Routing Optimization of Takeout Delivery Routes Under Joint Delivery Model of Drones, Occasional Drivers, and Riders

Takeout delivery route optimization is a challenging research topic in the field of e-commerce. In this paper, aiming at the problems such as limited service range, unreasonable allocation, and tight time windows in the takeout delivery process, we propose a collaborative delivery model with multi-distribution center cooperation, where both drones and occasional drivers collaborate with riders. That is, on the basis of using the joint delivery of drones and riders for long-distance orders, occasional drivers are used to deliver long-distance and some orders within the range, giving play to the advantages of the crowdsourcing model and complementing the drone delivery model to further utilize the advantages of both delivery methods. With the minimum delivery cost and the overall maximum customer satisfaction as the objective function, a model is constructed. In this paper, an improved adaptive large neighborhood search algorithm (IALNS) is designed to solve it. The affinity propagation (AP) clustering is adopted to generate the initial solution, combined with multiple destruction operators and repair operators. Meanwhile, the tabu search framework is embedded locally to optimize the sub-solutions, and the simulated annealing framework is embedded to expand the global search range. The experimental results show that this algorithm effectively improves the solution quality and efficiency. At the same time, compared with the rider-only delivery mode, it has been proven that this model can further reduce the delivery cost and improve customer satisfaction. Finally, sensitivity analysis further demonstrates the advantages of the crowdsourcing model.