Conclusions
Operations management, operations research and artificial
intelligence are capable of modeling and solving
selective municipal waste management problems, an area
belonging to the field of reverse logistics. One of the
problems encountered in the design of selective municipal
waste collection systems is modeled and solved by two
different approaches. The aim is to provide a better distribution
of collection points and thus improve the level of
recyclable products and the perceived quality of service.
The quality of the algorithms was tested with real-life instances,
obtaining similar results in allowable running times
for real applications, whereas the results obtained by the AI
approach are better for the instances in the literature.
It is important to point out that these algorithms should
be used in combination with both a geographical information
and decision support system to obtain an application
allowing a methodological approach to the design of municipal
waste management systems; to increase the collection
of recyclable products, and further adaptation to present legislation.
A tool with these characteristics facilitates the calculations
performed in the decision-making step for a given
management plan, which might not be solved in absence of
such a tool.