Optimizing the long-term operating plan of railway marshalling station for capacity utilization analysis.

Area of Science:

• Operations Research
• Transportation Engineering
• Logistics Management

Background:

• Marshalling station operating plans are crucial for organizing operations and analyzing facility capacity utilization.
• Optimizing these plans is essential for efficient railway yard management and resource allocation.

Purpose of the Study:

• To develop and optimize a long-term operating plan for marshalling stations focused on enhancing capacity utilization.
• To minimize the average staying time of railcars within the marshalling yard.

Main Methods:

• A mathematical model was formulated to minimize railcar average staying time, subject to constraints like minimum time intervals and marshalling track capacity.
• A hybrid algorithm combining a genetic algorithm (GA) and simulation was designed to solve the optimization model efficiently.
• The planning horizon was divided into sub-plans, optimized sequentially using GA to reduce computation time.

Main Results:

• The developed algorithm effectively minimizes railcar average staying time, contributing to better capacity utilization.
• Numerical examples demonstrated the algorithm's convergence and the impact of its parameters on performance.
• Analysis showed the influence of arrival train flow on the optimization process and overall efficiency.

Conclusions:

• The optimized long-term operating plan significantly improves marshalling station capacity utilization.
• The GA-based algorithm offers an efficient approach to solving complex railway yard scheduling problems.
• The methodology provides valuable insights for enhancing railway logistics and operational management.