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Speaker: Vicky Mak
School of Information Technology, Deakin University, Victoria, Australia
In this talk, I will give an overview of my experience in the modelling, solving, and some theoretical analysis of a number of combinatorial optimization problems. These include: (1) The polyhedral analysis and branch-and-bound based methods in obtaining exact solutions and to a number of VRP family and TSP-family problems. Areas of application: aircraft rotation and network design. (2) A new idea for exact solutions for integer programs: Iterative Aggregation and Disaggregation. (3) Constraint programming-based algorithm for the treatment planning optimization of Intensity-modulated radiotherapy. (4) Other optimization problems in the areas of frequency assignment, machine scheduling, and robotics routing.
Time: Friday, December 18, 11:00-12:00 noon
Location: Funger Hall 520 (2201 G Street, NW)
Speaker: Onesun Steve Yoo
Anderson School of Management, University of California at Los Angeles
For many entrepreneurs, the main bottleneck resource of their company is their own time, rather than cash. In this paper, we develop a dynamic time-management framework for entrepreneurial process improvement for contexts where time is more constrained than cash, and provide clear guiding principles for time management. We classify an entrepreneur's daily activities into four categories: fire-fighting, process improvement, revenue enhancement, and revenue generation, and analyze a stylized dynamic time allocation problem for maximizing long-term expected profits. We find that entrepreneurs should first invest time in process improvement until the process reliability reaches a certain threshold, then in revenue enhancement until the revenue rate reaches a certain threshold, and only then spend time generating revenue. Also, entrepreneurs with lower initial revenue rates should invest more time in process improvement and in revenue enhancement, ultimately earning revenue at a higher rate than if they were endowed with a higher initial revenue rate. Our model formally links time with money and introduces a framework for evaluating the opportunity cost of an entrepreneur's time. We highlight the performance difference between the optimal policy and two commonly employed (well-intentioned) time management heuristics and show that working hard does not necessarily imply good time management.
Time: Wednesday, December 16th 10:45-12:00 noon
Location: Duques 553
Speaker: Gokce Esenduran
Kenan-Flagler Business School, The University of North Carolina at Chapel Hill
We consider an original equipment manufacturer (OEM) in an industry regulated with take-back legislation that holds the OEM responsible for either taking back and properly treating products that reach the end of their life cycles or facilitating such take back and proper treatment through third parties. We use a stylized model of take-back legislation and consider three levels of legislation (no takeback legislation, legislation on collection levels, legislation on collection and reuse levels) in two different supply-chain settings. We aim to understand whether legislation causes an increase in remanufacturing levels and if it can induce OEMs to manufacture products that are easier and cheaper to remanufacture. We first analyze the effects of legislation on an OEM with in-house remanufacturing
capabilities. We find that if the manufacturing cost is very low, legislation on collection levels does not induce remanufacturing while if the cost is high, legislation may be redundant. While take-back legislation never causes a decrease in remanufacturing levels, it may cause an increase in the price of remanufactured goods and a decrease in the level of new-product manufacturing. If the OEM does
not remanufacture in-house and competes with a third-party remanufacturer instead, contrary to our earlier result, we find that legislation may cause a decrease in remanufacturing levels. But surprisingly, when we compare the effect of legislation on an OEM with in-house remanufacturing versus one competing with third-parties, remanufacturing levels may be higher in the latter for the same level of legislation. Finally we find that take-back legislation does induce OEMs to manufacture products that are easier and cheaper to remanufacture.
Time: Friday, December 10, 11:00-12:00 noon
Location: Funger Hall 320 (2201 G Street, NW)
Speaker: Rouba Ibrahim
Department of Industrial Engineering and Operations Research, Columbia University
Motivated by the desire to make delay announcements to arriving customers, we study alternative ways of estimating customer delay in many-server service systems. Our delay estimators differ in the type and amount of information that they use about the system. We introduce estimators that effectively cope with real-life phenomena, such as customer abandonment (impatience), time-varying arrival rates, and general service-time distributions. We use computer simulation and heavy-traffic analysis to
verify that our proposed estimators outperform several natural alternatives.
Time: Friday, December 9, 11:00-12:00 noon
Location: Funger Hall 320 (2201 G Street, NW)
Speaker: Marti A. Hearst
School of Information, University of California, Berkeley
We are in the midst of extraordinary change in how people interact with one another and with information. A combination of advances in technology and change in people's expectations is altering the way products are sold, scientific problems are solved, software is written, elections are conducted, and government is run. People are social animals, and as Shirky notes, we now have tools that are flexible enough to match our in-built social capabilities. Things can get done that weren't possible before because the right expertise, the missing information, or a large enough group of people can now be gathered together at low cost. These developments open a number of interesting research questions and potentially change how scientific research should be conducted. In this talk I will attempt to summarize and put some structure around some of these developments.
