Strategic Timing and Dynamic Pricing for Online Resource Allocation
<jats:p> This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which s...
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144175 |
| _version_ | 1826188971583995904 |
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| author | Abhishek, Vibhanshu Dogan, Mustafa Jacquillat, Alexandre |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Abhishek, Vibhanshu Dogan, Mustafa Jacquillat, Alexandre |
| author_sort | Abhishek, Vibhanshu |
| collection | MIT |
| description | <jats:p> This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. </jats:p><jats:p> This paper was accepted by Gabriel Weintraub, revenue management and market analytics. </jats:p> |
| first_indexed | 2024-09-23T08:07:51Z |
| format | Article |
| id | mit-1721.1/144175 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:07:51Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1441752023-12-21T22:15:06Z Strategic Timing and Dynamic Pricing for Online Resource Allocation Abhishek, Vibhanshu Dogan, Mustafa Jacquillat, Alexandre Sloan School of Management <jats:p> This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. </jats:p><jats:p> This paper was accepted by Gabriel Weintraub, revenue management and market analytics. </jats:p> 2022-08-01T16:21:36Z 2022-08-01T16:21:36Z 2021 2022-08-01T16:14:51Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144175 Abhishek, Vibhanshu, Dogan, Mustafa and Jacquillat, Alexandre. 2021. "Strategic Timing and Dynamic Pricing for Online Resource Allocation." Management Science, 67 (8). en 10.1287/MNSC.2020.3756 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 | Abhishek, Vibhanshu Dogan, Mustafa Jacquillat, Alexandre Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title | Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title_full | Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title_fullStr | Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title_full_unstemmed | Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title_short | Strategic Timing and Dynamic Pricing for Online Resource Allocation |
| title_sort | strategic timing and dynamic pricing for online resource allocation |
| url | https://hdl.handle.net/1721.1/144175 |
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