IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v69y2021i2p560-573.html
   My bibliography  Save this article

Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters

Author

Listed:
  • Qi (George) Chen

    (London Business School, Regent’s Park, London NW1 4SA, United Kingdom;)

  • Stefanus Jasin

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 75080)

  • Izak Duenyas

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 75080)

Abstract

We consider joint learning and pricing in network revenue management (NRM) with multiple products, multiple resources with finite capacity, parametric demand model, and a continuum set of feasible price vectors. We study the setting with a general parametric demand model and the setting with a well-separated demand model. For the general parametric demand model, we propose a heuristic that is rate-optimal (i.e., its regret bound exactly matches the known theoretical lower bound under any feasible pricing control for our setting). This heuristic is the first rate-optimal heuristic for an NRM with a general parametric demand model and a continuum of feasible price vectors. For the well-separated demand model, we propose a heuristic that is close to rate-optimal (up to a multiplicative logarithmic term). Our second heuristic is the first in the literature that deals with the setting of an NRM with a well-separated parametric demand model and a continuum set of feasible price vectors.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:69:y:2021:i:2:p:560-573
    DOI: 10.1287/opre.2020.2078
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/opre.2020.2078
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2020.2078?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Omar Besbes & Assaf Zeevi, 2012. "Blind Network Revenue Management," Operations Research, INFORMS, vol. 60(6), pages 1537-1550, December.
    2. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2019. "Nonparametric Self-Adjusting Control for Joint Learning and Optimization of Multiproduct Pricing with Finite Resource Capacity," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 601-631, May.
    3. Victor F. Araman & René Caldentey, 2009. "Dynamic Pricing for Nonperishable Products with Demand Learning," Operations Research, INFORMS, vol. 57(5), pages 1169-1188, October.
    4. Vivek F. Farias & Benjamin Van Roy, 2010. "Dynamic Pricing with a Prior on Market Response," Operations Research, INFORMS, vol. 58(1), pages 16-29, February.
    5. Stefanus Jasin, 2014. "Reoptimization and Self-Adjusting Price Control for Network Revenue Management," Operations Research, INFORMS, vol. 62(5), pages 1168-1178, October.
    6. Yiwei Chen & Vivek F. Farias, 2013. "Simple Policies for Dynamic Pricing with Imperfect Forecasts," Operations Research, INFORMS, vol. 61(3), pages 612-624, June.
    7. Pelin Pekgün & Ronald P. Menich & Suresh Acharya & Phillip G. Finch & Frederic Deschamps & Kathleen Mallery & Jim Van Sistine & Kyle Christianson & James Fuller, 2013. "Carlson Rezidor Hotel Group Maximizes Revenue Through Improved Demand Management and Price Optimization," Interfaces, INFORMS, vol. 43(1), pages 21-36, February.
    8. Dev Koushik & Jon A. Higbie & Craig Eister, 2012. "Retail Price Optimization at InterContinental Hotels Group," Interfaces, INFORMS, vol. 42(1), pages 45-57, February.
    9. Josef Broder & Paat Rusmevichientong, 2012. "Dynamic Pricing Under a General Parametric Choice Model," Operations Research, INFORMS, vol. 60(4), pages 965-980, August.
    10. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Woonghee Tim Huh & Michael Jong Kim & Meichun Lin, 2022. "Bayesian dithering for learning: Asymptotically optimal policies in dynamic pricing," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3576-3593, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boxiao Chen & Xiuli Chao & Cong Shi, 2021. "Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 726-756, May.
    2. Yiwei Chen & Cong Shi, 2023. "Network revenue management with online inverse batch gradient descent method," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2123-2137, July.
    3. Athanassios N. Avramidis & Arnoud V. Boer, 2021. "Dynamic pricing with finite price sets: a non-parametric approach," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 94(1), pages 1-34, August.
    4. Athanassios N. Avramidis, 2020. "A pricing problem with unknown arrival rate and price sensitivity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 92(1), pages 77-106, August.
    5. Stefanus Jasin, 2014. "Reoptimization and Self-Adjusting Price Control for Network Revenue Management," Operations Research, INFORMS, vol. 62(5), pages 1168-1178, October.
    6. Ruben Geer & Arnoud V. Boer & Christopher Bayliss & Christine S. M. Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbjørn Nilsen Ris, 2019. "Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(3), pages 185-203, June.
    7. N. Bora Keskin & Assaf Zeevi, 2017. "Chasing Demand: Learning and Earning in a Changing Environment," Mathematics of Operations Research, INFORMS, vol. 42(2), pages 277-307, May.
    8. Ningyuan Chen & Guillermo Gallego, 2021. "Nonparametric Pricing Analytics with Customer Covariates," Operations Research, INFORMS, vol. 69(3), pages 974-984, May.
    9. Ruben van de Geer & Arnoud V. den Boer & Christopher Bayliss & Christine Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbj{o}rn Nil, 2018. "Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference," Papers 1804.03219, arXiv.org.
    10. Ningyuan Chen & Guillermo Gallego, 2018. "A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint," Papers 1812.09234, arXiv.org, revised Oct 2021.
    11. Hamsa Bastani & David Simchi-Levi & Ruihao Zhu, 2022. "Meta Dynamic Pricing: Transfer Learning Across Experiments," Management Science, INFORMS, vol. 68(3), pages 1865-1881, March.
    12. 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.
    13. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    14. Yang, Chaolin & Xiong, Yi, 2020. "Nonparametric advertising budget allocation with inventory constraint," European Journal of Operational Research, Elsevier, vol. 285(2), pages 631-641.
    15. den Boer, Arnoud V., 2015. "Tracking the market: Dynamic pricing and learning in a changing environment," European Journal of Operational Research, Elsevier, vol. 247(3), pages 914-927.
    16. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2019. "Nonparametric Self-Adjusting Control for Joint Learning and Optimization of Multiproduct Pricing with Finite Resource Capacity," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 601-631, May.
    17. Arnoud V. den Boer & Bert Zwart, 2014. "Simultaneously Learning and Optimizing Using Controlled Variance Pricing," Management Science, INFORMS, vol. 60(3), pages 770-783, March.
    18. Maxime C. Cohen, & Georgia Perakis & Robert S. Pindyck, 2021. "A Simple Rule for Pricing with Limited Knowledge of Demand," Management Science, INFORMS, vol. 67(3), pages 1608-1621, March.
    19. Arnoud V. den Boer & N. Bora Keskin, 2020. "Discontinuous Demand Functions: Estimation and Pricing," Management Science, INFORMS, vol. 66(10), pages 4516-4534, October.
    20. Michael N. Katehakis & Yifeng Liu & Jian Yang, 2022. "A revisit to the markup practice of irreversible dynamic pricing," Annals of Operations Research, Springer, vol. 317(1), pages 77-105, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:69:y:2021:i:2:p:560-573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.