Adventures in Sports Scheduling
Carnegie Mellon University Tepper School of Business
Major League Baseball is a multi-billion dollar per year industry that relies heavily on the quality of its schedule. Teams, fans, TV networks, and even political parties (in a way revealed in the talk) rely on the schedule for profits and enjoyment. Only recently have the computational tools of operations research been powerful enough to address the issue of finding optimal schedules. I will discuss my experiences in scheduling college basketball, major league baseball, and other sports, and discuss major trends in optimization that lead to practical scheduling approaches.
Time: Friday, April 30th 11:00 am -12:15 pm
Location: Duques 652 (2201 G Street, NW)
Stochastic Integer Programming Models for Air Traffic Flow Management Problems
Speaker: Michael O. Ball,
Robert H Smith School of Business & Institute for Systems Research, University of Maryland
In this paper we address a stochastic air traffic flow management problem. Our problem arises when airspace congestion is predicted, usually because of a weather disturbance, so that the number of flights passing through a volume of airspace (flow constrained area, FCA) must be reduced. We
formulate an optimization model for the assignment of dispositions to flights whose preferred flight plans pass through an FCA. For each flight, the disposition can be either to depart as scheduled but via a secondary route, or to use the originally intended route but to depart with a controlled (adjusted) departure time and accompanying ground delay. We model the possibility that the capacity of the FCA may increase at some future time once the weather activity clears. The model is a two-stage stochastic
program that represents the time of this capacity windfall as a random variable, and determines expected costs given a second-stage decision, conditioning on that time. A novel aspect of our model is to allow the initial secondary routes to vary from pessimistic (initial trajectory avoids weather entirely) to optimistic (initial trajectory assumes weather not present). We conduct experiments allowing a range of such trajectories and draw conclusions regarding appropriate strategies.
Time: Friday, March 12, 2010, 3:30-4:30 pm
Location: Duques 553 (2201 G Street, NW)
Multi- and Matrix-variate Times Series & Graphical Models
Speaker: Mike West
Department of Statistical Science, Duke University
I will review some recent and current developments in Bayesian modelling of multi- and matrix-variate time series, all involving the integration of graphical modelling ideas and methods with dynamic models. This includes graphical models to constrain multivariate stochastic volatility models in financial applications and extensions to matrix-variate times series with economic examples. Stochastic simulation and search for Bayesian computations in these models are key and will be discussed, as will some current research frontiers. The talk covers developments from projects in collaborations with current and past students Carlos Carvalho, Craig Reeson and Hao Wang.
Time: Friday, March 5, 2010, 4:00-5:00 pm
Location: Duques 553 (2201 G Street, NW)
Network Routing in a Dynamic Environment
Speaker: Nozer D. Singpurwalla
Department of Statistics, The George Washington University
Network routing as done by network theorists, computer scientists, and operations research analysts, assumes that the failure probabilities of nodes and links are fixed and known. In many cases, this is an idealization. A case in point is the routing of material and personnel in the presence of improvised explosive devices (IED). The placement of IED’s by an active adversary makes the underlying probabilities dynamic. Assessing these probabilities calls for the pooling of data from diverse sources and the modeling of socio psychological behaviour of the adversary and the route planner. The situation is unconventional. In this talk I present a Bayesian approach for accomplishing the above. My approach has two novel features. The first is a strategy for specifying likelihoods that encapsulate adversarial behaviour, and the second is the generation of likelihoods empirically by sampling from the posterior distribution of a logistic regression.
Time: Friday, January 29, 2010, 4:00-5:00 pm
Location: Duques 553 (2201 G Street, NW)
A Strategic Perspective on Reverse Channel Design: Why a Less Cost-efficient Product Returns Channel Would Improve Manufacturer Profits
Speaker: Canan Savaskan-Ebert
R.H. Smith School of Business, University Maryland
Kellogg School of Management, Northwestern University
A homeowner buys wallpaper only to find out that it does not look as good as anticipated in the room and decides to incur a 30% restocking fee to return it. A photographer pays a 20% restocking fee to return a lens after discovering that a lens with a different focal length would be better suited for his subject. A businessman buys a new smartphone and realizes its trade-off between battery life and functionality does not fit with his lifestyle. These are just a few examples of product returns, a key cost factor that represents a great financial concern for sellers. In fact, product returns cost U.S. companies more than $100 billion annually. It is estimated that the U.S. electronics industry alone spent $13.8 billion dollars in 2007 to restock returned products (Lawton 2008). The bulk of these returns were non-defective items that simply weren’t what the consumer wanted. It is clear that product returns from consumers are costing companies a substantial amount of money. What is not as clear is who should pay for the cost and who should take responsibility for the returned units.
