LAUSR

With the development of intelligent transportation technologies, on-demand food delivery has become a prevalent logistics service that brings convenience to our daily life. However, problems such as inappropriate assignment and delayed delivery still exist in current food delivery platforms. To address these issues, this paper presents a Crowdsourced Recommending-and-Grabbing (CRG) system for order allocation in on-demand food delivery service. Unlike other traditional logistics systems, the CRG system considers the preferences of crowdsourced riders and allocates orders by recommending them to suitable crowdsourced riders. Riders can grab their favorite orders from personalized order lists. To optimize the experience of both customers and crowdsourced riders, a hierarchical solution framework composed of prediction and optimization is proposed to generate satisfactory order allocation schemes. First at the prediction part, we predict the preferences of crowdsourced riders by establishing a predictive model using eXtreme Gradient Boosting (XGBoost). Then at the optimization phase, an allocating-and-sequencing algorithm is designed to appropriately expose orders to crowdsourced riders and sequence the order list for each rider. Extensive experiments are conducted on real-world datasets and the comparative results demonstrate the effectiveness of the proposed methods, suggesting that the presented CRG system is promising to facilitate the on-demand food delivery effectively and efficiently.