I2SDS Seminars: Spring 2012

Analysis of Multi-server Ticket Queues with Customer Abandonment

Speaker: Kaan Kuzu, Sheldon B Lubar School of Business, University of Wisconsin-Milwaukee


“Ticket Queues” are the new generation of queuing systems that issue tickets to the customers upon their arrival. The ticket queues differ from the physical queues in terms of the amount of information available to the customers upon their arrival. This study aims at analyzing the system performance of the multi-server ticket queues with reneging and balking customers, who periodically observe their position in the queue and reevaluate their decisions on whether to abandon the system or not. We model the ticket queues using a Markov chain model, and develop two accurate and effective approximation heuristics. These valuation tools enable us provide a method to analyze abandonment probabilities in real systems. Using our analytical model, we analyze the ticket queue data set of a bank to propose a method for separation of customers’ reneging and balking probability.

Friday, May 11, 11:30 AM – 12:30 PM

Location: Duques 652 (2201 G Street, NW)

Subjective Probability: Its Axioms and Acrobatics

Speaker: Nozer D. Singpurwalla, Professor of Statistics and Decision Sciences, The George Washington University


The meaning of probability has been enigmatic, even to the likes of Kolmogorov, and continues to be so. It is fallacious to claim that the law of large numbers provides a definitive interpretation. Whereas the founding fathers, Kardano, Pascal, Fermat, Bernoulli, de Moivre, Bayes, and Laplace, took probability for granted, the latter day writers, Venn, von Mises, Ramsey, Keynes, deFinetti, and Borel engaged in philosophical and rhetorical discussions about the meaning of probability. Entering into the arena were also physicists like Cox, Jeffreys, and Jaynes and philosophers like Carnap, Jeffrey, and Popper. Interpretation matters because the paradigm used to process information and act upon it, is determined by perspective. The modern view is that the only philosophically and logically defensible interpretation of probability is that probability is not unique, that it is personal, and therefore subjective. But to make subjective probability mathematically viable, one needs axioms of consistent behavior. The Kolmogorov axioms are a consequence of the behavioristic axioms. In this expository talk, I will review these more fundamental axioms and point out some of the underlying acrobatics that have led to debates and discussions. Besides mathematicians, statisticians, and decision theorists, the material here should be of interest to physical, biological, and social scientists, risk analysts, and those engaged in the art of “intelligence” (Googling, code breaking, hacking, and eavesdropping)..

Friday, April 27, 3:00 PM – 4:00 PM (followed by a wine and cheese reception)

Location: Duques 651(2201 G Street, NW)

Information about Dependence in the Absence and Presence of a Probable Cause

Speaker: Ehsan S. Soofi, Sheldon B Lubar School of Business, University of Wisconsin-Milwaukee


In general, dependence is more complicated than that could be measured by the traditional indices such as the correlation coefficients, its nonparametric counterparts, and the fraction of variance reduction. An information measure of dependence, known as the mutual information, is increasingly being used in the traditional as well as more modern problems. The mutual information, denoted here as M, measures departure of a joint distribution from the independent model. We also view M as an expected utility of variables for prediction. This view integrates ideas from the general dependence literature and the Bayesian perspectives. We illustrate the success of this index as a “common metric” for comparing the strengths of dependence within and between families of distributions in contrast with the failures of the popular traditional indices.For the location-scale family of distributions, an additive decomposition of M gives the normal distribution as the unique minimal dependence model in the family. An implication for practice is that the popular association indices underestimate the dependence of elliptical distributions, severely for models such as t-distributions with low degrees of freedom. A useful formula for M of the convolution of random variables provides a measure of dependence when the predictors and the error term are normally distributed jointly or individually, as well as under other distributional assumptions. Finally, we draw attention to a caveat: M is not applicable to continuous variables when their joint distribution is singular, due to a “probable cause” for the dependence. For an indirect application of M to singular models, we propose a modification of the mutual information index, which retains the important properties of the original index and show some potential applications.

