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Pricing in Queues Without Demand Information

Author

Listed:
  • Moshe Haviv

    (Department of Statistics and Center of Rationality, Hebrew University of Jerusalem, 91905 Jerusalem, Israel)

  • Ramandeep S. Randhawa

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

We consider revenue optimization in an M / M /1 queue with price and delay sensitive customers, and we study the performance of demand-independent pricing that does not require any arrival rate information. We formally characterize the optimal demand-independent price and its performance relative to pricing with precise arrival rate knowledge. We find that demand-independent pricing can perform remarkably well and its performance improves as customers become more delay sensitive. In particular, for uniformly distributed customer valuations, under a large set of parameters, we find that demand-independent prices can capture more than 99% of the optimal revenue. We also study social optimization and find that demand-independent pricing can perform quite well; however, the performance is better under revenue optimization.

Suggested Citation

  • Moshe Haviv & Ramandeep S. Randhawa, 2014. "Pricing in Queues Without Demand Information," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 401-411, July.
    • Handle: RePEc:inm:ormsom:v:16:y:2014:i:3:p:401-411
    DOI: 10.1287/msom.2014.0479
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    References listed on IDEAS

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    Cited by:

    1. Shiliang Cui & Senthil Veeraraghavan, 2016. "Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion," Management Science, INFORMS, vol. 62(12), pages 3656-3672, December.
    2. Xinchang Wang & Sigrún Andradóttir & Hayriye Ayhan, 2019. "Optimal pricing for tandem queues with finite buffers," Queueing Systems: Theory and Applications, Springer, vol. 92(3), pages 323-396, August.
    3. Apostolos Burnetas, 2022. "Learning and data-driven optimization in queues with strategic customers," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 517-519, April.
    4. Jian Cao & Yongjiang Guo & Zhongxin Hu, 2023. "The Effect of Loss Preference on Queueing with Information Disclosure Policy," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-25, September.
    5. Pavlin, J. Michael, 2017. "Dual bounds of a service level assignment problem with applications to efficient pricing," European Journal of Operational Research, Elsevier, vol. 262(1), pages 239-250.
    6. Liu, Jian & Chen, Jian & Bo, Rui & Meng, Fanlin & Xu, Yong & Li, Peng, 2023. "Increases or discounts: Price strategies based on customers’ patience times," European Journal of Operational Research, Elsevier, vol. 305(2), pages 722-737.
    7. Vasiliki Kostami, 2020. "Price and Lead time Disclosure Strategies in Inventory Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2760-2788, December.
    8. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.
    9. Doan, Xuan Vinh & Lei, Xiao & Shen, Siqian, 2020. "Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications," European Journal of Operational Research, Elsevier, vol. 282(1), pages 235-251.
    10. Hassin, Refael & Haviv, Moshe & Oz, Binyamin, 2023. "Strategic behavior in queues with arrival rate uncertainty," European Journal of Operational Research, Elsevier, vol. 309(1), pages 217-224.
    11. Myron Benioudakis & Apostolos Burnetas & George Ioannou, 2022. "Single versus dynamic lead-time quotations in make-to-order systems with delay-averse customers," Annals of Operations Research, Springer, vol. 318(1), pages 33-65, November.
    12. Ying Chen & John J. Hasenbein, 2020. "Knowledge, congestion, and economics: Parameter uncertainty in Naor’s model," Queueing Systems: Theory and Applications, Springer, vol. 96(1), pages 83-99, October.

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