Toward Multi-sided Fairness: A Fairness-Aware Order Dispatch System for Instant Delivery Service

Zouying Cao, Lin Jiang, Xiaolei Zhou, Shilin Zhu, Hai Wang, Shuai Wang

Abstract: Instant delivery platforms, equipped with professional couriers to provide convenient delivery services, have emerged rapidly in many cities. For the benefit of platforms, many researchers focus more on maximizing overall efficiency but ignore individual fairness. Current fairness research in mobile systems mainly concentrates on one-sided or two-sided relationships, such as drivers and customers. However, instant delivery services have two new characteristics in fairness: (i) multi-stakeholder involvement, namely couriers, merchants and users should be considered comprehensively; (ii) more complicated matching relationship because of the concurrent dispatch mode, meaning one courier will handle multiple orders simultaneously. To handle this multi-sided fairness problem, our paper proposes a novel order dispatch system to balance the platform revenue and multi-stakeholder fairness. Motivated by the analysis of real-world datasets, we formulate the order dispatch problem as a sequential decision-making problem and incorporate multi-sided fairness into the decision criteria. Then, we design a multi-sided fairness-aware deep reinforcement learning algorithm to solve large-scale decision problem, with the fairness relying on Least Misery Fairness definition for users and Variance Fairness definition for couriers and merchants. Finally, extensive experiments show the effectiveness of our model in balancing multi-sided fairness among stakeholders and long-term profits of the whole platform.