Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Departmental Seminar (899): Robert Gramacy, Virginia Tech (September 12, 2019 3:00pm) https://events.umich.edu/event/65925 65925-16670253@events.umich.edu Event Begins: Thursday, September 12, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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:
Replication or exploration? Sequential design for stochastic simulation experiments

Abstract:
We investigate the merits of replication, and provide methods that search for optimal designs (including replicates), in the context of noisy computer simulation experiments. We first show that replication offers the potential to be beneficial from both design and computational perspectives, in the context of Gaussian process surrogate modeling. We then develop a lookahead based sequential design scheme that can determine if a new run should be at an existing input location (i.e., replicate) or at a new one (explore). When paired with a newly developed heteroskedastic Gaussian process model, our dynamic design scheme facilitates learning of signal and noise relationships which can vary throughout the input space. We show that it does so efficiently, on both computational and statistical grounds. In addition to illustrative synthetic examples, we demonstrate performance on two challenging real-data simulation experiments, from inventory management and epidemiology.

Bio:
Robert Gamacy is a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech). Previously, he was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. His research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.

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Workshop / Seminar Tue, 03 Sep 2019 16:36:19 -0400 2019-09-12T15:00:00-04:00 2019-09-12T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Robert Gramacy
Departmental Seminar (899): Lewis Ntaimo, Texas A&M University — *Stochastic Decomposition for Risk-Averse Multistage Stochastic Programming* (September 19, 2019 3:00pm) https://events.umich.edu/event/65937 65937-16676298@events.umich.edu Event Begins: Thursday, September 19, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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 Decomposition for Risk-Averse Multistage Stochastic Programming

Abstract:
Mean-risk multistage stochastic programming (MR-MSP) provides a framework for modeling sequential decision-making problems under uncertainty and risk. However, MR-MSP problems are difficult to solve due to their large-scale nature and the incorporation of risk measures in the objective. This work derives multistage stochastic decomposition (MSD) for solving large-scale MR-MSP instances with deviation and quantile risk measures. We show that risk-averse MSD converges asymptotically to an optimal solution and report on a computational study on the application of MSD to long-term hydrothermal scheduling. The study provides several insights into how optimal solutions vary across different risk levels as well as across different risk measures. In particular, the results reveal that conditional value at-risk exhibits desirable control over extreme scenarios than other risk measures.

Keywords:
Multistage stochastic programming, stochastic decomposition, interior sampling, mean-risk measures.

Bio:
Lewis Ntaimo is a Professor in the Department of Industrial and Systems Engineering at Texas A&M University and has been with the university since 2004. He obtained his Ph.D. in Systems and Industrial Engineering in 2004, his M.S. in Mining and Geological Engineering in 2000, and B.S. in Mining Engineering, all from the University of Arizona. Dr. Ntaimo’s research interests are in models and algorithms for large-scale stochastic optimization, systems modeling and process optimization, and computer simulation. Recent applications include patient and resource management in healthcare, wildfire response planning, aircraft assembly line production optimization, energy reduction in data centers, and wind farm operations and maintenance. His research has been funded by the National Science Foundation, Department of Homeland Security, and industry. Dr. Ntaimo is a member of INFORMS and IISE, and he is the 2018-2019 President of the INFORMS Minority Issues Forum. He currently serves as associate editor for INFORMS Journal on Computing, IISE Transactions, IISE Transactions on Healthcare Systems Engineering, and is on the Editorial Board of the Journal of Global Optimization.

