Crowdsourcing Last-Mile Deliveries
研究了利用独立众包司机满足有保证送达时间窗口的按需配送系统，通过鲁棒优化模型确定最优配送分配和时薪，帮助企业在不确定性下降低成本并保证准时送达。
Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.