Asymptotically optimal dynamic pricing for network revenue management

Abstract

Open AccessOpen Access licenseAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Go to SectionOpen AccessOpen Access license HomeStochastic SystemsVol. 2, No. 2 Asymptotically Optimal Dynamic Pricing for Network Revenue ManagementRami Atar, Martin I. ReimanRami Atar, Martin I. ReimanPublished Online:21 Sep 2012https://doi.org/10.1287/12-SSY062AbstractA dynamic pricing problem that arises in a revenue management context is considered, involving several resources and several demand classes, each of which uses a particular subset of the resources. The arrival rates of demand are determined by prices, which can be dynamically controlled. When a demand arrives, it pays the posted price for its class and consumes a quantity of each resource commensurate with its class. The time horizon is finite: at time T the demands cease, and a terminal reward (possibly negative) is received that depends on the unsold capacity of each resource. The problem is to choose a dynamic pricing policy to maximize the expected total reward. When viewed in diffusion scale, the problem gives rise to a diffusion control problem whose solution is a Brownian bridge on the time interval [0, T]. We prove diffusion-scale asymptotic optimality of a dynamic pricing policy that mimics the behavior of the Brownian bridge.The ‘target point’ of the Brownian bridge is obtained as the solution of a finite dimensional optimization problem whose structure depends on the terminal reward. We show that, in an airline revenue management problem with no-shows and overbooking, under a realistic assumption on the resource usage of the classes, this finite dimensional optimization problem reduces to a set of newsvendor problems, one for each resource. Back to Top Next FiguresReferencesRelatedInformationCited ByConstant Regret Resolving Heuristics for Price-Based Revenue ManagementYining Wang, He Wang31 January 2022 | Operations Research, Vol. 0, No. 0A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue ManagementPornpawee Bumpensanti, He Wang16 March 2020 | Management Science, Vol. 66, No. 7Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time RequirementsYanzhe (Murray) Lei, Stefanus Jasin2 April 2020 | Operations Research, Vol. 68, No. 3Nonparametric Self-Adjusting Control for Joint Learning and Optimization of Multiproduct Pricing with Finite Resource CapacityQi (George) Chen, Stefanus Jasin, Izak Duenyas23 April 2019 | Mathematics of Operations Research, Vol. 44, No. 2Workload-Dependent Dynamic Priority for the Multiclass Queue with RenegingRami Atar, Anat Lev-Ari7 November 2017 | Mathematics of Operations Research, Vol. 43, No. 2Joint Dynamic Pricing and Order Fulfillment for E-commerce RetailersYanzhe (Murray) Lei, Stefanus Jasin, Amitabh Sinha27 March 2018 | Manufacturing & Service Operations Management, Vol. 20, No. 2Real-Time Dynamic Pricing with Minimal and Flexible Price AdjustmentQi (George) Chen, Stefanus Jasin, Izak Duenyas23 November 2015 | Management Science, Vol. 62, No. 8Reoptimization and Self-Adjusting Price Control for Network Revenue ManagementStefanus Jasin13 June 2014 | Operations Research, Vol. 62, No. 5 Volume 2, Issue 2December 2012Pages 232-446 Article Information Metrics Downloaded 133 times in the past 12 months Information Received:January 01, 2012Published Online:September 21, 2012 Copyright © 2012, The author(s)Cite asRami Atar, Martin I. Reiman (2012) Asymptotically Optimal Dynamic Pricing for Network Revenue Management. Stochastic Systems 2(2):232-276. https://doi.org/10.1287/12-SSY062 KeywordsRevenue managementdynamic pricingthe Gallego and Van Ryzin modelfluid optimization problemdiffusion control problemasymptotic optimalityBrownian bridgebridge policyPDF download