Overbooking with Bounded Loss

Daniel Freund () and Jiayu (Kamessi) Zhao ()
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Daniel Freund: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Jiayu (Kamessi) Zhao: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142

Mathematics of Operations Research, 2023, vol. 48, issue 3, 1344-1363

Abstract: We study a classic problem in revenue management: quantity-based, single-resource revenue management with no-shows. In this problem, a firm observes a sequence of T customers requesting a service. Each arrival is drawn independently from a known distribution of k different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources B and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than B , the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, that is, its expected loss is independent of T . This improves upon prior works achieving Ω ( T ) guarantees.

Keywords: Primary: 90; 60; 91; online stochastic decision making; approximate dynamic programming; revenue management; overbooking; capacity management (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:48:y:2023:i:3:p:1344-1363

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