To eliminate returns and/or to recoup the cost of handling returns, many retailers today are adopting the practice of charging restocking fees to consumers as a penalty for making returns. In this paper, we employ an analytical supply chain model of a bilateral monopoly to examine how the product return policy, product prices, consumer demand and product return rates are affected by the choice of the agent (manufacturer or the retailer) who takes assumes responbibility for taking back and salvaging returned products. This study provides an explaination for why some manufacturers may take back and salvage consumer returns even though the retailer can more effectively and cost efficiently do so. Counter to common intuition, we show that the return penalty may be more severe even when returns are salvaged by a channel member who derives greater value from a returned unit. The manufacturer may earn greater profit by accepting returns even if the retailer has a more efficient outlet for salvaging units. As one of the very first studies on this topic, in a supply chain context, this paper shows that by assuming returned product responsibility, the manufacturer can use the refund scheme in the reverse channel as a means to align incentives in the forward supply process.
Time: Thursday, January 28, 2010, 11:00-12:00 noon
Location: Funger Hall 520
Coordinating Semi-Conductor Supply Chains
Speaker: Mehmet Altug
Department of Decision Sciences, The George Washington University
This talk is based on three related supply chain coordination problems that are motivated by working with a global semi-conductor manufacturer. First, we study a problem observed between the semi-conductor manufacturer and its distributors. We consider a vertically differentiated model, where the manufacturer makes a high and low quality (performance) product and sells these to a single distributor which in turn needs to price and sell them to a market with consumers that have heterogeneous valuations for quality. We determine the economic distortions that undermine both the sell-up (selling more of the higher quality part) and sell-through (volume) objective of the manufacturer. To align the economics of both parties, we analyze and compare several contracts. We then extend our model to multiple distributors to understand the effect of Cournot competition and derive an efficiency result under wholesale pricing as competition increases.
In the second half of the seminar, we consider the quality selection problem between the semi-conductor manufacturer and its resellers. In such a multi-supplier-one manufacturer type environment, each supplier sells a different component with a predefined quality range. The reseller has to decide on what quality to choose for each component as it is assembling them into a computer and trade-off between the total cost and the total quality of the computer both of which increases with individual quality levels of the components. Based on a model that translates these individual quality levels into one final product quality, we first define the manufacturer’s strategic design problem. We then characterize the strategic interaction among the suppliers and show what kind of inefficiencies in such systems could occur. Finally, we present the impact of gray market on supply chain coordination problems and explain why some of the contracts studied earlier may not be efficient in the presence of such gray markets.
Time: Friday, January 22nd 3:30-4:30 pm
Location: Funger Hall 620
Durable Products, Time Inconsistency, and Lock-in
Speaker: Sreelata Jonnalagedda
McCombs School of Business, The University of Texas at Austin
Many durable products cannot be used without a contingent consumable product, e.g. printers require ink, iPods require songs, razors require blades, etc. For such products, manufacturers may be able to lock-in consumers by making their products incompatible with consumables that are produced by other firms. We examine the effectiveness of such a strategy in the presence of strategic consumers who anticipate the future prices of both the durable product and the contingent consumable. On the one hand, by locking-in consumers to its own contingent consumable, a durable goods manufacturer can dampen its own incentive to reduce durables prices over time, thereby mitigating the classic time inconsistency problem. On the other hand, lock-in will also create a hold-up issue and will adversely affect consumers’ expectations of future prices for the contingent consumable. We demonstrate the trade-off between these two issues, time inconsistency and hold-up, and derive analytical results that provide insights about the conditions under which a lock-in strategy can be effective.
Time: Tuesday, January 19th 11:00-12:00 noon
Location: Funger Hall 520