Past Seminars

Spring 2007
Fall 2006
Spring 2006
Winter 2006
Fall 2005

Spring 2007

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"Inventory Management of a Fast-Fashion Retail Network"
Talk by Jeremie Gallien, MIT
March 28, 2007 - 2:15 PM, Room 561
Fast-fashion retailers (e.g. Zara, H&M) have met some success responding to volatile demand trends through frequent introductions of new garments produced in small series. An important associated operational problem is the allocation over time of a limited amount of inventory across all stores in their network. I will present stochastic and deterministic models developed in collaboration with Zara to address this challenge, then discuss the implementation and impact of this work.

"Incorporating Risk Considerations into Inventory Models and Supply Contracts"
Talk by Candace Yano, U of C at Berkeley
April 11, 2007 - 1:30 PM, Room 561
When retailers purchase goods, they often make decisions about purchase quantities or enter into supply contracts that aim to mitigate their risk. Very little of the research literature on inventory models and supply contracts explicitly considers risk, however. In this talk, we discuss two procurement models in which the decision-makers are concerned about risk. The first model considers a buyer and supplier, both of whom are risk averse but have some tolerance for risk. We show that several popular contract forms do not take full advantage of both parties tolerance for risk and therefore cannot optimize supply chain profits. We then propose a contract structure that overcomes these limitations.

In the second model, we consider a retailer who buys and then sells a product. The retailer is concerned about meeting corporate profit targets and this is the cause of his risk aversion. We show that under the standard accrual method of accounting, optimal inventory policies that properly account for his risk aversion have a relatively simple structure, but under the cash-basis method of accounting, optimal inventory policies may be extremely complicated and may have unintended consequences.

(Various parts of this research were done in collaboration with Shiming Deng of Oracle, Inc., Houmin Yan of the Chinese University of Hong Kong, and Hanqin Zhang of the Chinese Academy of Sciences.)

"Coordination of Marketing and Production for Price and Leadtime Decisions" and "Centralized vs. Decentralized Competition for Price and Lead-time Sensitive Demand"
Talk by Pinar Keskinocak, Georgia Tech
April 25, 2007 - 1:30 PM, Room 561

We study decentralized price and leadtime decisions made by the marketing and production departments, respectively, where the customers are sensitive both to the quoted price and the lead time.

First, we consider a single firm (in a monopoly setting) and analyze the inefficiencies that are due to the decentralization of price and leadtime decisions. In the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments. We also provide insights on the sensitivity of the optimal decisions with respect to market characteristics, sequence of decisions, and the firm’s capacity level.

Next, we extend our analysis to a competitive setting. We study two firms that compete based on their price and lead-time decisions in a common market. We explore the impact of the decentralization under competition comparing three scenarios: (i) Both firms are centralized, (ii) only one firm is centralized, (iii) both firms are decentralized. We find that under intense price competition, firms may suffer from a decentralized strategy, particularly under high flexibility induced by high capacity, where revenue based sales incentives motivate sales/marketing for more aggressive price cuts resulting in eroding margins. On the other hand, when price competition in the market is less intense than lead-time competition, a decentralized decision making strategy may dominate a centralized decision making strategy.

This is joint work with Pelin Pekgun and Paul Griffin.

"Complementarity in Improvement Programs"
Talk by Phil Lederer, University of Rochester
May 16th, 2007 - 1:30 PM, Jacobs Room 165

During the past two decades firms have adopted many types of functional improvement programs. In operations programs such as TQM, MRP, JIT have been adopted to reduce cost, increase quality and improve customer service. In marketing, marketing research programs have been used to better understand customer tastes. In accounting, ABC, and other programs to determine more accurate product costs have been implemented. Although many of these programs have been studied there has been little work on the joint effects (if any) between these activities. Indeed, the broadest claim of the continuous improvement movement is that all improvement activities are complementary, meaning that there are increasing gains to improvement programs. Papers such as Milgrom and Roberts (1990) seem to imply that most modern manufacturing innovations are complementary to each other.

