Dynamic Pricing through Data Sampling
We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy th...
| Main Authors: | , , |
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| Format: | Article |
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Wiley
2019
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| Online Access: | http://hdl.handle.net/1721.1/120722 https://orcid.org/0000-0002-9428-7748 https://orcid.org/0000-0002-0888-9030 |
| _version_ | 1826209669921636352 |
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| author | Lobel, Ruben Cohen, Maxime Perakis, Georgia |
| author2 | Massachusetts Institute of Technology. Operations Research Center |
| author_facet | Massachusetts Institute of Technology. Operations Research Center Lobel, Ruben Cohen, Maxime Perakis, Georgia |
| author_sort | Lobel, Ruben |
| collection | MIT |
| description | We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue throughout the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show computationally that regret-based objectives can perform well when compared to average revenue maximization and to a Bayesian approach. The modeling approach proposed in this study could be particularly useful for risk-averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling-based solution. |
| first_indexed | 2024-09-23T14:26:37Z |
| format | Article |
| id | mit-1721.1/120722 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:26:37Z |
| publishDate | 2019 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1207222022-10-01T21:21:46Z Dynamic Pricing through Data Sampling Lobel, Ruben Cohen, Maxime Perakis, Georgia Massachusetts Institute of Technology. Operations Research Center Sloan School of Management Cohen, Maxime Perakis, Georgia We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue throughout the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show computationally that regret-based objectives can perform well when compared to average revenue maximization and to a Bayesian approach. The modeling approach proposed in this study could be particularly useful for risk-averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling-based solution. 2019-03-05T14:53:27Z 2019-03-05T14:53:27Z 2018-02 2019-02-27T16:23:04Z Article http://purl.org/eprint/type/JournalArticle 1059-1478 http://hdl.handle.net/1721.1/120722 Cohen, Maxime C., Ruben Lobel, and Georgia Perakis. “Dynamic Pricing through Data Sampling.” Production and Operations Management 27, no. 6 (March 1, 2018): 1074–1088. https://orcid.org/0000-0002-9428-7748 https://orcid.org/0000-0002-0888-9030 http://dx.doi.org/10.1111/POMS.12854 Production and Operations Management Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Other repository |
| spellingShingle | Lobel, Ruben Cohen, Maxime Perakis, Georgia Dynamic Pricing through Data Sampling |
| title | Dynamic Pricing through Data Sampling |
| title_full | Dynamic Pricing through Data Sampling |
| title_fullStr | Dynamic Pricing through Data Sampling |
| title_full_unstemmed | Dynamic Pricing through Data Sampling |
| title_short | Dynamic Pricing through Data Sampling |
| title_sort | dynamic pricing through data sampling |
| url | http://hdl.handle.net/1721.1/120722 https://orcid.org/0000-0002-9428-7748 https://orcid.org/0000-0002-0888-9030 |
| work_keys_str_mv | AT lobelruben dynamicpricingthroughdatasampling AT cohenmaxime dynamicpricingthroughdatasampling AT perakisgeorgia dynamicpricingthroughdatasampling |