Strategic Timing and Dynamic Pricing for Online Resource Allocation
Name
SSRN-id3186931.pdf
Description
Submitted version
Size
2.63 MB
Format
Adobe PDF
Checksum (MD5)
0f93324ddf71801d6f993f314ebee63f
Author(s)
Abhishek, Vibhanshu
Dogan, Mustafa
Jacquillat, Alexandre
Date Issued
2021
Journal
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Abhishek, Vibhanshu, Dogan, Mustafa and Jacquillat, Alexandre. 2021. "Strategic Timing and Dynamic Pricing for Online Resource Allocation." Management Science, 67 (8).
Version
Original manuscript
Abstract
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. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/MNSC.2020.3756