This research studies the impact of combinations of improvement activities on firm performance. We study three types of improvement programs: process improvement, marketing research and cost estimation. Process improvements lower the variable cost of production, increases product quality, or cuts lead time, marketing research programs help us to identify segments and price accordingly, and cost estimation programs allow better cost estimation for pricing and control. We show that improvement programs may be complements or substitutes. This means that improvement programs can increase or decrease the desirability of the other programs. In particular, we show conditions that imply that marketing research and cost estimation programs are substitutes to each other. One of these conditions is that production technology displays increasing returns to scale, a characteristic found in queuing and inventory driven production systems. A model of a production system with queuing demonstrates the results. Further, our work demonstrates that process improvement programs reduce losses due to decentralized control of firms. The implication is that process improvement programs encourage decentralized organization of other improvement efforts.

Fall 2006

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"Inventory Turnover Performance in the U.S. Retail Sector"
Talk by Vishal Gaur, NYU
September 27, 1:30 PM, Room 561
We report results from published and ongoing empirical studies in inventory turnover in the U.S. retail sector, employing financial data for about 400 public-listed retailers across ten retail segments for the years 1985-2004. In the first part of this research, we develop benchmarks for inventory turns for different types of retailers, and show the correlation of inventory turns with other performance measures such as gross margin and capital intensity. We also show the effects of demand uncertainty, sales growth rate, and size on inventory turns. In the second part of this research, we show the effect of inventory turnover performance of retailers on their financial performance, measured by their stock returns. Using a case study, we also discuss possible reasons why inventory turnover may be a predictor of financial performance.
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"Safeguarding Strategic Supplies: Planning for Disaster"
Talk by Awi Federgruen, Columbia
October 18, 1:00 PM, Room 561
Standard supply chain management texts discuss the benefits of consolidating the set of suppliers in the chain. These benefits include economies of scale in the production costs as well as statistical economies of scale due to the pooling of demand risks. Recently, many corporations and governments, alike, have recognized a variety of risks associated with external disruptions of the supply process. These provide a powerful argument against (maximal) consolidation. Such disruptions may arise because of “natural” disasters, e.g. fires in production plants or the need to shut down a facility because of violations of quality regulations or standards. Disruptions may also occur because of labor strikes, or planned acts of sabotage, resulting from terror attacks among others. While these disruptions may be rare, their consequences can be catastrophic for an individual firm as well as for a region or a country as a whole.
We analyze a planning model for a firm or public organization which needs to cover uncertain demand for a given item by procuring supplies from multiple sources. The necessity to employ multiple suppliers arises from the fact that when an order is placed with any of the suppliers, only a random fraction of the order size is useable. The model considers a single demand season, with a given demand distribution, where all supplies need to be ordered simultaneously before the start of the season. The suppliers differ from each other in terms of their yield distributions, their procurement costs and capacity levels.
The planning model determines which of the potential suppliers are to be retained and what size order is to be placed with each. We consider two versions of the planning model: in the first, (SCM), the orders must be such that the available supply of useable units covers the random demand during the season with (at least) a given probability. In the second version of the model, (TCM), the orders are determined so as to minimize the aggregate of procurement costs and end-of-the season inventory and shortage costs. In the classical inventory model with a single, fully reliable, supplier, these two models are known to be equivalent, but the equivalency breaks down under multiple suppliers with unreliable yields.
Determining the optimal set of suppliers, the aggregate order and its allocation among the suppliers, on the basis of the exact shortfall distribution, is prohibitively difficult. We have therefore developed two approximations for the shortfall distribution. While both approximations are shown to be highly accurate, the first, based on a Large Deviations Technique (LDT), has the advantage of resulting in a rigorous upper bound for the required total order. The second approximation is based on a Central Limit Theorem (CLT) and is shown to be asymptotically accurate, while the order quantities determined by this method are asymptotically optimal, as the number of suppliers grows. Most importantly, this CLT-based approximation permits many important qualitative insights.
