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Reoptimization and Self-Adjusting Price Control for Network Revenue Management

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  • Stefanus Jasin

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

We consider a standard dynamic pricing problem with finite inventories, finite selling horizon, and stochastic demands, where the objective of the seller is to maximize total expected revenue. We introduce a simple improvement of the popular static price control known in the literature. The proposed heuristic only requires a single optimization at the beginning of the selling horizon and does not require any reoptimization at all. This provides an advantage over the potentially heavy computational burden of reoptimization, especially for very large applications with frequent price adjustments. In addition, our heuristic can be implemented in combination with a few reoptimizations to achieve a high-level revenue performance. This hybrid of real-time adjustment and reoptimization allows the seller to enjoy the benefit of reoptimization without overdoing it.

Suggested Citation

  • Stefanus Jasin, 2014. "Reoptimization and Self-Adjusting Price Control for Network Revenue Management," Operations Research, INFORMS, vol. 62(5), pages 1168-1178, October.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:5:p:1168-1178
    DOI: 10.1287/opre.2014.1297
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    References listed on IDEAS

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    Cited by:

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    4. Pornpawee Bumpensanti & He Wang, 2020. "A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue Management," Management Science, INFORMS, vol. 66(7), pages 2993-3009, July.
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    7. Manmohan Aseri & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2018. "Procurement Policies for Mobile-Promotion Platforms," Management Science, INFORMS, vol. 64(10), pages 4590-4607, October.
    8. Yongbo Xiao, 2018. "Dynamic pricing and replenishment: Optimality, bounds, and asymptotics," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(1), pages 3-25, February.
    9. Yanzhe (Murray) Lei & Stefanus Jasin, 2020. "Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements," Operations Research, INFORMS, vol. 68(3), pages 676-685, May.
    10. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2016. "Real-Time Dynamic Pricing with Minimal and Flexible Price Adjustment," Management Science, INFORMS, vol. 62(8), pages 2437-2455, August.
    11. Joseph Jiaqi Xu & Peter S. Fader & Senthil Veeraraghavan, 2019. "Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets," Service Science, INFORMS, vol. 21(1), pages 121-138, January.
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    13. Yanzhe (Murray) Lei & Stefanus Jasin & Amitabh Sinha, 2018. "Joint Dynamic Pricing and Order Fulfillment for E-commerce Retailers," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 269-284, May.
    14. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2021. "Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters," Operations Research, INFORMS, vol. 69(2), pages 560-573, March.
    15. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    16. Xiangyu Gao & Stefanus Jasin & Sajjad Najafi & Huanan Zhang, 2022. "Joint Learning and Optimization for Multi-Product Pricing (and Ranking) Under a General Cascade Click Model," Management Science, INFORMS, vol. 68(10), pages 7362-7382, October.
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    18. Alberto Vera & Siddhartha Banerjee & Itai Gurvich, 2021. "Online Allocation and Pricing: Constant Regret via Bellman Inequalities," Operations Research, INFORMS, vol. 69(3), pages 821-840, May.

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