Apostolos Burnetas

Competition and Diversification Effects in Supply Chains with Supplier Default Risk

We study the effects of disruption risk in a supply chain where one retailer deals with competing risky suppliers who may default during their production lead times. The suppliers, who compete for business with the retailer by setting wholesale prices, are leaders in a Stackelberg game with the retailer. The retailer, facing uncertain future demand, chooses order quantities while weighing the benefits of procuring from the cheapest supplier against the advantages of order diversification. For the model with two suppliers, we show that low supplier default correlations dampen competition among the suppliers, increasing the equilibrium wholesale prices. Therefore the retailer prefers suppliers with highly correlated default events, despite the loss of diversification benefits. In contrast, the suppliers and the channel prefer defaults that are negatively correlated. However, as the number of suppliers increases, our model predicts that the retailer may be able to take advantage of both competition and diversification.

Option Pricing with Downward-Sloping Demand Curves: The Case of Supply Chain Options

This article investigates the role of option contracts in a supply chain when the demand curve is downward sloping. We consider call (put) options that provide the retailer with the right to reorder (return) goods at a fixed price. We show that the introduction of option contracts causes the wholesale price to increase and the volatility of the retail price to decrease. In general, options are not zero-sum games. Conditions are derived under which the manufacturer prefers to use options. When this happens the retailer is also better off, if the uncertainty in the demand curve is low. However, if the uncertainty is sufficiently high, then the introduction of option contracts alters the equilibrium prices in a way that hurts the retailer.

Quantity discounts in single-period supply contracts with asymmetric demand information

We investigate how supplier can use a quantity discount schedule to influence the stocking decisions of a downstream buyer that faces a single period of stochastic demand. In contrast to much of the work that has been done on single-period supply contracts, we assume that there are no interactions between the supplier and the buyer after demand information is revealed and that the buyer has better information about the distribution of demand than does the supplier. We characterize the structure of the optimal discount schedule for both all-unit and incremental discounts and show that the supplier can earn larger profits with an all-unit discount.

Adaptive Ordering and Pricing for Perishable Products

We consider the combined problem of pricing and ordering for a perishable product with unknown demand distribution and censored demand observations resulting from lost sales, faced by a monopolistic retailer. We develop an adaptive pricing and ordering policy with the asymptotic property that the average realized profit per period converges with probability one to the optimal value under complete information on the distribution. The pricing mechanism is modeled as a multiarmed bandit problem, while the order quantity decision, made after the price level is established, is based on a stochastic approximation procedure with multiplicative updates.

Future Capacity Procurements Under Unknown Demand and Increasing Costs

In this paper we study a situation in which a broker must manage the procurement of a short-life-cycle product. As the broker observes demand for the item, she learns about the demand process. However, as is often the case in practice, it becomes either more difficult or more expensive to procure the item as the selling season advances. Thus, the broker must trade off higher procurement costs against the benefit of making ordering decisions with better information about demand. Problems of this type arise, for example, in the travel industry, where a travel agents cost of procuring airline and hotel reservations increases as the date of a vacation package approaches. We develop a newsvendor-like characterization of the optimal procurement policy. In a numerical analysis, we demonstrate how broker procurements tend to cluster just before price increases and how brokers can benefit from explicitly considering the effects of information about demand in their ordering policies.

Mathematical Models for Studying the Value of Motivational Leadership in Teams

Mathematical models are presented for studying the value of leadership in a team where the members interact with each other. The models are based on a leader’s role of motivating each team member to perform closer to his/her maximum ability. These models include controllable parameters whose values reflect the amount of task interdependence among the workers as well as the motivational skill and variability in the skill of the leader. Confirming results—such as the fact that the skill level of the leader is a critical factor in the expected performance of the team—establish credibility in the models. Mathematical analysis and computer simulations are used to provide new managerial insights into the value of the leader—such as the fact that the skill of the leader can be more important than controlling the amount of interdependence among the team members and that having a choice of multiple leaders with no particular motivating skill is beneficial to the performance of small teams but not to large teams.

Customer equilibrium and optimal strategies in an M/M/1 queue with dynamic service control

We consider the problem of customer equilibrium strategies in an M/M/1 queue under dynamic service control. The service rate switches between a low and a high value depending on system congestion. Arriving customers do not observe the system state at the moment of arrival. We show that due to service rate variation, the customer equilibrium strategy is not generally unique, and derive an upper bound on the number of possible equilibria. For the problem of social welfare optimization, we numerically analyze the relationship between the optimal and equilibrium arrival rates as a function of various parameter values, and assess the level of inefficiency via the price of anarchy measure. We finally derive analytic solutions for the special case where the service rate switch occurs when the queue ceases to be empty.

Comparison of ISO-GUM and Monte Carlo methods for the evaluation of measurement uncertainty: Application to direct cadmium measurement in water by GFAAS

The propagation stage of uncertainty evaluation, known as the propagation of distributions, is in most cases approached by the GUM (Guide to the Expression of Uncertainty in Measurement) uncertainty framework which is based on the law of propagation of uncertainty assigned to various input quantities and the characterization of the measurand (output quantity) by a Gaussian or a t-distribution. Recently, a Supplement to the ISO-GUM was prepared by the JCGM (Joint Committee for Guides in Metrology). This Guide gives guidance on propagating probability distributions assigned to various input quantities through a numerical simulation (Monte Carlo Method) and determining a probability distribution for the measurand. In the present work the two approaches were used to estimate the uncertainty of the direct determination of cadmium in water by graphite furnace atomic absorption spectrometry (GFAAS). The expanded uncertainty results (at 95% confidence levels) obtained with the GUM Uncertainty Framework and the Monte Carlo Method at the concentration level of 3.01 μg/L were ±0.20 μg/L and ±0.18 μg/L, respectively. Thus, the GUM Uncertainty Framework slightly overestimates the overall uncertainty by 10%. Even after taking into account additional sources of uncertainty that the GUM Uncertainty Framework considers as negligible, the Monte Carlo gives again the same uncertainty result (±0.18 μg/L). The main source of this difference is the approximation used by the GUM Uncertainty Framework in estimating the standard uncertainty of the calibration curve produced by least squares regression. Although the GUM Uncertainty Framework proves to be adequate in this particular case, generally the Monte Carlo Method has features that avoid the assumptions and the limitations of the GUM Uncertainty Framework.

Understanding and attenuating the complexity catastrophe in Kauffman's N K model of genome evolution

Kauffmans N K model—used for studying the performance of systems consisting of a finite number of components that interact with each other in complex ways—exhibits the complexity catastrophe, in which high levels of interaction in systems with a large number of components lead to a decrease in performance. It is shown here that the complexity catastrophe is a consequence of the mathematical assumptions underlying the N K model. Analysis and simulations are used to establish the idea that relaxing any one of these assumptions results in a new model in which the complexity catastrophe is attenuated. Thus, good performance from systems having high levels of interactions is possible. ©1999 John Wiley & Sons, Inc.

ASYMPTOTIC BAYES ANALYSIS FOR THE FINITE-HORIZON ONE-ARMED-BANDIT PROBLEM

The multiarmed-bandit problem is often taken as a basic model for the trade-off between the exploration and utilization required for efficient optimization under uncertainty. In this article, we study the situation in which the unknown performance of a new bandit is to be evaluated and compared with that of a known one over a finite horizon. We assume that the bandits represent random variables with distributions from the one-parameter exponential family. When the objective is to maximize the Bayes expected sum of outcomes over a finite horizon, it is shown that optimal policies tend to simple limits when the length of the horizon is large.