Friday, April 27, 11:30 AM – 12:30 PM

Location: Funger 320 (2201 G Street, NW)

Optimal Stopping Problem for Stochastic Differential Equations with Random Coefficients

Speaker: Mou-Hsiung (Harry) Chang, Mathematical Sciences Division, U.S. Army Research Office


This talk is based on the paper “Optimal Stopping Problem for Stochastic Differential Equations with Random Coefficients”, Mou-Hsiung Chang, Tao Pang, and Jiongmin Yong, SIAM J. Control & Optimization, vol. 48, No. 2, pp. 941-971, 2009. The paper received the 2011 SIAM Control and Systems Activity Group best paper award. In this talk we consider an optimal stopping problem for stochastic differential equations with random coefficients. The dynamic programming principle leads to a Hamilton-Jacobi-Bellman equation, which, for the current case, is a backward stochastic partial differential variational inequality (BSPDVI, for short) for the value function. Well-posedness of such a BSPDVI is established, and a verification theorem is proved.

Friday, April 13, 4:00 PM – 5:00 PM

Location: Duques 553 (2201 G Street, NW)

Fallacies of Certainty in Operational Decision Models

Speaker: Dr. Suvrajeet Sen, Professor in Integrated Systems Engineering, Ohio State University


For most practitioners in government and industry, uncertainty is a fact of life. Yet, decision aids for many operational questions set aside uncertainty because they are supposedly difficult to either model, or solve, or both. Drawing upon several industrial applications (network planning, inventory control etc.) we will demonstrate that the state-of-the art for including uncertainty in decision models has come a long way. We will present the case that the boom in business analytics, coupled with algorithmic advances in stochastic programming provide unique opportunity for models that provide better support for operational decisions under uncertainty.

Short Biography:

Suvrajeet Sen is Professor of Integrated Systems Engineering at The Ohio State University (OSU). Until recently, he was also the Director of the Center for Energy, Sustainability, and the Environment at OSU. Prior to joining OSU, he was a Professor at the University of Arizona, and also served as a program director at NSF where he was responsible for the Operations Research, and the Service Enterprise Engineering programs. Starting in August 2012, he will assume a position on the faculty at the University of Southern California. Professor Sen is a Fellow of INFORMS. He has served on the editorial board of several journals, including Operations Research as Area Editor for Optimization, and as Associate Editor for INFORMS Journal on Computing, and Journal of Telecommunications Systems. Professor Sen founded the INFORMS Optimization Section.

Friday, March 30, 11:00 AM – 12:00 PM

Location: Funger 620 (2201 G Street, NW)

Managing Opportunistic Supplier Product Adulteration: Deferred Payments, Inspection, and Combined Mechanisms

Speaker: Volodymyr Babich, McDonough School of Business, Georgetown University


Recent cases of product adulteration by foreign suppliers have compelled many manufacturers to re-think approaches to deterring suppliers from cutting corners, especially when manufacturers cannot fully monitor and control the suppliers’ actions. In this paper we study three mechanisms for dealing with product adulteration problem: (a) the deferred payment mechanism: the buyer pays the supplier after the deferred payment period only if no adulteration has been discovered by the customers; (b) the inspection mechanism: the buyer pays the supplier immediately, contingent on product passing the inspection; and (c) the combined mechanism: a combination of the deferred payment and inspection mechanisms. We show that the inspection mechanism cannot completely deter the suppliers from product adulteration, while the deferred payment mechanism can. Surprisingly, the combined mechanism is redundant: either the inspection or the deferred payment mechanisms perform just as well. Finally, we identify four factors that determine the dominance of deferred payment mechanism over the inspection mechanism are: (a) the inspection cost relative to inspection accuracy, (b) the buyer’s liability for adulterated products, (c) the difference in financing rates for the buyer and the supplier relative to the defects discovery rate by customers, and (d) the difference in production costs for adulterated and unadulterated product. We find that the deferred payment mechanism is preferable to inspection if the threat of adulteration (either incentive to adulterate or the consequences) are low. The paper is available (here).

