FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services

Lin Jiang, Shuai Wang, Baoshen Guo, Hai Wang, Desheng Zhang, Guang Wang

Abstract: In recent years, we have been witnessing a rapid prevalence of instant delivery services (e,g., UberEats, Instacart, and Eleme) due to their convenience and timeliness. A unique characteristic of instant delivery services is the concurrent dispatch mode, where (i) one courier usually simultaneously delivers multiple orders, especially during rush hours, and (ii) couriers can receive new orders when delivering existing orders. Most existing concurrent dispatch systems are efficiency-oriented, which means they usually dispatch a group of orders that have a similar delivery route to a courier. Although this strategy may achieve high overall efficiency, it also potentially causes a huge disparity of earnings between different couriers. To address the problem, in this paper, we design a Fairness-aware Concurrent dispatch system called FairCod, which aims to optimize the overall operation efficiency and individual fairness at the same time. Specifically, in FairCod, we design a Dynamic Advantage Actor-Critic algorithm with Fairness constrain (DA2CF). The basic idea is that it includes an Actor network to make dispatch decisions based on dynamic action space and a Critic network to evaluate the dispatch decisions from the fairness perspective. More importantly, we extensively evaluate our FairCod system based on one-month real-world data consisting of 36.38 million orders from 42,000 couriers collected by one of the largest instant delivery companies in China. Experimental results show that our FairCod improves courier fairness by 30.3% without sacrificing the overall system benefit compared to state-of-the-art baselines.