Methods For Healthcare Access Modeling","Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Distributed Computational Methods For Healthcare Access Modeling
Abstract:
The research presented in this seminar has been motivated by one of my research programs to bring rigor in measurement of and inference on healthcare access, with a recent book to be released, titled Healthcare System Access: Measurement, Inference and Intervention. I will begin with an overview of the underlying framework to assess healthcare access with a focus on health policy making. I will use this framework to motivate the access model, a classic assignment optimization but with many important computational challenges, including spatial dependence in the outcome measures, complex system constraints, large-scale decision space among other. I will present computationally efficient methods for addressing large-scale optimization problems accounting for spatial coupling in the context of uncertainty quantification.
Bio:
Nicoleta Serban is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. Dr. Serban's education and research trajectory makes her unique in the pursuit of data-driven discovery endeavors. While trained as a mathematician at the most prestigious university in Romania, she pursued a doctoral degree in Statistics at Carnegie Mellon University. Her doctoral research focused on fundamental statistical methods with application to genomics and protein structure determination. After graduation, she changed fields to take a tenure-track position in an engineering school at Georgia Institute of Technology. While at Georgia Tech, she has been engaged in engineering-focused research spanning multiple fields, including enterprise transformation, degradation modeling and monitoring, and healthcare among others. Her research record is quite diverse, from mathematical statistics to modeling to data analysis to qualitative insights on causality and complexity. Dr. Serban’s current research emphasis is on health analytics using massive data sets to inform policy making and targeted interventions. To date, she has published more than 60 journal articles, and a collaborative (with Dr. William B. Rouse) book titled Understanding and Managing the Complexity of Healthcare published by MIT Press. She is the Editor for physical sciences, engineering, and the environment for the Annals of Applied Statistics. She has reviewed for multiple funding agencies and she has served in multiple workshops and meetings organized by the National Academy of Engineering and National Academy of Medicine.",https://events.umich.edu/event/66425,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66537-16744986,"2019-11-14 15:00:00","2019-11-14 17:00:00","Departmental Seminar (899): Clive D’Souza, U-M IOE",,"Workshop / Seminar","The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
More details will be provided closer to the event's date.",https://events.umich.edu/event/66537,"Industrial and Operations Engineering Building",1680,"Industrial and Operations Engineering Building",,"899 Seminar Series","Industrial and Operations Engineering"
66539-16744990,"2019-11-21 15:00:00","2019-11-21 17:00:00","2019 Wilbert Steffy Distinguished Lecture: Ramayya Krishnan, Carnegie Mellon",,"Workshop / Seminar","The Wilbert Steffy Lectureship was established in 2003 to honor one of U-M Industrial and Operations Engineering's early distinguished faculty members, Wilbert Steffy, who retired in 1976, after 29 years of service within the College of Engineering.
This seminar is open to all. U-M IOE graduate students and faculty are especially encouraged to attend.
Title:
Network problems and model interpretability in Social Cyber Physical Systems",https://events.umich.edu/event/66539,"Lurie Robert H. Engin. Ctr","Johnson Rooms","Lurie Robert H. Engin. Ctr",,"899 Seminar Series","Industrial and Operations Engineering"