Friday, March 23, 3:30 – 4:30 PM

Location: Duques 553 (2201 G Street, NW)

Semi-parametric Bayesian Modeling of Spatiotemporal Inhomogeneous Drift Diffusions in Single-Cell Motility

Speaker: Ioanna Manolopoulou, Department of Statistical Science, Duke University


We develop dynamic models for observations from independent time series influenced by the same underlying inhomogeneous drift. Our methods are motivated by modeling single cell motion through a Langevin diffusion, using a flexible representation for the drift as radial basis kernel regression. The primary goal is learning the structure of the tactic fields through the dynamics of lymphocytes, critical to the immune response. Although individual cell motion is assumed to be independent, cells interact through secretion of chemicals into their environment. This interaction is captured as spatiotemporal changes in the underlying drift, allowing us to flexibly identify regions in space where cells influence each other’s behavior. We develop Bayesian analysis via customized Markov chain Monte Carlo methods for single cell models, and multi-cell hierarchical extensions for aggregating models and data across multiple cells. Our implementation explores data from multi-photon vital microscopy in murine lymph node experiments, and we use a number of visualization tools to summarize and compare posterior inferences on the 3-dimensional tactic fields.

Friday, March 23, 11:00 AM – 12:00 PM

Location: Duques 553 (2201 G Street, NW)

Dynamic Multiscale Spatio-Temporal Models for Gaussian Areal Data

Speaker: Marco A. Ferreira, Department of Statistics, University of Missouri – Columbia


We introduce a new class of dynamic multiscale models for spatio-temporal processes arising from Gaussian areal data. Specifically, we use nested geographical structures to decompose the original process into multiscale coefficients which evolve through time following state-space equations. Our approach naturally accommodates data observed on irregular grids as well as heteroscedasticity. Moreover, we propose a multiscale spatio-temporal clustering algorithm that facilitates estimation of the nested geographical multiscale structure. In addition, we present a singular forward filter backward sampler for efficient Bayesian estimation. Our multiscale spatiotemporal methodology decomposes large data-analysis problems into many smaller components and thus leads to scalable and highly efficient computational procedures. Finally, we illustrate the utility and flexibility of our dynamic multiscale framework through two spatio-temporal applications. The first example considers mortality ratios in the state of Missouri whereas the second example examines agricultural production in Espirito Santo State Brazil.

Friday, March 9, 2:00 – 3:00 PM

Location: Duques 453 (2201 G Street, NW)

On Coverage & Detection Problems in Sensor Networks

Speaker: Dr. Bimal Roy, Director, Indian Statistical Institute, Kolkata


Since a sensor has limited communication capability, covering a “field” with sensors so that the communication in the network is smooth is a challenging problem. A method of dropping sensors from a helicopter and then using an actuator (robot with limited intelligence and carrying capability) to make minor adjustments is proposed. Once the sensors are placed, detecting an event (say for example, an explosive) is the next challenge. Assuming a model for sensing, a method based on standard test of hypothesis is proposed.

Wednesday, March 7, 4:00 – 5:00 PM

Location: Duques 453 (2201 G Street, NW)

Business Analytics Degrees: Disruptive Innovation or Passing Fad?

Speaker: Michael Rappa, Founding Director, Institute for Advanced Analytics, North Carolina State University


Recently more and more schools have begun offering degrees in business analytics. This talk will use the nation’s first Master of Science in Analytics, now in its fifth year, as a backdrop to discuss the rise of analytics degree programs and the implications for business schools. In a future where data-driven decisions will be critically important to the success of business, will analytics become the impetus for disruptive innovation that transforms business education ? Or is analytics simply the latest in a long line of management fads soon to be forgotten ?

