The waste collection problem is sto

The waste collection problem is stochastic by nature as the amount of MSW is highly variable and the accumulation of waste depends on several factors such as the number of inhabitants per container, GDP per capita, lifestyle, and season (Nuortio et al.,
2006). In spite of that, there is a generalized lack of reliable information
on the mass and volume of waste in individual containers.
Most of the works reported in literature used constant average values
for the model parameters within selected areas or even in the
whole area under study (e.g. Teixeira et al., 2004). To overcome
this, Nuortio et al. (2006) used the average accumulation rate of
waste in each container type estimated separately based on the
historical weight and route. Johansson (2006), in turn, introduced
a dynamic scheduling and routing for waste collection based on
the real-time information provided by a level sensor placed inside
the containers. Faccio et al. (2011) introduced an innovative framework
to implement the modern traceability devices in waste collection
(e.g. volumetric sensors, RFID, GPRS and GPS) and
presented a multi-objective routing model for waste collection
based on the integration of real-time traceability data inputs,
including real-time bin level status and real-time vehicle position.
With these approaches the obtained results for routing optimization
are more reliable than those obtained with static methods.
Although the implementation requires a considerable investment
in equipments, it can be compensated by the gained technical
and economic benefits (Faccio et al., 2011).