Train timetabling with rolling stock assignment, short-turning and skip-stop strategy for a bidirectional metro line

Metro train operations are becoming more challenging due to overcrowding and irregular passenger demand. To avoid passenger dissatisfaction, metro operators employ various operational strategies to increase the number of train services using limited number of trains. This paper integrates metro timetabling with several operational strategies to improve passenger services with limited number of trains. We propose three optimization models for timetable planning during both peak and off-peak hours. While the first and second models minimize operational costs, and passenger waiting and travel times, respectively, the third model is a multi-objective model that considers both the objectives simultaneously. These models integrate operational strategies such as rolling-stock assignment, short-turning, and skip-stopping to increase the number of services with limited trains on a bidirectional metro line. To solve the multi-objective model, we propose a Hybrid epsilon-Constraint Logic-Based Benders Decomposition (Hybrid ε-LBBD) approach, which achieves orders-of-magnitude reduction in computation time when compared to the classical epsilon-constraint method. Further, we perform computational experiments for both time-invariant and time-dependent demand arrival patterns. Finally, we also establish relationship of our model with classical queueing theory and thereby propose a Queue-Integrated Fixed-Point Algorithm to reduce congestion. The proposed framework is tested on a simplified version of Santiago Metro Line 1.