A Symbolic Efficiency-Aware Cooperative Decision System for Heterogeneous Workers and Tasks in IoT-Enabled Last-Mile Logistics

In last-mile logistics, couriers are assigned to deliver orders to customers and conduct real-time generated pick-up orders. Meanwhile, salespeople are hired to conduct customer expansion tasks, i.e., visiting and attracting potential customers, to increase the market share. Effectively scheduling heterogeneous workers (i.e., couriers and salespeople) for heterogeneous tasks (i.e., delivery, pick-up, and customer expansion tasks) is crucial for enhancing operational efficiency and driving revenue growth. In the state-of-the-practice, couriers and salespeople are scheduled separately, leading to a sub-optimal task completion rate. Therefore, we explore a cooperation-based worker scheduling scheme, where couriers and salespeople can also conduct customer expansion and pick-up tasks, respectively. We formulate the problem as a Markov Decision Process and design a symbolic efficiency-aware cooperative decision system called ECOS for heterogeneous worker scheduling to optimize task completion rate. Firstly, a Customized Task-Worker Representation module is designed to predict the spatial-temporal distribution of pick-up tasks and capture workers’ task-specific efficiencies and heterogeneous task-worker distributions. Then, an Efficiency-aware Multi-agent Actor-Critic module is proposed to achieve long-term optimization of the sequential worker scheduling in complicated environments. We have implemented ECOS in one leading logistics company in China, and evaluation results demonstrate that ECOS effectively improves completion rates of heterogeneous tasks.