Random Instance Scale Configuration for High-Speed Rail Emergency Scheduling

A 5.5 KB dataset by Shuaixin Guo, last updated April 28, 2026, containing random instance configurations used to validate an emergency scheduling optimization framework for high-speed rail logistics. The data supports a Mixed-Integer Linear Programming model and an Adaptive Large Neighborhood Search algorithm designed for truck transshipment following network disruptions. It was used in a case study of the Zhengzhou-Qingdao Express Rail Line.

Use Cases

• Benchmarking optimization algorithms like Adaptive Large Neighborhood Search based on the described random/worst/Shaw/depot consolidation removal operators.
• Testing Mixed-Integer Linear Programming models for vehicle deployment point selection and route planning under timeliness constraints.
• Comparing solution quality and computational efficiency between heuristic algorithms and exact solvers like CPLEX, as referenced in the description.
• Studying multi-level collaborative decision-making for 'vehicle deployment point selection - truck scheduling - goods transshipment' in disrupted logistics networks.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting broad reuse.
• The underlying model validation achieved a solution gap of only 0.037% compared to CPLEX, as stated in the description.
• Data is associated with a peer-reviewed research methodology involving a specific case study (Zhengzhou-Qingdao Express Rail Line) and multi-scale random instances.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope or a parameter/config file rather than extensive instance data.
• Row count and specific column definitions are unknown, requiring manual inspection after download to understand the data structure.
• Description metadata is limited; actual data quality and applicability require manual verification.

Provenance

Source

Shuaixin Guo via figshare

Collection Method

Generated as random instance configurations for model validation in a research study.

Freshness

Last updated 2026-04-28 17:25:27; freshness should be verified.

Geography

Potentially includes data related to the Zhengzhou-Qingdao Express Rail Line in China.