Robust stop-skipping at the tactical planning stage with Evolutionary Optimization.

The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from the expected ones. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies which are robust to travel time variations. In more detail, we show how historical travel time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of 5-month data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop skipping problem.