Spring of 2008
Welcome to the Management Science and Operations Management (MSOM) Seminar at the School of Business Administration at the University of Miami.
Here you will find details about the talks given during the Spring of 2008.
Past Talks
- April 11th, 11:00am, room KE 403.
Speaker: Sujin Kim, School of Industrial Engineering, Purdue University
Title: Adaptive Control Variate Methods in Monte Carlo Simulation
Abstract: In this talk we discuss recent results in adaptive control variate methods. Adaptive Monte Carlo methods are Monte Carlo simulation techniques that aim at improving performance of simulation experiments by adaptively tuning variance reduction methods as the simulation progresses. The primary focus of such techniques has been in adaptively tuning importance sampling distributions to reduce the variance of an estimator. We instead focus on adaptive control variate schemes, developing asymptotic theory for the performance of two adaptive control variate estimators. The first estimator is based on a stochastic approximation scheme for identifying the optimal choice of control variate. It is easily implemented, but its performance is sensitive to certain tuning parameters. The second estimator uses a sample average approximation approach. It has the advantage that it does not require any tuning parameters, but it can be computationally expensive and requires the availability of nonlinear optimization software. We present the numerical results of simulation experiments on pricing of barrier options using these two adaptive methods. (This is joint work with Shane G. Henderson, Cornell University.) The talk will conclude with a brief overview of ongoing research efforts and future research directions. Traditionally, power system operation and management involves a variety of challenging decision problems, and new interesting problems have emerged with the restructuring of many electricity markets around the globe. In this context, current research on applications of stochastic optimization algorithms for problems such as unit commitment and strategic bidding is described.
About the Speaker: Dr. Kim holds a Ph.D. degree in Operations Research from Cornell University. She is currently Visiting Assistant Professor in the Department of Industrial Engineering at Purdue University. Her research interests include simulation methodology, simulation optimization, and applications in electric power system management and air ambulance services.
- February 13th, 11:00am, room KE 403.
Speaker: Natalia Yankovic, Columbia University
Title: Nurse Staffing: Patients or Patience?
Abstract: Nurse staffing in hospitals is traditionally based on target ratios of patients to nurses. However, varying needs for care based on age, diagnosis, case severity, and medical complexity, as well as emerging evidence relating nursing levels to quality of care have led to the search for better staffing models. This talk will describe a newly developed queuing model for nurse staffing and an on-going hospital-based project to implement it to assure timely nursing responses to patients' needs.
- February 8th, 11:00am, room KE 403.
Speaker: Tim Leung, Princeton University
Title: Credit Derivatives and Risk Aversion
- February 4th, 11:00am, room KE 403.
Speaker: Jingchen Liu, Department of Statistics, Harvard University
Title: Rare-event Simulation for Heavy-tailed Multi-server Queues
Abstract: In this talk, I will discuss the first provably efficient simulation algorithm for steady-state estimation of long delays in a positive recurrent two-server (G/G/2) queue with heavy-tailed service requirement. Long delays are usually caused by one or two customers (depending on the traffic intensity) who have extremely large service requirement and block the servers for long time. We propose a three-step program to design the algorithm and prove its efficiency. First, we adopt a mixture family of changes-of-measure; second, propose an appropriate Lyapunov inequality to control the variance of our estimator; third, construct a Lyapunov function (the solution to the Lyapunov inequality) and tune various parameters to verify the inequality. Because of the upper bound provided by the Lyapunov function, our method also suggests an asymptotic approximation of the rare-event probability. Therefore, rare-event simulation and large deviations analysis for heavy-tailed models are connected naturally. Our strategy including the mixture family, the construction of Lypunov function, and proof techniques can solve a large class of problems. We shall also mention other large deviations problems involving multidimensional heavy-tailed models for which our program can be successfully applied.
- January 25th, 11:00am, room KE 403.
Speaker: Stergios Fotopoulos, Department of Management and Operations, Washington State University
Title of First Part: Change-Point Estimation for Gaussian Sequences
Abstract:
Title of Second Part: Change-Point Analysis for Stochastic Systems with Poisson Inputs
Abstract:
- January 18th, 11:00am, room KE 403
Speaker: Robert Lund, Department of Mathematical Sciences, Clemson University
Title: Periodic Time Series
Abstract: This talk overviews modeling and inference procedures for time series data with periodic means and autocovariances. Such series arise in hydrology, meteorology, astronomy, economics, and ecology. The class of periodic autoregressive moving-average (PARMA) models is introduced to describe series with second-order periodicities. PARMA models are compared and contrasted to seasonal autoregressive moving-average models. Testing for the presence of second order periodicities, asymptotic properties of PARMA parameter estimators, and parsimonious PARMA modeling are central issues of the talk. An application to United States temperature trends is given.