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Workshop / Seminar Mon, 23 Sep 2019 14:42:07 -0400 2019-09-19T15:00:00-04:00 2019-09-19T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Photo of Lewis Ntaimo and IOE logo
Departmental Seminar (899): Shima Nassiri, University of Michigan (September 26, 2019 3:00pm) https://events.umich.edu/event/65957 65957-16676320@events.umich.edu Event Begins: Thursday, September 26, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Mon, 09 Sep 2019 13:45:21 -0400 2019-09-26T15:00:00-04:00 2019-09-26T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Photo of Shima Nassiri and IOE logo
Departmental Seminar (899): Eunhye Song, Pennsylvania State University (October 3, 2019 3:00pm) https://events.umich.edu/event/66534 66534-16744983@events.umich.edu Event Begins: Thursday, October 3, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Mon, 09 Sep 2019 13:50:51 -0400 2019-10-03T15:00:00-04:00 2019-10-03T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Eunhye Song, Pennsylvania State University
Departmental Seminar (899): Allen Holder, Rose-Hulman Institute of Technology — *Robust Analysis of Metabolic Pathways* (October 10, 2019 3:00pm) https://events.umich.edu/event/65962 65962-16676323@events.umich.edu Event Begins: Thursday, October 10, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Mon, 23 Sep 2019 14:46:11 -0400 2019-10-10T15:00:00-04:00 2019-10-10T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Allen Holder, Rose-Hulman Institute of Technology
Departmental Seminar (899): Suvrajeet Sen, University of Southern California — *Stochastic Hierarchical Planning: A Win-Win Paradigm for Power System Operations* (October 17, 2019 3:00pm) https://events.umich.edu/event/66535 66535-16744984@events.umich.edu Event Begins: Thursday, October 17, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Tue, 24 Sep 2019 12:16:17 -0400 2019-10-17T15:00:00-04:00 2019-10-17T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Suvrajeet Sen, University of Southern California — *Stochastic Hierarchical Planning: A Win-Win Paradigm for Power System Operations*
Departmental Seminar (899): Santanu Dey, Georgia Tech — *Convexification of substructures in quadratically constrained quadratic program* (October 31, 2019 3:00pm) https://events.umich.edu/event/66536 66536-16744985@events.umich.edu Event Begins: Thursday, October 31, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Tue, 08 Oct 2019 13:07:37 -0400 2019-10-31T15:00:00-04:00 2019-10-31T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Santanu Dey, Georgia Tech
Departmental Seminar (899): Nicoleta Serban, Georgia Tech (November 7, 2019 3:00pm) https://events.umich.edu/event/66425 66425-16736298@events.umich.edu Event Begins: Thursday, November 7, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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.

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Workshop / Seminar Tue, 29 Oct 2019 16:43:56 -0400 2019-11-07T15:00:00-05:00 2019-11-07T17:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Nicoleta Serban, Georgia Tech
*CANCELED* Departmental Seminar (899): Clive D’Souza, U-M IOE (November 14, 2019 3:00pm) https://events.umich.edu/event/66537 66537-16744986@events.umich.edu Event Begins: Thursday, November 14, 2019 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

THIS EVENT HAS BEEN CANCELED.

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Workshop / Seminar Mon, 11 Nov 2019 08:37:46 -0500 2019-11-14T15:00:00-05:00 2019-11-14T17:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899): Clive D’Souza, U-M IOE
2019 Wilbert Steffy Distinguished Lecture: Ramayya Krishnan, Carnegie Mellon University (November 21, 2019 3:00pm) https://events.umich.edu/event/66539 66539-16744990@events.umich.edu Event Begins: Thursday, November 21, 2019 3:00pm
Location: Lurie Robert H. Engin. Ctr
Organized By: U-M Industrial & Operations Engineering

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

Bio:
A faculty member at CMU since 1988, Krishnan was appointed Dean when the Heinz School of Public Policy and Management became the Heinz College of Information Systems and Public Policy in 2008. He was reappointed upon the completion of his first term as Dean in 2014.

