EURO 2025 Leeds
Abstract Submission

407. Resource Allocation Optimization: Maximizing Revenue and Ensuring Fairness

Invited abstract in session MA-33: Social Networks and Decisions, stream Decision Analysis.

Monday, 8:30-10:00
Room: Maurice Keyworth 1.31

Authors (first author is the speaker)

1. Mosayeb Jalilian
Information Systems, Supply Chain Management & Decision Support, Neoma Business School
2. Hamed Jalali
Department of information systems, supply chain management, and decision making, NEOMA Business school
3. Mozart Menezes
Operations Management & Information Systems, NEOMA Business School -Bordeaux
4. Roel Leus
ORSTAT, KU Leuven

Abstract

Resource allocation involves distributing resources among customers who derive utility from them. Resources can be allocated either dynamically, over time, or statically in a single-shot process. This study examines revenue and fairness trade-offs by focusing on static multi-resource allocation problems. We consider two types of decision-makers: fairness-aware, who prioritize fairness among allocations with the same revenue, and fairness-unaware, who focus solely on revenue. To quantify this trade-off, we define sacrifice as the percentage reduction in fairness or revenue necessary to optimize the other objective. We identify two scenarios where fairness and revenue can be simultaneously optimized: when customers prefer more expensive resources or when the number of customers exceeds the number of resources, and all customers value having a resource. However, a trade-off between fairness and revenue is unavoidable where the total number of resources exceeds the number of customers and customers favor cheaper resources. Experimental results reveal that fairness-unaware decision-makers face a higher risk of poor fairness in popular events. Additionally, increasing the price of resource categories reduces fairness for customers preferring cheaper resources while minimally affecting those favoring expensive ones. Moreover, a diverse customer base with evenly distributed preferences amplifies the fairness-revenue trade-off for fairness-aware decision-makers.

Keywords

Status: accepted

Back to the list of papers