Based on the CLT-approximation, we develop, for both the (SCM) and (TCM), a highly efficient procedure which generates the optimal set of suppliers as well as the optimal orders to be assigned to each. Most importantly, these procedures generate a variety of important qualitative insights, for example, regarding which sets of suppliers allow for a feasible solution, both when they have ample supply, and when they are capacitated.
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"Service Level Agreements in Call Centers: Perils and Prescriptions" (pdf/Kellogg ID required)
Talk by Tava Olsen, Washington-St. Louis
October 25, 1:30 PM, Room 561
A call center with both contract and non-contract customers was giving priority to the contract customers only in off-peak hours, precisely when having priority was least important. Using asymptotic analysis we show why this is indeed rational behavior on the part of the call center and what the implications are for customers. We then suggest other contracts that do not result in this type of undesirable behavior from a contract customer’s perspective. We compare the performance of the different contracts in terms of mean, variance, and outer percentiles of delay for both customer types using both numerical and asymptotic heavy-traffic analyses.
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"Investment and Market Structure in Congestible Services"
Talk by Ramesh Johari, Stanford
November 1, 1:30 PM, Room 561
We consider investment and market structure in a model of congestion-sensitive service provision. Our starting point is a simple model of network routing that has received a great deal of attention in the engineering community, the so-called "selfish routing" model. A continuum of users wish to send data from source to destination, and can choose from several parallel routes. Each route is owned by an independent network provider that sets a price per unit flow along the route. A user's overall disutility is measured as the sum of price and congestion experienced along the chosen route. In contrast to previous work, we consider a model where providers can invest in their routes, to minimize the impact of this congestion externality.
We investigate this model through the Nash equilibria of the pricing and investment game played by providers. We find that returns to investment and the timing of strategic decisions are critical determinants of the outcome of the game. For a broad range of models for which (1) providers choose prices and investments simultaneously, and (2) the model exhibits nonincreasing returns to investment, we show that if a pure strategy Nash equilibrium exists, it is unique, symmetric, and efficient; we also establish conditions for existence of pure strategy Nash equilibrium in special cases. This result does not hold if either (1) or (2) are violated, and we discuss these scenarios as well. We also investigate several extensions, including modeling the entry of providers into the market. We will emphasize the implications of our results for key issues in telecommunications, including wireless Internet service penetration and the viability of source-directed routing.
This is joint work with Gabriel Weintraub and Ben Van Roy.
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"Revenue Management Models in Media Broadcasting"
Talk by Ioana Popescu, INSEAD
November 9, 1:30 PM, Room 561
An important challenge faced by media broadcasting companies is how to allocate limited advertising space across multiple clients and markets (upfront/scatter) in order to maximize profits. We develop stylized optimization models of inventory allocation under audience uncertainty. At the strategic planning level, we provide simple solutions for upfront market allocation and contracting for multiple clients. In a dynamic setting, we investigate make-goods allocation during the scatter market, under reversible and irreversible commitment regimes. Our results hold under general performance metrics, and bring out interesting parallels with standard inventory and revenue management frameworks.
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"Competing on Time: A Framework for New Product Introduction Decision in the High Technology Industry"
Talk by Ozalp Ozer, NYU
January 17, 1:30 PM, Room 561
In this presentation, we will outline the challenges and uncertainties associated with bringing a new product to market. To do so, we will focus on a major global high-technology company located in the Bay Area and discuss their challenges related to new product introductions (NPI). The high technology industry is characterized by lightning speed in technology innovation, intense competition and relentless price erosion. It is, therefore, critical to bring the new product to the market at the right time to ensure profitability.

