Ablation Study Results for Bus-Assisted Drone Delivery Algorithm

Algorithm ablation study results compare the performance of an Improved Black-Kite Algorithm (IBKA) against other optimizers for a rural bus-assisted drone delivery model. The dataset contains numerical experiment outcomes from adapted Solomon instances and a real-world case study from Shaanxi Province. Song Jin authored the study, which was last updated in April 2026.

Use Cases

• Benchmark algorithm cost performance using total_delivery_cost reduction percentages against GA, EGO, and BKA.
• Analyze algorithm stability improvements using the provided stability enhancement metrics of 23%, 55%, and 23%.
• Evaluate the impact of drone payload and bus headway parameters from the sensitivity test results.
• Compare convergence speed and solution quality across the tested algorithms (Gurobi, GA, EGO, BKA, IBKA).
• Validate the proposed two-stage framework using results from the real-world Xunyang–Tongqianguan line case study.

Strengths

• Results include specific performance metrics: IBKA reduces cost by 5%, 9%, and 13% relative to three benchmark algorithms.
• Contains real-world validation from a specific logistics line in Shaanxi Province.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope and likely a summary of results rather than raw experimental data.
• Specific row counts, column names, and the full experimental data are not provided.
• The temporal coverage of the underlying case study data is unknown.

Provenance

Source

figshare, authored by Song Jin.

Collection Method

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

Time Range

null

Freshness

Last updated in April 2026.

Geography

Includes a real-world study on the Xunyang–Tongqianguan line in Shaanxi Province, China.