Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem

TL;DR

This paper introduces a novel genetic algorithm framework that simultaneously optimizes utility and two-sided fairness in dynamic vehicle routing problems, addressing a gap in existing single-sided fairness approaches.

Contribution

It proposes 2FairGA, a multi-objective genetic algorithm that incorporates two-sided fairness into DVRP, expanding beyond traditional utility-focused models.

Findings

2FairGA outperforms existing methods in fairness and efficiency

Injecting two fairness definitions improves overall system balance

The correlation analysis reveals trade-offs between utility and fairness

Abstract

Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation. Typically, DVRPs involve two stakeholders: service providers that deliver services to customers and customers who raise requests from different locations. Many real-world applications can be formulated as DVRP such as ridesharing and non-compliance capture. Apart from original objectives like optimising total utility or efficiency, DVRP should also consider fairness for all parties. Unfairness can induce service providers and customers to give up on the systems, leading to negative financial and social impacts. However, most existing DVRP-related applications focus on improving fairness from a single side, and there have been few works considering two-sided fairness and utility optimisation concurrently. To this end, we propose a novel framework, a Two-sided Fairness-aware Genetic Algorithm (named 2FairGA), which expands the genetic algorithm from the original objective solely focusing on utility to multi-objectives that incorporate two-sided fairness. Subsequently, the impact of injecting two fairness definitions into the utility-focused model and the correlation between any pair of the three objectives are explored. Extensive experiments demonstrate the superiority of our proposed framework compared to the state-of-the-art.