Optimization of train stopping plan, scheduling, seat allocation with social distancing strategies

The potential spread of infectious diseases, e.g. Influenza A, SARS, and COVID-19, has brought challenges to railway operations. One issue is how to improve railway utilization while implementing social distancing strategies. This study proposes to jointly optimize train stop planning, scheduling, and seat allocation when incorporating social distancing strategies (TSPS-SA-SD). The proposed approach incorporates heterogeneous social distancing requirements for passengers from areas associated with different levels of risks in epidemics/pandemics. To tackle the TSPS-SA-SD problem, an integer nonlinear programming (INLP) model is developed, which minimizes travel time and maximizes rail revenue while ensuring that social distancing strategies are respected. The proposed INLP is reformulated into an integer linear programming (ILP) model. Then, a decomposition-based heuristics solution (DHS) algorithm, which leverages the adaptive large neighborhood search (ALNS) and GUROBI solver, has been introduced to solve large instances. The proposed approach is evaluated on a small network and two real instances.