2352. Fairness and Efficiency for Scarce Resource Allocation
Invited abstract in session TC-11: Theory & Methods, stream Pricing and Revenue Management.
Thursday, 11:45-13:15Room: U2-200
Authors (first author is the speaker)
| 1. | Alejandro Lamas
|
| NEOMA Business School | |
| 2. | Hamed Jalali
|
| Department of information systems, supply chain management, and decision making, NEOMA Business school | |
| 3. | Mosayeb Jalilian
|
| Information Systems, Supply Chain Management & Decision Support, Neoma Business School |
Abstract
We consider a decision-maker that aims to balance revenues and fairness when allocating a scarce resource among two customer classes. Customers’ requests arrive stochastically. This problem fits to the allocation of public goods, like medical supplies, ticket to massive events and transportation, etc. Different to our work, current literature focuses on either fairness or revenues. We propose several fairness measures based on both demand fill rate and Rawls’ notion of fairness, i.e., we maximize the minimum expected fill rate and the expected minimum fill rate. For scenarios where requests of classes realize sequentially, we characterize both revenue optimal and fair allocations; we develop a dynamic programming approach for more complex environments in which arrivals pattern is uncertain. In addition, we study how to relax fairness considerations so managers can control the trade-off between revenues and allocation equity.
Keywords
- Revenue Management and Pricing
- Stochastic Models
- Dynamic Programming
Status: accepted
Back to the list of papers