Krishnan was educated at the Indian Institute of Technology and the University of Texas at Austin. He has a bachelor’s degree in mechanical engineering, a master’s degree in industrial engineering and operations research, and a PhD in management science and information systems. Krishnan’s research interests focus on consumer and social behavior in digitally instrumented environments. His work has addressed technical, policy, and business problems that arise in these contexts and he has published extensively on these topics. He has served as Department Editor for Information Systems at Management Science, the premier journal of the Operations Research and Management Science Community. Krishnan is current (2019) President of INFORMS and an INFORMS Fellow, and was formerly a member of the Global Agenda Council on Data Driven Development of the World Economic Forum, and president of the INFORMS Information Systems Society as well as the INFORMS Computing Society. He is the recipient of the prestigious Y. Nayuduamma award in 2015 for his contributions to telecommunications management and business technology, the Distinguished Alumnus award from the Indian Institute of Technology (Madras), the Distinguished PhD Alumnus award from the University of Texas, and the Bright Internet Award (Jae Kyu Lee Award) from the Korea Society of Management Information Systems.

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Workshop / Seminar Wed, 06 Nov 2019 13:31:46 -0500 2019-11-21T15:00:00-05:00 2019-11-21T17:00:00-05:00 Lurie Robert H. Engin. Ctr U-M Industrial & Operations Engineering Workshop / Seminar Photo of Ramayya Krishnan
*CANCELED* Departmental Seminar (899): Ramamoorthi Ravi, Carnegie Mellon University (February 27, 2020 3:00pm) https://events.umich.edu/event/72001 72001-17914110@events.umich.edu Event Begins: Thursday, February 27, 2020 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

THIS EVENT HAS BEEN CANCELED.
SEE LINK ABOVE FOR REPLACEMENT EVENT.

Title:
Models and Methods for Omni-channel Fulfillment

Abstract:
Omni-channel retailing, the combination of online and traditional store channels, has led to the use of traditional stores as fulfillment centers for online orders. A key aspect of omni-channel fulfillment problems is the trade-off between cancellations of accepted online orders and profits; a riskier fulfillment policy may result in more online sales but also more cancelled orders.

In this talk, I will describe a stochastic model of the process leading to order cancellations for a single item so that retailers may find fulfillment policies that effectively use this information along with shipping costs between various locations. We describe iterative algorithms based on Infinitesimal Perturbation Analysis (IPA) that converge to optimal and locally optimal policies within certain flexible policy classes for the multiple-location version of this model, and show their empirical performance on simulations based on real data from a high-end North American retailer.

Time permitting, I will describe a related problem of maximizing revenues subject to a constraint on cancellations across a large portfolio of items and an approach to solving it. This talk is based on the dissertation of Jeremy Karp at CMU describing work carried out jointly with Prof. Sridhar Tayur (CMU) and Dr. Srinath Sridhar (Onera Inc).

Bio:
Dr. R. Ravi is Andris A. Zoltners Professor of Business, and Professor of Operations Research and Computer Science at Carnegie Mellon University.

Ravi received his bachelor's degree from IIT, Madras, and Master's and doctoral degrees from Brown University, all in Computer Science.

Ravi's research focuses on models, methods and applications of discrete optimization, and their applications in the intersection of business and technology. He served as area editor for "Operations Research" in charge of the discrete optimization area between 2012-2017. He has formerly served as associate editor in the ACM Transactions on Algorithms, Management Science, Networks and Journal of Algorithms. He has also served on several international program committees including as the program chair for the 2008 IEEE Foundations of Computer Science (FOCS) conference. Ravi has co-authored two books, over 130 publications, and has a h-index of over 50. His research has been supported by the U.S. National Science Foundation, Office of Naval Research, Microsoft, Yahoo and Google; He has so far supervised 18 doctoral theses and developed over half a dozen new graduate classes.

Ravi has been at the Tepper School of Business since 1995 where he served as the Associate Dean for Intellectual Strategy from 2005-2008. He was Chair of the Future Educational Delivery Committee that launched the online hybrid Tepper MBA in 2013. He was elected a Fellow of the INFORMS in 2017.

