Sensitivity Analysis for Bus-Assisted Drone Delivery Optimization

5.5 KB Excel file containing sensitivity analysis results for parameter settings in a Differential Evolution hybrid operator. The analysis is part of a study on a bus-assisted heterogeneous-drone logistics model for rural e-commerce. Authored by Song Jin and last updated in April 2026.

Use Cases

• Analyze the influence of drone payload and bus headway parameters on total delivery cost and algorithm stability.
• Benchmark the performance of the Improved Black-Kite Algorithm (IBKA) against Gurobi, Genetic Algorithms, and other optimizers using cost and convergence metrics.
• Validate the proposed two-stage optimization framework (bus-stop clustering followed by IBKA) using adapted Solomon benchmark instances.
• Assess the operational advantages and cost savings of bus-drone collaboration models over truck-only delivery in specific geographic cases like the Xunyang–Tongqianguan line.

Strengths

• Results demonstrate a 5% to 13% reduction in total delivery cost compared to benchmark algorithms.
• Algorithm stability improvements of 23% to 55% are quantified against specific comparator methods.
• Model incorporates concrete constraints including bus schedules, drone heterogeneity, time windows, and battery limits.

Limitations

• Dataset is very small at 5.5 KB, indicating limited raw data or summary results only.
• Underlying numerical experiment data (e.g., full Solomon instance adaptations) is not included.
• Real-world study is confined to a single case (Xunyang–Tongqianguan line), limiting generalizability.

Provenance

Source

figshare, author Song Jin.

Collection Method

Numerical experiments on adapted Solomon instances and a real-world case study.

Time Range

null

Freshness

Updated in April 2026, indicating recent analysis.

Geography

Includes a case study on the Xunyang–Tongqianguan line in Shaanxi Province, China.