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Joint Dynamic Pricing with Acquisition and Selling Opportunities

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  • Ghuloum, Mohammad
  • Song, Boqian
  • Aydin, Goker
  • Souza, Gilvan C.

Abstract

We analyze a novel dynamic pricing problem, where not only the demand depends on the dynamic selling price, but also the supply can be influenced through the dynamic acquisition price. This paper is motivated by firms that acquire and resell pre-owned items, such as cell phones. A firm with fixed inventory is always under pressure to sell the remaining inventory, yet when acquisition opportunities arise, the firm may become under pressure to acquire inventory in anticipation of a surge in demand. We establish conditions under which the firm will find itself under pressure to acquire versus pressure to sell. We study how the optimal acquisition and selling prices, and the difference between them (the optimal margin) behaves with respect to time and inventory. In addition, we devise heuristics with fixed prices based on a deterministic approximation, and we evaluate how much the firm gains by using a dynamic acquisition or selling price as opposed to fixing them at the beginning of the horizon.

Suggested Citation

  • Ghuloum, Mohammad & Song, Boqian & Aydin, Goker & Souza, Gilvan C., 2022. "Joint Dynamic Pricing with Acquisition and Selling Opportunities," European Journal of Operational Research, Elsevier, vol. 297(1), pages 252-267.
    • Handle: RePEc:eee:ejores:v:297:y:2022:i:1:p:252-267
    DOI: 10.1016/j.ejor.2021.04.052
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    References listed on IDEAS

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    1. Awi Federgruen & Aliza Heching, 1999. "Combined Pricing and Inventory Control Under Uncertainty," Operations Research, INFORMS, vol. 47(3), pages 454-475, June.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Gabriel R. Bitran & Susana V. Mondschein, 1997. "Periodic Pricing of Seasonal Products in Retailing," Management Science, INFORMS, vol. 43(1), pages 64-79, January.
    4. Ming Chen & Zhi-Long Chen, 2015. "Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information," Production and Operations Management, Production and Operations Management Society, vol. 24(5), pages 704-731, May.
    5. Mihai Banciu & Prakash Mirchandani, 2013. "Technical Note—New Results Concerning Probability Distributions with Increasing Generalized Failure Rates," Operations Research, INFORMS, vol. 61(4), pages 925-931, August.
    6. Mildenstein, Eckart & Schleef, Harold J, 1983. "The Optimal Pricing Policy of a Monopolistic Marketmaker in the Equity Market," Journal of Finance, American Finance Association, vol. 38(1), pages 218-231, March.
    7. Serhan Ziya & Hayriye Ayhan & Robert D. Foley, 2004. "Relationships Among Three Assumptions in Revenue Management," Operations Research, INFORMS, vol. 52(5), pages 804-809, October.
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    Cited by:

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    2. Nishihara, Michi, 2023. "Target-initiated takeover with search frictions," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1480-1497.

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