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Workshop / Seminar Mon, 24 Feb 2020 12:03:22 -0500 2020-02-27T15:00:00-05:00 2020-02-27T17:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Ramamoorthi Ravi
Departmental Seminar (899): Jon Lee, University of Michigan (February 27, 2020 3:00pm) https://events.umich.edu/event/73226 73226-18179646@events.umich.edu Event Begins: Thursday, February 27, 2020 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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:
Sparse Generalized Inverses

Abstract:
Generalized inverses are ubiquitous in matrix algebra and its applications, in particular in statistics. The most commonly-used generalized inverse is the well-known and celebrated Moore-Penrose pseudo-inverse. But not all Moore-Penrose properties are needed to ensure that a generalized inverse solves key problems, like least squares. So there is the opportunity to find sparser generalized inverses that do the jobs. The usual approach of exact 1-norm minimization to induce sparsity has flaws here, so we will look at an alternative approach overcoming the flaws. I will present theoretical and computational results on this, in particular approximation algorithms with nice properties. Based on joint works with: Marcia Fampa (Universidade Federal do Rio de Janeiro), Luze Xu (UM), and Gabriel Ponte (Universidade Federal do Rio de Janeiro).

Bio:
Jon’s research focus is on nonlinear discrete optimization (NDO). Many practical engineering problems have physical aspects which are naturally modeled through smooth nonlinear functions, as well as design aspects which are often modeled with discrete variables. Research in NDO seeks to marry diverse techniques from classical areas of optimization, for example methods for smooth nonlinear optimization and methods for integer linear programming, with the idea of successfully attacking natural NDO models for practical engineering problems.

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Workshop / Seminar Mon, 24 Feb 2020 11:57:02 -0500 2020-02-27T15:00:00-05:00 2020-02-27T17:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Jon Lee
Departmental Seminar (899): Joseph B. Lyons, United States Air Force Academy (March 12, 2020 3:00pm) https://events.umich.edu/event/72002 72002-17914113@events.umich.edu Event Begins: Thursday, March 12, 2020 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

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:
Trust Considerations of Advanced Technology

Abstract:
This talk will dig into issues of trust and transparency as they relate to intelligent machines. The construct of trust will be defined, and prior research involving trust and transparency will be explored. As machines gain capabilities and authorization to act on behalf of humans, humans will begin to attribute greater intentionality to their actions - but what impact does this have on human attitudes toward intelligent machines? This talk will explore the dimensions of human-autonomy teaming and will discuss the need for transparency of intent.

Bio:
Joseph B. Lyons is the Lead for the Collaborative Interfaces and Teaming Core Research Area within the 711 Human Performance Wing at Wright-Patterson AFB, OH. Dr. Lyons received his PhD in Industrial/Organizational Psychology from Wright State University in Dayton, OH, in 2005. Some of Dr. Lyons’ research interests include human-machine trust, interpersonal trust, human factors, and influence. Dr. Lyons has worked for the Air Force Research Laboratory as a civilian researcher since 2005, and between 2011-2013 he served as the Program Officer at the Air Force Office of Scientific Research where he created a basic research portfolio to study both interpersonal and human-machine trust as well as social influence. Dr. Lyons has published in a variety of peer-reviewed journals, and is an Associate Editor for the journal Military Psychology. Dr. Lyons is a Fellow of the American Psychological Association and the Society for Military Psychologists.

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Workshop / Seminar Mon, 17 Feb 2020 14:30:33 -0500 2020-03-12T15:00:00-04:00 2020-03-12T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar Departmental Seminar (899)
*CANCELED* Departmental Seminar (899): Kayse Maass, Northeastern University (March 19, 2020 3:00pm) https://events.umich.edu/event/72005 72005-17914114@events.umich.edu Event Begins: Thursday, March 19, 2020 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

THIS EVENT HAS BEEN CANCELED.