We will present our OR based modeling framework that is used to help a major global high-technology company make effective time-to-market decisions. Our model solves the problem in two nested phases: a design phase and a mass production phase. The design phase is modeled as an optimal stopping problem where decision to "enter or not" is made. The solution of the design stage affects the mass production phase. This second phase is modeled as a stochastic production control problem where production decisions are made. We will characterize an optimal policy for market timing, an optimal policy for production decisions and how and why they are amenable for implementation. We will also discuss the techniques used to solve this large-scale stochastic dynamic program, including how structural results enabled us to improve computational efficiency.

Finally, we will discuss how this project and the resulting software enabled various functional areas, such as Finance, Manufacturing, Marketing and R&D within the firm to communicate and jointly address this strategic question. If time permits, we will share our perspectives on the challenges and key success factors of working at the university/industry boundary

Spring 2006
The following visited NU in Spring 2006 for the Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

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Please check individual listings for locations of the talks.


"Dynamic Resource Control in a Stochastic Network: Limiting Regimes and Asymptotic Optimality"
Talk by David Yao, Columbia
April 7, 12:05 PM, Jacobs Rm. 561
We study a class of stochastic networks with concurrent occupancy of resources, which, in turn, are shared among jobs. For example, streaming a video on the Internet requires bandwidth from all the links that connect the source of the video to its destination; and the capacity of each link is shared, according to a certain protocol, among all source-destination connections
that involve this link. Another example is a multi-leg flight on an airline reservation system: to book the flight, seats on all legs must be committed simultaneously. We focus on a class of dynamic resource control on this
type of networks, where the link capacities are allocated among the job classes, in each state of the network, according to the solution to a
utility maximization problem. We derive fluid and diffusion limits of the network under this type of (myopic) control policy. Furthermore, we identify a cost function that is minimized in the diffusion regime, thereby justifying the asymptotic optimality of the control. (Joint work with Hengqing Ye.)
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"Valuing Risky Projects using Mixed Asset Portfolio Selection Models"
Talk by Bert De Reyck, London Business School;
and Janne Gustafsson, Cheyne Capital Management Limited
April 14, 11 AM, Jacobs Rm. 561
We examine the valuation of projects in a setting where an investor can invest in a portfolio of private projects as well as in securities in financial markets, but where exact replication of project cash flows in financial markets is not necessarily possible. We consider both single-period and multi-period models, and develop an inverse optimization procedure for valuing projects in this setting. We show that the valuation procedure exhibits several important analytical properties, for example, that project values for a mean-variance investor converge towards prices given by the capital asset pricing model. We also conduct several numerical experiments.
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"An Asymptotically-Optimal Dynamic Admission Policy for a Revenue Management Problem"
Talk by Marty Reiman, Bell Labs
May 3, 4:00 PM, Jacobs Rm. 166
We consider the following canonical revenue management problem, which has been analyzed in the context of airline seat inventory control and has applications to other service industries and supply chain management. There are several resource types (legs), each of which has a fixed capacity (number of seats). There are several customer classes (routes), each with an associated arrival process, price and resource consumption vector. The aim is to make dynamic accept/reject decisions at customer arrival epochs to maximize the total expected revenue obtained over the finite horizon [0,T] subject to not exceeding the capacity of any of the resources.
We introduce a control policy motivated by fluid and diffusion limits (as the resource capacities and arrival rates grow large). Our control policy makes an initial resource allocation decision based on solving a linear program (LP). The solution of the LP yields the fraction of arrivals of each class to accept. We then form a ‘trigger function’ based on the difference between the actual and expected number of accepted customers of each class. When this trigger function exceeds a pre-set threshold a reoptimization is performed: An LP involving the remaining resource capacities and remaining time is solved. The solution of this LP is then used to control admissions over the remainder of the horizon. We show that this policy is asymptotically optimal on diffusion scale, a property that is not shared by other approaches such as booking limits and bid price control.
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"Blind Nonparametric Revenue Management"
Talk by Assaf Zeevi, Columbia
May 10, 12:00 PM, Jacobs Rm. 561
In most revenue management studies one assumes the decision maker knows the manner in which consumers react to prices. The most typical way to express this fact is to assume the demand function is known, and it is just as common to posit that it admits a simple parametric structure. So what happens if none of this holds?
To investigate this question we consider a general class of network revenue management problems, where the objective is to price multiple products so as to maximize expected revenues over a finite sales horizon. The decision maker observes realized demand over time, but is otherwise ``blind'' to the underlying demand function which maps prices into the instantaneous demand rate. Few structural assumptions are made with regard to the demand function, in particular, it need not admit any parametric representation. We introduce a general method for solving such blind revenue management problems which involves the classical trade off between exploration and exploitation. To evaluate the performance of the proposed method we compare the revenues it generates to those corresponding to the optimal dynamic pricing policy that knows the demand function a priori. While that may seem as a lofty benchmark, we prove that as the sales volume grows large the revenue loss is guaranteed to be small. A more loose interpretation might run as follows: in problems that involve high sales volume, the value of “full information” (or penalty for “blind” decision making) is not as significant as one might guess.
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"Portfolio Selection with Robust Estimates of Risk"
Talk by Victor DeMiguel, London Business School
June 7, 12:00 PM, Jacobs Rm. 561
It is well-known that mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Moreover, it has been demonstrated that estimation error in the sample mean is, for most real-world datasets, much larger than that in the sample covariance matrix. For this reason, recent research has focused on the minimum-variance portfolio, which relies only on estimates of the covariance matrix and thus usually performs better out-of-sample than mean-variance portfolios. But even minimum-variance portfolios are still quite sensitive to estimation error and have unstable weights that fluctuate substantially over time.
Jagannathan and Ma (2003} show that imposing shortselling constraints can help to alleviate this difficulty. In this paper, we explore a different mechanism to combat estimation error. Concretely, we show how to compute the policy that minimizescertain robust estimator of portfolio risk by solving a nonlinear program. We also give an analytical bound on the sensitivity of the resulting portfolio weights to changes in the distributional assumptions. Finally, our out-of-sample numerical results show that the portfolio weights of the proposed policies are more stable than those of the minimum-variance policy and that they usually perform better in terms of Sharpe ratio. Moreover, although the imposition of shortselling constraints does improve the performance of the minimum-variance policy, the proposed robust policies are more stable and usually perform better even in the presence of constraints.
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Winter 2006
The following speakers visited NU in winter 2006 for the Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

