High-Speed Rail Emergency Logistics Scheduling Data for Model Validation

A 9.5 KB dataset supporting a study on emergency scheduling for high-speed rail logistics disruptions. It was created by Shuaixin Guo and last updated on 2026-04-28. The data likely contains parameters and results for validating a Mixed-Integer Linear Programming model and an Adaptive Large Neighborhood Search algorithm.

Use Cases

• Benchmarking optimization algorithms based on the described MILP model for truck transshipment.
• Validating emergency scheduling frameworks based on the case study of the Zhengzhou-Qingdao Express Rail Line disruption.
• Comparing solution quality and computation time between ALNS, CPLEX, and Genetic Algorithms as mentioned in the description.

Strengths

• Dataset is small (9.5 KB), facilitating quick download and inspection.
• Model validation is described using a specific case (Zhengzhou-Qingdao Express Rail Line) and multi-scale random instances.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Shuaixin Guo via figshare

Collection Method

Likely generated from simulation or model outputs for the described optimization study.

Freshness

Last updated 2026-04-28 17:25:26; freshness should be verified.

Geography

The study references a case involving the Zhengzhou-Qingdao Express Rail Line, suggesting a focus on China.