  Motivated by the tactical planning level of demand adaptive public
transportation systems, we present the stochastic symmetric traveling salesman
problem with generalized latency (STSP-GL), a stochastic extension to the
symmetric traveling salesman problem with generalized latency (TSP-GL). The
STSP-GL aims to choose a subset of nodes of an undirected graph and determines
a Hamiltonian tour amongst those nodes, minimizing an objective function that
is a weighted combination of route design and passenger routing costs. These
nodes are selected to ensure that a predefined percentage of uncertain
passenger demand is served with a given probability. We formulate the STSP-GL
as a stochastic program and propose a branch-and-price algorithm for solving
its deterministic equivalent. We also develop a local search approach with
which we improve the performance of the B&P approach. We assess the efficiency
of the proposed methods on a set of instances from the literature. We
demonstrate that the proposed methods outperform a known benchmark, improving
upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that
solutions of the stochastic model are both more cost-effective and robust than
those of the deterministic model.