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"Resource flexibility in manufacturing systems"
Talk by Mark Lewis, Cornell
Jan. 18, 12 PM, Jacobs Rm. 561
Recent interest in an agile workforce and machine flexibility has lead to a new
wave of challenges in manufacturing systems. Classic questions need to be revisited such as where should flexible machines be allocated? how often should they be utilized? and can they be used to mitigate the challenge of machine failures or worker availability? In this talk we consider each of these problems as they relate to tandem queues. We begin by answering the question of where machines should be allocated and show that the classic
c-\mu-rule from parallel systems applies in the tandem queue setting. We then show that this extends to the case when workers might be available only temporarily. We also show that when there is complete control of the capacity decision this control is monotone in the number of customers in each queue. We conclude (time permitting) by showing that an "almost" monotone switching curve describes the optimal policy when machine reliability is considered.
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"Managing Customer Outrage: Focus Organizational Learning Efforts on Service Failure or Recovery?"
Talk by Michael Lapre, Vanderbilt
Jan. 25, 12 PM, Jacobs Rm. 561
As service failures are inevitable, firms must be prepared to recover from service failures, thereby turning angry, frustrated customers into loyal customers. Despite the compelling economics of customer loyalty, firms continue to struggle with service recovery. Should firms focus organizational learning efforts on reducing service failure or on reducing dissatisfaction with recovery? Drawing from the literatures on organizational learning, learning curves, and marketing, I hypothesize that dissatisfaction with recovery contributes more to the variation in customer outrage across firms than service failure does (H1), that a U-shaped function of operating experience explains more variation in dissatisfaction with recovery than in service failure (H2), and that heterogeneity in organizational learning curves explains more variation in dissatisfaction with recovery than in service failure (H3). The hypotheses are tested with quarterly data for nine major U.S. airlines over 11 years. All three hypotheses are supported. In the context of mishandling baggage, dissatisfaction with recovery explains 88% of the variation in customer outrage, whereas service failure explains only 12%. The empirical results suggest firms should pay more attention to organizational learning curves for service recovery.
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"Excess Inventory and Long-Term Stock Price Performance"
Talk by Vinod Singhal, Georgia Tech
Feb. 8, 12 PM, Jacobs Rm. 586
This paper estimates the long-run stock price effects of excess inventory using nearly 900 excess inventory announcements made by publicly traded firms during 1990-2002. It examines the stock price effects starting one year before through two years after the excess inventory announcement date. Statistically significant abnormal returns are observed during the year before the announcement and on announcement. There is no evidence of statistically significant abnormal return during the two years after the announcement. I estimate that the mean (median) abnormal return due to excess inventory is -37.22% (-27.03%). Negative abnormal returns are observed across industries, calendar time, firm size, and actions taken to deal with excess inventory. The evidence suggests that the stock market partially anticipates excess inventory situations, firms do not recover quickly from the negative effect of excess inventory, and the negative effect of excess inventory is economically and statistically significant.
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"Pricing Dynamics of Competitors Who Ignore Competition"
Talk by Anton Kleywegt, Georgia Tech
Feb. 15, 1:30 PM, Jacobs Rm. 586
A variety of demand models are widely used in revenue management, all of which are known to be inaccurate. We are interested in the dynamic behavior of systems in which revenue managers use these inaccurate models, they make decisions based on the models, observe data, and attempt to refine the models with the observed data. Most of this talk will describe a duopoly in which each seller models demand as a function of the prices of that seller only. That is, the demand models that sellers estimate with data, model the quantity demanded as a function of the prices of the seller, and these models do not include the prices of the other seller. Such simplified models are often used in revenue management practice, even when revenue managers are aware of the competition. We compare the resulting dynamical behavior with the outcomes in other settings, such as the equilibria of well informed competitors, the outcomes of collaborators, and the outcomes when there is asymmetric information.