Wednesday, March 7, 10:30-11:45 AM

Location: Duques 453 (2201 G Street, NW)

Adaptive Convex Enveloping for Multidimensional Stochastic Dynamic Programming

Speaker: Sheng Yu, Engineering Management & Systems Engineering, George Washington University


Adaptive Convex Enveloping is a powerful general purpose method for solving convex stochastic dynamic programs. With its optimization-oriented design, Adaptive Convex Enveloping easily handles large numbers of decision variables and constraints with the speed and reliability of convex optimizations, and approximates the value function with error control on the entire state space. We discuss interesting aspects and strengths of the new method, and use it on battery station management to find the optimal policy for charging electric vehicle batteries.

Friday, March 2, 11:00-12:00PM

Location: Funger 420 (2201 G Street, NW)

Providers’ Profiling for Supporting Decision Makers in Cardiovascular Healthcare

Speaker: Francesca Ieva, Dipartimento di Matematica “F.Brioschi”, Politecnico di Milano


Investigations on surgical performance have always adopted routinely collected clinical data to highlight unusual providers outcomes. In addition,there are a number of regular reports using routinely collected data to produce indicators for hospitals. As well as highlighting possible high- and low-performers, such reports help in understanding the reasons behind variation in health outcomes, and provide a measure of performance which may be compared with benchmarks or targets, or with previous results to examine trends over time. Statistical methodology for provider comparisons has been developed in the context of both education and health. It is known that pursuing the issue of adjustment for patient severity (case-mix) is a challenging task, since it requires a deep knowledge of the phenomenon from a clinical, organizational, logistic and epidemiological point of view. However, this is the reason why it is always expected to be inadequate and therefore unavoidable residual variability (over-dispersion) will generally exist between providers. It is then crucial that a statistical procedure is able to assess whether a provider may be considered “unusual”. In particular, note that although hierarchical models arerecommended since they account for the nested structure in describing hospitals performance, it is not straightforward how assessing unusual performance. Studies of variations in health care utilization and outcomes involve the analysis of multilevel clustered data. Those studies quantify the role of contributing factors (patients and providers) and assess the relationship between health-care processes and outcomes. We develop Bayes rules for different loss functions for hospital report cards when Bayesian Semiparametric Hierarchical models are used, and discuss the impact of assuming different loss functions on the number of hospitals identified as “non acceptably performing”. The analysis is carried out on a case study dataset arising from one of the clinical survey arising from Strategic Program of Regione Lombardia, concerning patients admitted with STEMI to one of the hospitals of its Cardiological Network. The major aim consists of the comparison among different loss functions to discriminate among health care providers’ performances, together with the assessment of the role of patients’ and providers’ characteristics on survival outcome. The application of this theoretical setting to the problem of managing a Cardiological Network is an example of how Bayesian decision theory could be employed within the context of clinical governance of Regione Lombardia. It may point out where investments are more likely to be needed, and could help in not to loose opportunities of quality improvement.

Tuesday, February 21st 11:00 AM – 12:00 PM

Location: Funger 420 (2201 G Street, NW)

Fairness in Sharing Gains and Losses

Speaker: Dr. Luc Wathieu, Associate Professor, McDonough School of Business, Georgetown

Authors: Luc Wathieu (Georgetown University), Guillermo Baquero (ESMT, Berlin), Willem Smit (Singapore Management University)


We conducted an experimental exploration of ultimatum games involving gains and losses of varying amounts. Proposers indicated their offer in gain- (and neatly comparable) loss- games, respondents indicated minimum acceptable gain and maximum acceptable loss (n=326). We find a significant “generosity effect”: Proposers take the lion’s share of gains but respondents endure less than 50% of losses. We explain our results with a “Fairness Requirement Theorem” involving reference dependence and loss aversion.

