A Predictive-Reactive Optimization Framework With Feedback-Based Knowledge Distillation for On-Demand Food Delivery
Jie Zheng, Ling Wang, Jing-fang Chen, Zixiao Pan, Donghui Li, Yile Liang, Xuetao Ding
Abstract
On-demand food delivery (OFD) service is a representative scenario of last-mile logistics. It has gained a fast-growing market but also encounters many challenges, e.g., high dynamism, large-scale complexity, and immediacy requirement. To solve the OFD problem, a predictive-reactive optimization framework with feedback-based knowledge distillation is presented by organically combining deep learning technology and operational research method. In the prediction phase, a deep learning model is designed to predict future information which can reflect the delivery efficiency of dispatching results. To improve model performance, a feedback-based knowledge distillation is proposed which balances the diversity and effectiveness of the ensembled models by adaptively controlling learning weights. In the optimization phase, to avoid myopic decisions and obtain high-quality solutions for long-term objectives, a greedy heuristic with a multi-stage decision-making strategy is designed by employing the predicted future information to assist in making decisions. Extensive experiments are conducted on real-world datasets to test the performance of the proposed model and heuristic. Besides, the simulation results illustrate the superiority of the proposed framework for solving the OFD problem in both delivery efficiency and customer satisfaction.