Confidence Level and Significance in Railway Emergency Resource Scheduling

A study from 2026 by Jianping Sun presents a dataset supporting an optimization model for railway emergency resource scheduling under uncertainty. The dataset likely contains parameters and results from a case study comparing the VEPSO algorithm to PSO, NSGA-II, VEGA, and MOEA/D. It is a 5.5 KB XLS file shared under a CC-BY-4.0 license on figshare.

Use Cases

• Benchmarking multi-objective optimization algorithms based on convergence efficiency and solution quality metrics.
• Analyzing the relationship between resource allocation satisfaction and confidence levels across different accident scenarios.
• Conducting sensitivity analysis on scheduling cost and time based on changes in resource demand scaling factors.
• Developing or validating fuzzy-random and interval programming models for resource scheduling in uncertain environments.

Strengths

• Includes specific performance metrics for the proposed VEPSO algorithm, such as an average of 385 convergence iterations and a 93.3% convergence success rate.
• Provides quantifiable results from sensitivity analysis, including a 16.0529% change in total scheduling cost and a 50% change in total scheduling time for a given demand scaling factor.

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

Jianping Sun via figshare.

Collection Method

Likely generated from a case study modeling railway emergency resource scheduling.

Freshness

Last updated 2026-05-19 17:30:44; freshness should be verified.