Fair task allocation in transportation

TL;DR

This paper introduces a polynomial-time approach for fair task allocation in transportation, balancing cost efficiency with equitable distribution among diverse participants.

Contribution

It presents two novel algorithms that optimally solve the fair minimum cost allocation problem considering fairness, a new criterion in transportation task allocation.

Findings

Fairness can be integrated with minimal additional cost.

Proposed algorithms solve the problem optimally in polynomial time.

Experiments show fairness improves task distribution with small cost trade-offs.

Abstract

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost.