Correspondence: Aurélie Thiele, Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA 18015, USA. E-mail: aurelie.thiele@lehigh.edu

¹is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University in Bethlehem, PA. Her research is on decision-making under uncertainty, with applications to revenue and portfolio management. She is the recipient of an IBM Faculty Award and the first prize in the George Nicholson student paper competition organised by the Institute for Operations Research and Management Sciences. Her work is currently funded by multiple grants from the National Science Foundation and the Center for Engineering Logistics and Distribution, the nation's largest industry-university consortium on logistics. She holds an SM and a PhD in Electrical Engineering and Computer Science from MIT and a 'diplôme d'ingénieur' from the Ecole des Mines de Paris in France.

Received 20 September 2008; Revised 20 September 2008; Published online 5 December 2008.

Abstract

We propose an approach to model demand uncertainty in pricing problems with capacitated resources that builds upon: (i) range forecasts for various product lines and (ii) bounds on the amount of the resources that can be used by the random part of the cumulative demand. The bounds are adjusted to reflect the decision-maker's risk preferences. Although revenue management traditionally assumes that enough historical observations are available to estimate the underlying probability distributions accurately, the model of uncertainty presented in this work is particularly well suited to the level of information available in real-life settings, in particular in applications with long production lead times, short shelf life or brand new products. We derive robust counterparts to the deterministic pricing problem in the case of additive uncertainty, and analyse the impact of uncertainty and risk aversion on the decision-maker's strategy. In particular, when the price response function is linear or when uncertainty is small, we provide an explicit characterisation of the impact of the system parameters on the optimal strategy and establish the existence of a reference price for each product, which plays a key role in understanding how randomness affects the optimal prices.