Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models
<jats:p> We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected pro...
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2023
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| Online Access: | https://hdl.handle.net/1721.1/148650 |
| _version_ | 1826194555097055232 |
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| author | Qin, Hanzhang Simchi-Levi, David Wang, Li |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Qin, Hanzhang Simchi-Levi, David Wang, Li |
| author_sort | Qin, Hanzhang |
| collection | MIT |
| description | <jats:p> We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected profit over a finite horizon by matching the inventory level with a random demand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm’s sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is [Formula: see text], where T is the number of periods, and ϵ is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and show that the proposed data-driven algorithm solves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms. </jats:p><jats:p> This paper was accepted by J. George Shanthikumar, big data analytics. </jats:p> |
| first_indexed | 2024-09-23T09:57:33Z |
| format | Article |
| id | mit-1721.1/148650 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:57:33Z |
| publishDate | 2023 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1486502023-03-22T03:42:40Z Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models Qin, Hanzhang Simchi-Levi, David Wang, Li Massachusetts Institute of Technology. Department of Civil and Environmental Engineering <jats:p> We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected profit over a finite horizon by matching the inventory level with a random demand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm’s sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is [Formula: see text], where T is the number of periods, and ϵ is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and show that the proposed data-driven algorithm solves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms. </jats:p><jats:p> This paper was accepted by J. George Shanthikumar, big data analytics. </jats:p> 2023-03-21T16:52:06Z 2023-03-21T16:52:06Z 2022 2023-03-21T16:46:30Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148650 Qin, Hanzhang, Simchi-Levi, David and Wang, Li. 2022. "Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models." Management Science, 68 (9). en 10.1287/MNSC.2021.4212 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) SSRN |
| spellingShingle | Qin, Hanzhang Simchi-Levi, David Wang, Li Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title | Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title_full | Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title_fullStr | Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title_full_unstemmed | Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title_short | Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models |
| title_sort | data driven approximation schemes for joint pricing and inventory control models |
| url | https://hdl.handle.net/1721.1/148650 |
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