Friday, February 17, 2012, 3:00-4:00 PM

Location: Duques Room 553 (2201 G Street, NW)

Data & Analytics – Enabling Consumer Preference

Speaker: Raghan Lal, Head of Analytics, VISA

Friday, February 10, 2012, 11:00-12:00PM

Location: Duques Room 651 (2201 G Street, NW)

Optimization and Resource Allocation Models in an Aviation Security System

Speaker: Rajan Batta, Professor of Industrial and Systems Engineering, University of Buffalo


This talk will summarize results from a recently completed NSF project related to airport security modeling. The general theme is the development and analysis of optimization and resource allocation models. The first part of the talk will delineate results for a model that focuses on security in the area prior to checkpoint screening. The second part of the talk will develop and present three separate models that all focus on improving the efficiency of checkpoint screening. For the second part of the talk implementation issues will also be discussed.

Thursday, February 9, 2012, 10:30-11:30 AM

Location: Duques Room 520 (2201 G Street, NW)

Solving Two-Stage Stochastic Steiner Tree Problems by Two-Stage Branch-and-Cut

Speaker: Ivana Ljubic (Decision, Operations, and Information Technologies Department, The Robert H. Smith School of Business, University of Maryland)


Network design problems frequently occur in various practical areas, e.g., in the design of fiber optic networks or in the development of district heating or water supply systems. Most of the network design problems are NP-hard combinatorial optimization problems. In practice, network design problems are often subject to uncertainty of the input data. It might happen that the actual demand patterns or connection costs become known only after the network has been built. In that case, networks found by solving an instance in which it is assumed that the complete knowledge of the input is known up-front, might not provide appropriate solutions if deviations from the assumed scenario are encountered. Stochastic and robust optimization are two promising ways to take these uncertainties into account. In this talk we consider the Steiner tree problem under a two-stage stochastic model with recourse and finitely many scenarios. In this problem, edges are purchased in the first stage when only probabilistic information on the set of terminals and the future edge costs is known. In the second stage, one of the given scenarios is realized and additional edges are purchased in order to interconnect the set of (now known) terminals. The goal is to decide on the set of edges to be purchased in the first stage while minimizing the overall expected cost of the solution. We consider mixed integer programming formulations for this problem and propose a two-stage branch-and-cut (B&C) approach in which L-shaped and integer-L-shaped cuts are generated. In our computational study we compare the performance of two variants of our algorithm with that of a B&C algorithm for the extensive form of the deterministic equivalent (EF). We show that, as the number of scenarios increases, the new approach significantly outperforms the (EF) approach.

This is a joint work with Immanuel Bomze (University of Vienna), Markus Chimani (University of Jena), Michael Juenger (University of Cologne), Petra Mutzel and Bernd Zey (TU Dortmund).

Friday, January 27, 2012, 11:00-12:00PM

Location: Duques Room 521 (2201 G Street, NW)

Title: Markov Chain Monte Carlo for Inference on Phase-Type Models

Speaker: Simon Wilson, School of Computer Science and Statistics Trinity College, Dublin, Ireland


Bayesian inference for phase-type distributions is considered when data consist only of absorption times. Extensions to the methodology developed by Bladt et al. (2003) are presented which enable specific structure to be imposed on the underlying continuous time Markov process and expand computational tractability to a wider class of situations. The conditions for maintaining conjugacy when structure is imposed are shown. Part of the original algorithm involves simulation of the unobserved Markov process and the main contribution is resolution of computational issues which can arise here. Direct conditional simulation, together with exploiting reversibility when available underpin the changes. Ultimately, several variants of the algorithm are produced, their relative merits explained and guidelines for variant selection provided. The extended methodology thus advances modelling and tractability of Bayesian inference for phase-type distributions where there is direct scientific interest in the underlying stochastic process: the added structural constraints more accurately represent a physical process and the computational changes make the technique practical to implement. A simple application to a repairable redundant electronic system when ultimate system failure (as opposed to individual component failure) comprise the data is presented. This provides one example of a class of problems for which the extended methodology improves both parameter estimates and computational speed.

Friday, January 6, 2012, 11:15 AM – 12:15 PM

Location: Duques Room 553 (2201 G Street, NW)