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:
An Industrial and Operations Engineering Approach to Disrupting Human Trafficking

Abstract:
Human trafficking is a prevalent and malicious global human rights issue, with an estimated 24 million victims currently being exploited worldwide. A major challenge to its disruption is the fact that human trafficking is a complex system interwoven with other illegal and legal networks, both cyber and physical. Efforts to disrupt human trafficking must understand these complexities and the ways in which a disruption to one portion of the network affects other network components. As such, industrial and systems engineering models are uniquely positioned to address the challenges facing anti-human trafficking efforts. This presentation will discuss ongoing interdisciplinary anti-human trafficking efforts focusing on prevention, network disruption, and survivor empowerment. Specifically, we will discuss 1) the adaptions to current network interdiction models that are necessary for adequately representing human trafficking contexts and 2) a budget-constrained optimization model that maximizes the societal value of locating additional shelters for human trafficking survivors.

Bio:
Kayse Lee Maass is an Assistant Professor in the Department of Mechanical and Industrial Engineering and leads the Operations Research and Social Justice lab at Northeastern University. She also currently holds a research appointment with the Information and Decision Engineering Program at Mayo Clinic and is the co-founder of the Trafficking Research for Action Collaborative. Dr. Maass’s research focuses on the application of operations research methodology to social justice, access, and equity issues within human trafficking, mental health, housing, and supply chain contexts. Her work is supported by multiple National Science Foundation grants, centers interdisciplinary survivor-informed expertise, and has been used to inform policy and operational decisions at the local, national, and international levels.

Dr. Maass earned a PhD in Industrial and Operations Engineering (IOE) from the University of Michigan and completed her postdoctoral studies in the Department of Health Sciences Research at the Mayo Clinic. She is a recipient of multiple awards, including: the INFORMS Judith Liebman Award, NSF Graduate Research Fellowship Program Award, and the INFORMS Section on Location Analysis Dissertation Award-Runner Up. She was also named a ‘Rising Star’ among INFORMS’ Powerful, Pragmatic Pioneers. Dr. Maass currently serves on the INFORMS Subdivision Council, as INFORMS Section on Location Analysis Secretary, and is a member of the H.E.A.L. Trafficking Research Committee.

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Workshop / Seminar Fri, 13 Mar 2020 14:02:09 -0400 2020-03-19T15:00:00-04:00 2020-03-19T17:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Workshop / Seminar "Canceled" text
Departmental Seminar (899): Siddhartha Banerjee, Cornell University (March 26, 2020 3:00pm) https://events.umich.edu/event/72006 72006-17914115@events.umich.edu Event Begins: Thursday, March 26, 2020 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Attend remotely via BlueJeans:
Online: https://bluejeans.com/959596695
Dial-in: 1.408.614.7898 (US or Canada only) and enter the meeting ID 959596695

Sign up to meet with the speaker (IOE graduate students only):
http://ioe2.engin.umich.edu/class/ioe899/signup.php?spkr_id=417

Title:
Constant Regret Algorithms for Online Decision-Making

Abstract:
I will present a simple algorithm that achieves constant regret in many widely studied online decision-making problems, including online resource-allocation and pricing, generalized assignment, and online bin packing. In particular, I will consider a general class of finite-horizon control problems, where we see a stream of stochastic arrivals from some known distribution, and need to select actions, with the final objective depending only on the aggregate type-action counts. For such settings, I will introduce a unified algorithmic paradigm, and provide a simple, yet general, condition under which these algorithms achieve constant regret, i.e., additive loss compared to the hindsight optimal solution which is independent of the horizon and state-space. The results stem from an elementary sample-path coupling argument, which may prove useful for a larger class of problems in online decision-making. Time permitting, I will illustrate this by showing how we can use this technique to obtain simple data-driven implementations of the above algorithms, which achieve constant regret with as little as a single data trace.

