Research on railway emergency resource scheduling strategy under multiple uncertainty coupling.

Area of Science:

• Operations Research
• Railway Engineering
• Optimization Theory

Background:

• Railway emergency resource scheduling faces significant uncertainty from various sources.
• Existing models often struggle to adequately address complex fuzzy and random variables.
• Accurate and efficient scheduling is critical for minimizing response times and costs.

Purpose of the Study:

• To develop a robust optimization framework for railway emergency resource scheduling under multiple uncertainties.
• To minimize both scheduling time and cost while ensuring high demand fulfillment credibility.
• To introduce an improved algorithm for efficiently finding Pareto-optimal solutions.

Main Methods:

• Utilized interval numbers for scheduling time uncertainty and fuzzy-random variables for resource demand.
• Integrated interval programming with fuzzy credibility constraints into a unified optimization model.
• Developed a constrained, multi-objective, variable neighborhood search algorithm (VEPSO) for efficient solution finding.

Main Results:

• The VEPSO algorithm demonstrated superior convergence efficiency and solution quality compared to PSO, NSGA-II, VEGA, and MOEA/D.
• Achieved an average of 385 convergence iterations (26% lower than MOEA/D) with a 93.3% success rate and 0.983 demand fulfillment credibility.
• Sensitivity analysis confirmed model robustness, showing stable, approximately linear responses to demand variations.

Conclusions:

• The proposed interval fuzzy credibility-constrained programming model and VEPSO algorithm effectively handle uncertainties in railway emergency scheduling.
• The satisfaction evaluation method provides a quantitative basis for dynamic strategy adjustments.
• Offers significant theoretical and practical value for decision support in uncertain railway environments.