This is joint work with Tito Homem-de-Mello at Northwestern University and Bill Cooper at the University of Minnesota.
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"Asymptotic Results in Single and Multiclass Type Queueing Networks"
Talk by David Gamarnik, MIT
Feb. 22, 1:30 PM, Jacobs Rm. 561
Stochastic queueing networks have a variety of industrial applications including services, call centers, data and communication networks, manufacturing and more recently business processes.
We will begin with some motivating examples of business workflow processes and the underlying performance analysis issues. Then we will continue by introducing stochastic single class and multiclass queueing networks. The principal question is whether the probability distribution of the queue lengths has exponentially fast decaying tails in steady-state. We establish that for single class queueing networks this is indeed the case. Moreover, we establish that the stationary distribution of the associated reflected diffusion process provides a valid heavy-traffic approximation of the underlying queueing network in steady-state.
The presence of multiclass structure makes the picture rather different. We
present an example of a network where the queue length exhibit an unexpected subexponential behavior. Thus, we show the slow decay of the tails can be a purely network effect. We will discuss the implication of these results for control designs.
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"Simple Models of Discreet Choice and Their Performance in Bandit Experiments"
Talk by Noah Gans, Wharton
March 8, 12 PM, Jacobs Rm. 561
Recent operations management papers model customers as solving multi-armed bandit problems, positing that consumers use a particular heuristic when choosing among suppliers. These papers then analyze the resulting competition among suppliers and mathematically characterize the equilibrium actions. There remains a question, however, as to whether the original customer models upon which the analyses are built are reasonable representations of actual consumer choice.
In this paper, we empirically investigate how well these choice rules match actual performance as people solve two-armed Bernoulli bandit problems. We find that some of the most analytically tractable models perform best in tests of model fit. We also find that the expected number of consecutive trials of a given supplier is increasing and convex its expected quality level, a result that is consistent with the models' predictions, as well as with loyalty effects described in the popular management literature.
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"Operational Benefits of Subscription Services"
Talk by Sunil Kumar, Stanford
March 15, 12 PM, Jacobs Rm. 561
In this talk we study a monopolistic firm that offers reusable products, or a service, to price and quality-of-service sensitive customers - a rental firm can be thought of as the canonical example. Customers' perception of quality is determined by their likelihood of obtaining the product or service immediately upon request. We study the alternatives of offering either a subscription option or a pay-per-use option from a profit-maximizing perspective. In order to do this we propose a Markovian model of how subscribers generate requests and use a standard Poisson model for the pay-per-use option. In a large market setting, under the assumption of exponential demand, we show that using the
subscription option is more profitable for the firm. Further, via a numerical study, we show that this assumption is not essential for the result to hold. However, we show that it is not necessarily true that the subscription option dominates the pay-per-use option on quality-of-service. The firm is able to manage the trade-off between price and quality-of-service better in the subscription option. Moreover, we show that the social welfare and the consumer surplus can also be higher in the subscription option, indicating that both the firm and the consumers can benefit from the subscription option.
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Fall 2005 Seminars
The following speakers visited NU for the Fall 2005 Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

