Solving real-size stochastic railway rapid transit network construction scheduling problems

We propose an iterated greedy matheuristic for efficiently solving stochastic railway rapid transit transportation network construction scheduling problems, where both the construction duration of the segments and the passenger demand rate of increase are stochastic. The network construction scheduling problem consists of sequencing the construction of lines of a urban transportation network with the aim of maximizing the discounted long-term profit of the project. This problem can be described as a resource-constrained project scheduling problem, where both the budget and the available construction equipment act as resources influencing the schedule. We consider that partial lines can be put into operation as soon as they are finished, thus benefiting users with a partial and quick usage of the network infrastructure. This assumption makes both the costs and the revenues dependent on the schedule. After analyzing some characteristics of the best solutions, we propose an iterated greedy matheuristic for solving the stochastic version of real-size network construction scheduling problems. To illustrate our methodology we apply the algorithm to the construction of the full metro network of the city of Seville.