Balancing fairness and efficiency in order allocation for quick-commerce applications

Abstract

Last-mile delivery platforms operating in quick-commerce settings face an inherent tension between operational efficiency and equitable distribution of work among riders. We propose a multi-objective optimization framework for the dynamic, real-time assignment of delivery orders to a rider fleet, governed by a tunable parameter [Formula: see text] that interpolates between pure efficiency and perfect fairness. This reduces the dual objective to a unified minimum-cost assignment problem, solved at scale via a two-stage architecture that decouples route generation (VRP) from optimal rider assignment (Hungarian algorithm). Validated on real-world trip data from the NYC Yellow Taxi dataset over a simulated 24-hour horizon, the framework demonstrates that a 4–5[Formula: see text]% increase in mean delivery time relative to an efficiency-only baseline reduces earnings inequality measured by the Gini coefficient by 33[Formula: see text]%. The empirically convex shape of the fairness-efficiency frontier reveals that early gains in fairness are nearly cost-free, while the marginal efficiency cost accelerates only as perfect equality is approached. This convexity implies the existence of a principled operating point at which platforms capture the bulk of the equity benefit while preserving service guarantees, offering operators a transparent and quantifiable lever to balance customer service levels against sustainable rider welfare, a trade-off of growing regulatory significance in the gig economy.