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"Spectrum Sharing Games"
Talk by Randy Berry, NU Engineering Dept.
Sept. 21
In wireless networks a key consideration is how multiple users can share the available spectrum. This is especially true in unlicensed or open bands, where users may be deployed without any centralized frequency planning or control.
In this talk, we describe some simple models for sharing a given spectrum band. We discuss both a case where a "spectrum manager" controls access and a case where there is no manager and users implement a distributed algorithm to manage access. In the first case, we describe auction mechanisms where the users bid for spectrum access. We characterize the resulting equilibria and discuss iterative algorithms for reaching these.
In the second case, we give a distributed algorithm, in which users announce "price" signals that indicate their "cost" of interference. We relate this algorithm to a "fictitious" game, which in certain cases is supermodular. We use this relation to characterize the algorithms convergence. Extensions to multi-channel networks may also be discussed.
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"Managing Demand Uncertainty with Dual Supply Contracts"
Presentation by Feryal Erhun, Stanford
Oct. 5
We consider a single product, dual supply problem under a periodically-reviewed finite planning horizon. The downstream party, manufacturer, receives supply from two upstream parties, local and global suppliers with consecutive leadtimes (i.e., the leadtime of the global supplier is one period longer than that of the local supplier). The suppliers offer complementary contracts in terms of transfer prices and leadtimes; thus, the manufacturer faces a trade-off between the responsive local supplier and the cost-efficient global supplier. We model the manufacturer’s problem in two stages: (i) she first chooses a portfolio of contracts (one from each supplier) and reserves capacity levels (at the prices specified by the contracts) for the whole planning horizon; (ii) she then orders from the suppliers according to the terms of the contracts chosen in the previous stage. In our second-stage problem, we prove that a two-level modified base-stock policy is optimal for a wide range of transfer prices. With various analytical results and numerical analysis, we illustrate how the optimal policy parameters change with respect to problem parameters. A reserve-up-to policy is shown to be optimal for the
manufacturer’s capacity reservation problem. We also develop a methodology that can be used to explain diverse sourcing strategies (such as in-house vs. offshore production) practiced by many companies in various industries.
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"The Martingale Approach to Operational and Financial Hedging"
Talk by Rene Caldentey, NYU
Oct. 19
We consider the problem of maximizing the profits of a corporation when these profits depend in part on movements in the financial markets and/or economic indices. We propose a methodology for the optimal selection of dynamic operating and financial hedging strategies when the decision maker is risk averse or budget constrained.
Risk aversion is imposed through constraints on the feasible policies such as VaR, CVaR and budget constraints, among others. We apply our methodology to some standard operations problems including the popular newsvendor model and a supply chain procurement/inventory problem. We also identify circumstances in which the risk management constraints can effectively be ignored when solving for the optimal operating policy.
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"Maximizing Queueing Network Utility Subject to Stability"
Talk by Sasha Stolyar, Bell Laboratories
Oct. 26 at 11:00 AM
Jacobs Center Rm. 1246
We study a model which accommodates a wide range of seemingly very different resource allocation problems in communication networks. Some examples: utility based congestion control of complex time-varying (wireless) networks, minimizing average power consumption in wireless networks, scheduling in wireless systems subject to power consumption and/or traffic rate constraints.
The model is a controlled queueing network, where controls have dual effect. In addition to determining exogenous customer arrival rates, service rates at the nodes, and (possibly random) routing of customers among the nodes, each control decision produces a certain vector of "commodities." The set of available control choices depends on the underlying random network mode. Network "utility" is a concave function of the vector of long-term average rates at which commodities are produced. The goal is maximize utility while keeping network queues stable. We introduce a very parsimonious dynamic control policy, called Greedy Primal-Dual algorithm, and prove its asymptotic
optimality. Although the model is formulated in terms of a queueing network, the algorithm can be viewed as a dynamic mechanism for solving rather general convex optimization problems.
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"Self-Organizing Cyclic Logistics Systems"
Talk by Don Eisenstein, University of Chicago
Nov. 9 at 1:00 PM
Jacobs Center Rm. G05
A self-organizing system is one in which the actions of decentralized entities combine to elicit stable global behavior. We seek rules that make systems "gravitate" to a balance point after a shock or perturbation.
We review our ideas on self-balancing production lines, and how they can lead to newer models of self-organization of cyclic systems.
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"Optimal Vote Trading"
Talk by Dave Hartvigsen, Notre Dame
Nov. 30 at 11:00 AM
Jacobs Center Rm. 561
During the 2000 U.S. Presidential race an apparently new idea, called vote trading, was introduced to help one of the two major-party candidates (Gore) win. The idea was, through an Internet mechanism, to induce voters who supported a minor-party candidate (Nader) to vote for Gore in states where this would help Gore and to induce an equal number of voters who supported Gore to vote for Nader in states where this would not hurt Gore. Thus Nader would receive the same number of popular votes as he would have received without the trading (providing an incentive for Nader voters to participate). Vote trading was implemented at a number of Web sites in 2000 (and again in 2004). In this talk, we formalize this idea, present several variations, and present an optimal way for Web sites to implement it (so as to best help the major-party candidate get elected) in both deterministic and stochastic settings.

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