Revenue Optimization for a Make-to-Order Queue in an Uncertain Market Environment

Omar Besbes () and Costis Maglaras ()
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Omar Besbes: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Costis Maglaras: Graduate School of Business, Columbia University, New York, New York 10027

Operations Research, 2009, vol. 57, issue 6, 1438-1450

Abstract: We consider a revenue-maximizing make-to-order manufacturer that serves a market of price- and delay-sensitive customers and operates in an environment in which the market size varies stochastically over time. A key feature of our analysis is that no model is assumed for the evolution of the market size. We analyze two main settings: (i) the size of the market is observable at any point in time; and (ii) the size of the market is not observable and hence cannot be used for decision making. We focus on high-volume systems that are characterized by large processing capacities and market sizes, and where the latter fluctuate on a slower timescale than that of the underlying production system dynamics. We develop an approach to tackle such problems that is based on an asymptotic analysis and that yields near-optimal policy recommendations for the original system via the solution of a stochastic fluid model.

Keywords: revenue management; dynamic pricing; market uncertainty; queueing; state-dependent queues; asymptotic analysis (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (19)

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