IDEAS home Printed from https://ideas.repec.org/a/ids/ijrevm/v9y2016i1p17-39.html
   My bibliography  Save this article

Mathematical model for revenue management under oligopolistic competition

Author

Listed:
  • Nur Aini Masruroh
  • Yun Prihantina Mulyani

Abstract

Revenue management (RM) models have been developed for products characterised as perishable with fixed capacity. This paper proposes mathematical models for RM under oligopolistic competition, using price as the sole factor that affects demand. These models are developed based on two different conditions. First, the competition among players is explicitly under deterministic and stochastic conditions with three possible scenarios namely deterministic oligopoly, sequential oligopoly and extended duopoly are evaluated. Second, the oligopoly competition is explained using customer valuation to capture demand changes towards price fluctuation. The models are then integrated with quantity-based RM to form the revenue model. Optimal pricing strategy is evaluated using game theory approach. All models are developed based on empirical case for hotel RM. The result shows that the model, which is developed using N-player game theory with 'cartel against single player' formation, is the most representative model with consistent ability to predict the actual system.

Suggested Citation

  • Nur Aini Masruroh & Yun Prihantina Mulyani, 2016. "Mathematical model for revenue management under oligopolistic competition," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 9(1), pages 17-39.
    • Handle: RePEc:ids:ijrevm:v:9:y:2016:i:1:p:17-39
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=76180
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Switgard Feuerstein, 2005. "Collusion in Industrial Economics—A Survey," Journal of Industry, Competition and Trade, Springer, vol. 5(3), pages 163-198, December.
    2. Dai, Yue & Chao, Xiuli & Fang, Shu-Cherng & Nuttle, Henry L.W., 2005. "Pricing in revenue management for multiple firms competing for customers," International Journal of Production Economics, Elsevier, vol. 98(1), pages 1-16, October.
    3. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2015. "Learning and Pricing with Models That Do Not Explicitly Incorporate Competition," Operations Research, INFORMS, vol. 63(1), pages 86-103, February.
    Full references (including those not matched with items on IDEAS)

    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. Hamed Jafari & Seyed Reza Hejazi & Morteza Rasti-Barzoki, 2016. "Pricing Decisions in Dual-Channel Supply Chain Including Monopolistic Manufacturer and Duopolistic Retailers: A Game-Theoretic Approach," Journal of Industry, Competition and Trade, Springer, vol. 16(3), pages 323-343, September.
    2. Kopányi, Dávid, 2017. "The coexistence of stable equilibria under least squares learning," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 277-300.
    3. António Brandão & Joana Pinho & Hélder Vasconcelos, 2014. "Asymmetric Collusion with Growing Demand," Journal of Industry, Competition and Trade, Springer, vol. 14(4), pages 429-472, December.
    4. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    5. Hatsor, Limor & Hashimzade, Nigar & Jelnov, Artyom, 2024. "Green antitrust conundrum: Collusion with social goals," MPRA Paper 122611, University Library of Munich, Germany.
    6. Justus Haucap & Christina Heldman, 2023. "On the sociology of cartels," European Journal of Law and Economics, Springer, vol. 56(2), pages 289-323, October.
    7. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    8. Tim Friehe, 2014. "Tacit collusion and liability rules," European Journal of Law and Economics, Springer, vol. 38(3), pages 453-469, December.
    9. Pio Baake & Vanessa Schlippenbach, 2011. "Quality distortions in vertical relations," Journal of Economics, Springer, vol. 103(2), pages 149-169, June.
    10. Savorelli, Luca, 2012. "Asymmetric cross-price effects and collusion," Research in Economics, Elsevier, vol. 66(4), pages 375-382.
    11. Pedro Mendi & Rafael Moner-Colonques & José Sempere-Monerris, 2011. "Vertical integration, collusion, and tariffs," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(3), pages 359-378, September.
    12. de Mesnard, Louis, 2009. "Is the French mobile phone cartel really a cartel?," International Journal of Production Economics, Elsevier, vol. 122(2), pages 663-677, December.
    13. Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
    14. Li, Juan & Tang, Ou, 2012. "Capacity and pricing policies with consumer overflow behavior," International Journal of Production Economics, Elsevier, vol. 140(2), pages 825-832.
    15. Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
    16. Doruk İriş & Luís Santos-Pinto, 2013. "Tacit Collusion under Fairness and Reciprocity," Games, MDPI, vol. 4(1), pages 1-16, February.
    17. Holt, Andrew Chase, 2024. "Monopsony power in the United States: Evidence from the great depression," Explorations in Economic History, Elsevier, vol. 92(C).
    18. Oliver Budzinski & Arndt Christiansen, 2007. "The Oracle/PeopleSoft Case: Unilateral Effects, Simulation Models and Econometrics in Contemporary Merger Control," Marburg Working Papers on Economics 200702, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    19. Klaus Friesenbichler & George Clarke & Michael Wong, 2014. "Price competition and market transparency: evidence from a random response technique," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 5-21, February.
    20. Fazel M. Farimani & Seyed Reza Mirnezami & Ali Maleki, 2019. "A Gas Cartel in the Global Market? Hype or Reality," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 296-304.

    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:ids:ijrevm:v:9:y:2016:i:1:p:17-39. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=99 .

    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.