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Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem

Yufan Kang, Rongsheng Zhang, Wei Shao, Flora D. Salim, Jeffrey Chan

TL;DR

The paper addresses DVRP under dynamic requests with a dual fairness concern, proposing 2FairGA to jointly optimize total utility and two fairness metrics for service providers and customers. It introduces constrained clustering-based initialization and Leader-based Random Keys Encoding to handle multi-agent dynamics, while modifying GA fitness to balance three objectives $U$, $F_{provider}$, and $F_{customer}$. The contributions include the first unified two-sided fairness framework within DVRP, empirical validation on ridesharing and non-compliance capture datasets, and ablation studies demonstrating the importance of initialization and fairness components. The results show that 2FairGA achieves favorable fairness-utility trade-offs and competitive efficiency relative to baselines that optimize only utility or fairness, indicating practical impact for fair and effective DVRP implementations.

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.

Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem

TL;DR

The paper addresses DVRP under dynamic requests with a dual fairness concern, proposing 2FairGA to jointly optimize total utility and two fairness metrics for service providers and customers. It introduces constrained clustering-based initialization and Leader-based Random Keys Encoding to handle multi-agent dynamics, while modifying GA fitness to balance three objectives , , and . The contributions include the first unified two-sided fairness framework within DVRP, empirical validation on ridesharing and non-compliance capture datasets, and ablation studies demonstrating the importance of initialization and fairness components. The results show that 2FairGA achieves favorable fairness-utility trade-offs and competitive efficiency relative to baselines that optimize only utility or fairness, indicating practical impact for fair and effective DVRP implementations.

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.
Paper Structure (20 sections, 9 equations, 2 figures, 6 tables, 1 algorithm)

This paper contains 20 sections, 9 equations, 2 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. Problem Formulation
  5. Ridesharing Systems and Fairness
  6. Non-Compliance Capture and Fairness
  7. Utility
  8. Service Provider-based Fairness
  9. Customer-based Fairness
  10. Methodology
  11. Initial Sampling
  12. Leader-based Random Keys Encoding
  13. Genetic Algorithm
  14. Experiments
  15. Datasets
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Flowchart of our algorithm.
  • Figure 2: Ablation study using Melbourne Parking dataset with different modules removed from 2FairGA to test the range of individual utility gained among service providers, which shows impact of different modules on range of fairness and utility.