Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem

• URL: http://arxiv.org/abs/2405.19184v1
• Date: Wed, 29 May 2024 15:24:28 GMT
• Title: Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem
• Authors: Yufan Kang, Rongsheng Zhang, Wei Shao, Flora D. Salim, Jeffrey Chan,
• Abstract summary: DVRPs involve two stakeholders: service providers that deliver services to customers and customers who raise requests from different locations. 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.
• Score: 21.148097893483406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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