Bio:
Sid Banerjee is an Assistant Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell, as well as a field member in the CS and ECE Departments and the Center for Applied Mathematics. His research is on stochastic modeling and control, and the design of algorithms and incentives for large-scale systems. He got his PhD in ECE from UT Austin, and worked as a postdoctoral researcher in the Social Algorithms Lab at Stanford, as well as a technical consultant at Lyft. His work is supported by an NSF CAREER award, and grants from the NSF and ARL.

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Livestream / Virtual Thu, 26 Mar 2020 13:19:49 -0400 2020-03-26T15:00:00-04:00 2020-03-26T17:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Livestream / Virtual Departmental Seminar (899)
Departmental Seminar (899): Malcolm Miranda, University of Michigan (April 2, 2020 3:00pm) https://events.umich.edu/event/72008 72008-17914117@events.umich.edu Event Begins: Thursday, April 2, 2020 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

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 virtual social hour that is open to anyone in the IOE community.

Title:
Intro to The Great Lakes HPC Cluster

Abstract:
The talk will cover how to get started with Great Lakes. The first topic is Lmod, which allow specific software version to be added to the user's environment. Next, the speaker will show how to look up resources accounts and state of the cluster and then end with Slurm job submission. Assuming a person already understands the basics of the Linux command prompt, this will teach a person the basics of using a cluster running Lmod & Slurm.

Bio:
Malcolm Miranda is a Senior Research Computing Consultant from CAEN, the information technology (IT) services department for the University of Michigan (U-M) College of Engineering.

Attend virtually via BlueJeans:
To join the meeting on a computer or mobile phone: https://bluejeans.com/794562171

Just want to dial in?

1.) Dial:
1.408.614.7898 (US or Canada only)
+1.312.216.0325
International Callers (http://bluejeans.com/numbers)
2.) Enter the Meeting ID: 794562171

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Livestream / Virtual Mon, 30 Mar 2020 13:24:45 -0400 2020-04-02T15:00:00-04:00 2020-04-02T17:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Livestream / Virtual "Seminar" text and IOE logo
Departmental Seminar (899): Cong Shi, U-M IOE (April 9, 2020 3:00pm) https://events.umich.edu/event/74123 74123-18541331@events.umich.edu Event Begins: Thursday, April 9, 2020 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title:
Network Revenue Management with Online Inverse Batch Gradient Descent Method

Abstract:
We consider a general class of price-based network revenue management problems that a firm aims to maximize revenue from multiple products produced with multiple types of resources endowed with limited inventory over a finite selling season. A salient feature of our problem is that the firm does not know the underlying demand function that maps prices to demand rate, which must be learned from sales data. It is well known that for almost all classes of demand functions, the revenue rate function is not concave in the products' prices but is concave in products' market shares (or price-controlled demand rates). This creates challenges in adopting any stochastic gradient descent based methods in the price space. We propose a novel nonparametric learning algorithm termed online inverse batch gradient descent (IGD) algorithm. For the large scale systems wherein all resources' inventories and the length of the horizon are proportionally scaled by a parameter $k$, we establish a dimension-independent regret bound of $O( k^{4/5} \log k)$. This result is independent of the number of products and resources and works for a continuum action-set prices and the demand functions that are only once differentiable. Our result guarantees the efficacy of both algorithms in the high dimensional systems where the number of products or resources is large and the prices are continuous. (This is a joint work with Dr. Yiwei Chen.)

Bio:
Cong Shi is an associate professor in the Department of Industrial and Operations Engineering at the University of Michigan at Ann Arbor. His main research interests include supply chain management, revenue management, and service operations. He has won the first place in the INFORMS George Nicholson Student Paper Competition, the third place in the INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, and the finalist for the MSOM Data Driven Challenge. He received his Ph.D. in Operations Research from MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.

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Workshop / Seminar Mon, 06 Apr 2020 13:32:28 -0400 2020-04-09T15:00:00-04:00 2020-04-09T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Cong Shi