Volume 65

Co-Optimization of Hybrid Renewable Energy Systems and Drone Fleets for Sustainable Last-Mile Logistics Zeeshan Zulfiqar, Lanyu Li, Shuhui Jia, Xiaonan Wang

Abstract

The rise in last-mile logistics has made the freight sector a major contributor to greenhouse gas emissions, threatening global sustainability goals. While drone-based delivery offers a cleaner alternative, its environmental benefits can only be realized with a robust, reliable clean energy infrastructure. However, existing studies often treat energy systems and logistics planning separately, limiting their decarbonization potential. This work addresses this gap by proposing a multi-objective co-optimization framework that simultaneously designs hybrid renewable energy systems (HRES) and drone delivery fleets. By integrating the Non-dominated Sorting Genetic Algorithm III (NSGA-III) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework identifies Pareto-optimal solutions that balance cost, reliability, and environmental performance. Case studies in Hainan and Shenzhen indicate that the optimized drone service consumes ≈ 0.50 MJ pkg⁻¹ km⁻¹ in both cities and fulfills 89% – 91% of annual delivery requests (with LPSP ≤ 5%). The HRES-powered fleet achieves ≈ 12.6 gCOâ‚‚e pkg⁻¹ km⁻¹ in off-grid Hainan and ≈ 0.67 gCOâ‚‚e pkg⁻¹ km⁻¹ in grid-connected Shenzhen, whereas a purely grid-charged drone in Shenzhen emits ≈ 69 gCOâ‚‚e pkg⁻¹ km⁻¹. This work introduces a novel co-optimization approach, bridging energy and logistics systems to create scalable, low-carbon, and resilient last-mile delivery solutions.

Keywords hybrid energy, drone logistics, last-mile delivery, energy storage, levelized cost, carbon reduction