Swarm Intelligence Optimization for Solving Combinational Optimization Problems with Complex Constraints and Its Practical Application to Railway Transportation

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A real-world combinational optimization problem has many complicated constraints. In railway transportation systems, which need to consider operation of railway vehicles and their maintenance scheduling simultaneously, the combinational solutions which meet periodical maintenance constraints have to be constructed in optimization process. In this research, we developed the ant colony optimization-based approach as an efficient method which can treat periodical constraints, and applied it to a railway rolling stock planning for passenger trains as a real-world application. In addition, we implemented local searches in our approach and extended it for treating multi-objective problem. Then our approach was also extended for tackling the more complicated problem withtrain-sets composition.