Time: Friday, November 20th, 11:00-12:00 noon
Location: Duques Hall 254 (2201 G Street, NW)
Speaker: Dick De Veaux
Department of Mathematics and Statistics, Williams College
Data mining has been defined as a process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions and decisions. Statistical inference concerns the same problems. Are the two really different? Through a series of case studies, we will try to illuminate some of the challenges and characteristics of data mining. Each case study reminds us that the important issues are often the ones that transcend the methodological choice one faces when solving real world problems. What lessons can these teach us about teaching the introductory course?
Time: Thursday, November 19 4:00-5:00 pm
Location: Funger Hall 620 (2201 G Street, NW)
Speaker: Javier Pena
Tepper School of Business, Carnegie Mellon University
Finding a Nash equilibrium of an extensive form game is a central problem in computational game theory. For a two-person, zero-sum game this problem can be formulated as a linear program, which in principle is solvable via standard algorithms such as the simplex or interior-point methods. However, most interesting games lead to enormous linear programs that are beyond today's computational capabilities. We propose specialized algorithms that tailor modern smoothing techniques to the highly structured polytopes that arise in the Nash equilibrium formulation. We discuss computational results with instances of poker, whose Nash equilibrium formulation has about nearly a billion variables and a billion constraints.
Time: Friday, November 13th 3:30-4:30 pm
Location: Duques Hall 553 (2201 G Street, NW)
Speaker: Nalini Ravishanker
Department of Statistics, University of Connecticut
Easy availability of information on a customer's transactions with the firm and the pressure to establish financial returns from marketing investments has led to a dominance of models that directly connect marketing investments to sales at the customer level. Customer's attitudes, on the other hand, have always been assumed to influence customer's reactions to a firm's marketing communications, but rarely included in models that determine customer value. We empirically assess (a) the role of customer's attitudes in determining their value to the firm, and (b) how knowledge of customer attitudes can influence a firm's customer management strategy. Specifically, we evaluate which aspects of attitudes, i.e., attitudes towards firm or competition, have a bigger effect on customer behavior, and whether customer attitudes are more important for managing some customers than others. We use monthly sales call, sales, and survey based attitude information collected over three years from the same customers of a multinational pharmaceutical firm for this study. We develop a hierarchical generalized dynamic linear model (HGDLM) framework that combines the sales call and sales data that are available at regular time intervals, with customer attitudes that are not available at regular intervals, and carry out inference in the Bayesian framework.
Time: Monday, October 19th 4:00-5:00 pm
Location: Funger Hall 520 (2201 G Street, NW)
Speaker: David Higdon
Statistical Sciences, Los Alamos National Laboratory
The Lambda-Cold Dark Matter (LCDM) model of cosmology is perhaps the simplest model that best describes the makeup and evolution of the universe in accordance with physical observations. This model contains up to 20 different cosmological parameters from space and ground based surveys.
These cosmological measurements have reached a remarkable level of accuracy over the last decade. Future sky surveys promise to give even more numerous and more accurate data. However, such data does not inform directly about the cosmological parameters of interest. Detailed physical simulation models are typically required to relate information from these surveys to cosmological parameters of interest. A Bayesian formulation adapted from Kennedy and O'Hagan (2001) and Higdon et al. (2008) is used to give parameter constraints from physical observations and a limited number of simulations. The framework is based on the idea of replacing the simulator by an emulator which can then be used to facilitate computations required for the analysis. In this talk I'll describe an application that uses large scale structure and Cosmic Microwave Background (CMB) data to inform about a subset of the
parameters controlling the LCDM model.
Time: Friday, October 9th 4:30-5:30 pm (Followed by wine & cheese reception)
Location: Duques Hall 652 (2201 G Street, NW)
Speaker: Margaret M. Polski
George Mason University
Join the GW School of Business Institute for Integrating Statistics in Decision Sciences (I2SDS), the Department of Decision Sciences, and the Elliott School of International Affairs in hosting Dr. Polski for an informative book discussion. The event is free and open to the public.
Margaret M. Polski is a political economist with research interests in growth, innovation, regulation, and security. She has more than 25 years of experience developing and implementing transformation initiatives in business, government, and civic affairs. Dr. Polski has a Ph.D. from Indiana University, an M.P.A. from the Kennedy School of Government at Harvard University, and a B.E.S. from the University of Minnesota. She is a Research Affiliate at the Krasnow Institue for Advanced Study at George Mason University and a Research Fellow at the Institute for Development Strategies at the School for Public and Environmental Affairs at Indiana University.
Time: Wednesday, September 30th 3:00 - 4:00 pm
Location: Duques Hall 652