Research on Dynamic Optimization of Takeout Delivery Routes Considering Food Preparation Time

Abstract

The rapid development of the food delivery industry has imposed higher demands on optimizing delivery routes, especially with regard to addressing dynamic demand and stringent time constraints. As a critical factor impacting delivery efficiency, the food preparation time must be reasonably considered to optimize overall delivery routes effectively. Aiming at enhancing delivery efficiency by minimizing total delivery costs, a novel food delivery route optimization model was designed and constructed. This model specifically takes into account the impact of merchants’ food preparation times on the delivery process and improves upon a genetic algorithm based on clustering ideas for solving the problem. The clustering basis for obtaining initial solutions is calculated through the temporal and spatial similarity of orders. The feasibility of the algorithm is verified through designed computational examples. The simulation results demonstrate that the algorithm excels in reducing average delivery costs per order, decreasing the total mileage traveled by delivery personnel, and shortening average waiting times. Quantitative outcomes confirm that the new model can address dynamic demand issues, significantly reduce wait times for delivery personnel, and maximize platform revenue. Analysis of key parameters yields management insights that could provide references for operational decisions made by food delivery platforms, aiding in promoting environmental protection and sustainable development within the food delivery industry.