Optimizing last-mile food delivery

Abstract

This study addresses the growing challenges in last-mile delivery logistics, particularly after the surge in demand for food delivery services post-COVID-19. Issues like increased traffic congestion, higher operational costs, and greater environmental impacts from frequent delivery trips highlight the need for optimized delivery strategies.

In response, this study develops a simulation model to optimize last-mile food delivery in residential areas, focusing on efficiency, sustainability, and cost-effectiveness. The model significantly reduces vehicle trips, meets strict delivery time windows, and lowers travel distances, thereby reducing carbon footprints. Future plans to replace petrol vehicles with electric ones are expected to further reduce emissions.

The model's robustness was tested against variables like speed changes and time delays, simulating real-world conditions. The results showed the model consistently met its goals under both controlled and dynamic factors.

The findings demonstrate the model's potential to enhance delivery logistics, laying the groundwork for scalable, resilient last-mile delivery systems adaptable to diverse urban environments.