Fairness and Privacy in the Optimal Transport for Resource Allocation

Faculty Mentor: Juntao Chen (Fordham University)


Optimal transport (OT) is a framework that facilitates the most efficient allocation of a limited amount of resources. It aims to maximize the aggregated utilities of all participants in a centralized manner, regardless of whether that distribution scheme is fair or privacy-preserving from the resource receivers’ perspective. This efficiency maximization OT paradigm is not suitable for many societal problems. For example, in energy systems, resilience planning should take into account these generally under-considered communities which are hit heavily by natural disasters. Though from the central planner’s perspective, the resilience planning in these areas may not contribute as significant as other areas to the system’s utility by cost-benefit analysis.

To this end, in this study, our goal is to incorporate fairness consideration during the transport mechanism design for constrained resource allocation, especially in the scenarios that promote social equity. We will also develop computationally light algorithms that can be applied to large-scale problems and implement them in a distributed fashion which preserves the privacy of participants to a certain extent.

Objectives and Learning Goals:

The students can achieve the following through this project:

  • Learn the basics of discrete optimal transport theory
  • Learn how to incorporate fairness/social equity in the resource allocation
  • Understand and design efficient algorithms for fair resource matching
  • Implement the algorithm and validate results using case studies