Bus-Assisted Drone Delivery Optimization Results

Numerical experiments on adapted Solomon instances compare the performance of an Improved Black-Kite Algorithm (IBKA) against Gurobi, a Genetic Algorithm, and other optimizers for a rural logistics problem. The 17.5 KB Excel file contains results from a study by Song Jin, published in April 2026. It models a bus-assisted heterogeneous-drone scheme for first-mile pickup and last-mile delivery in rural areas.

Use Cases

• Compare total delivery cost reduction metrics (e.g., 5% vs GA) across different optimization algorithms.
• Analyze stability improvement percentages (e.g., 23% vs BKA) from the IBKA's enhancements like opposition-based learning.
• Examine sensitivity test results on key parameters like drone payload and bus headway.
• Evaluate real-world case study results from the Xunyang–Tongqianguan line for cost savings over truck-only delivery.

Strengths

• Results benchmarked against established solvers like Gurobi and metaheuristics like Genetic Algorithm.
• Includes a real-world case study from a specific rural bus line in Shaanxi Province.
• Algorithm performance quantified with specific percentage improvements in cost and stability.

Limitations

• Dataset is extremely small at 17.5 KB, indicating limited raw data or a summary of results only.
• Specific row count, column names, and sample data are unavailable for direct analysis.
• The temporal and geographic coverage of the underlying problem instances is not explicitly stated.

Provenance

Source

Song Jin, via figshare.

Collection Method

Generated from numerical experiments on adapted Solomon instances and a real-world case study.

Time Range

null

Freshness

Last updated in April 2026.

Geography

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