Rolling stock planning for passenger trains based on ant colony optimization

Abstract

Railway companies in Japan are required to formulate further efficient passenger transportation and to reduce relevant costs because of competition against other transportations (air, bus, truck) and a decline in passenger. A railway rolling stock planning is one of the important scheduling in railway transport, which assigns physical train units to given time table services and determines a roster of the train units. This planning is usually designed with an expert's hand calculation. Therefore, an effective algorithm for the rolling stock planning has been developed. This paper proposes a novel approach based on Ant Colony Optimization to solve the planning. The proposed method can not only minimize the number of train units and deadheads, but also can consider a periodical inspection for the train units. The effectiveness of the proposed method is demonstrated through numerical experiments with instance problems made from real railway lines.