In the setting of public transportation system, improving the service
quality as well as robustness against uncertainty through minimizing the total
waiting times of passengers is a real issue. This study proposed robust multi-objective
stochastic programming models for train timetabling problem in urban rail
transit lines. The objective is to minimize the expected value of the passenger
waiting times, its variance and the penalty cost function including the capacity
violation due to overcrowding. In the proposed formulations, the dynamic and
uncertain travel demand is represented by the scenario-based time-varying arrival
rates and alighting ratio at stops. Two versions of the robust stochastic programming
models are developed and a comparative analysis is conducted to testify the
tractability of the models. The effectiveness of the proposed stochastic programming
model is demonstrated through the application to line 5 of Tehran underground
railway. The outcomes validate the benefits of implementing robust
timetables for rail industry. The computational experiments shows significant
reductions in expected passenger waiting time of 21.27 %, and cost variance drop of
59.98 % for the passengers, through the proposed robust mathematical modeling
approach.