CREW ROSTERING SCHEDULE OPTIMIZATION FOR URBAN RAIL TRANSIT THROUGH RESOURCE SHARING

This study addresses the crew rostering problem for urban rail transit under fixed‐shift patterns, enhanced by inter‐line resource sharing. It suggests a novel mixed‐integer programming model based on a composite fatigue index accounting for driving time, non‐driving duties, off‐peak penalties, and insufficient rest intervals, enabling the minimization of short‐term imbalance of crew workload. Using an enhanced discrete Artificial Bee Colony (ABC) algorithm with specialized search strategies tailored to fixed‐shift cyclic patterns and resource‐sharing constraints, the authors demonstrate that the proposed approach yields more balanced crew schedules and reduces total crew requirements. As an example, we illustrate the proposed method on a three‐line metro network with two crew interchange stations. Our proposal improves operational efficiency by reducing total crew requirements by up to 4.5 % and fully accommodates individual crew preferences for start–end locations, thus enhancing both efficiency and crew satisfaction. The new method for effectiveness evaluation is confirmed by calculation of workload‐imbalance metrics and fatigue‐index reductions. The new research results develop existing crew rostering methodologies by integrating fatigue modeling and resource sharing, and can be used for crew scheduling optimization in urban rail systems. This study is novel because it is the first to combine composite fatigue index modeling with inter‐line resource sharing constraints in a fixed‐shift crew rostering model solved via an enhanced distinct ABC algorithm.