An XGBoost-enhanced fast constructive algorithm for food delivery route planning problem

As e-commerce booms, online food ordering and delivery has attracted much attention. For food delivery platforms, planning high-quality routes for drivers so as to accomplish the delivery tasks efficiently is of great importance. This paper addresses a food delivery route planning problem (FDRPP), which considers one driver delivering multiple orders from restaurants to customers. Due to the immediacy of the delivery tasks, very limited computational time is provided for generating satisfactory solutions. We mathematically formulate the FDRPP and propose an Extreme Gradient Boosting-enhanced (XGBoost-enhanced) fast constructive algorithm to solve the problem. To construct a complete route, an insertion-based heuristic with different sequencing rules is adopted, together with an acceleration strategy based on geographic information to speed up the insertion procedure. In order to avoid the waste of computational time, we design an adaptive selection mechanism to select sequencing rules for route construction. A classification model using XGBoost is established to predict the performance of different sequencing rules. Through analysis of the route construction procedure, three types of problem-specific features are designed to improve the performance of XGBoost. The effectiveness of the proposed algorithm is demonstrated by conducting experiments on datasets from Meituan food delivery platform, which shows that large amounts of computational time can be saved by our proposed algorithm, while guaranteeing the quality of solutions.

Automated summary

This paper addresses a food delivery route planning problem (FDRPP) considering one driver delivering multiple orders from restaurants to customers. An Extreme Gradient Boosting-enhanced fast constructive algorithm is proposed, which combines an insertion-based heuristic with different sequencing rules and an acceleration strategy based on geographic information. An adaptive selection mechanism is designed to select sequencing rules, and a classification model using XGBoost is established to predict the performance of different sequencing rules, resulting in improved solution quality and computational time savings.