  In this paper we present a multi-stage stochastic optimization model to solve
an inventory routing problem for recyclable waste collection. The objective is
the maximization of the total expected profit of the waste collection company.
The decisions are related to the selection of the bins to be visited and the
corresponding routing plan in a predefined time horizon. Stochasticity in waste
accumulation is modeled through scenario trees generated via conditional
density estimation and dynamic stochastic approximation techniques. The
proposed formulation is solved through a rolling horizon approach, providing a
worst-case analysis on its performance. Extensive computational experiments are
carried out on small- and large-sized instances based on real data provided by
a large Portuguese waste collection company. The impact of stochasticity on
waste generation is examined through stochastic measures, and the performance
of the rolling horizon approach is evaluated. Some managerial insights on
different configurations of the instances are finally discussed.
