This article describes the application of soft computing methods for solving
the problem of locating garbage accumulation points in urban scenarios. This is
a relevant problem in modern smart cities, in order to reduce negative
environmental and social impacts in the waste management process, and also to
optimize the available budget from the city administration to install waste
bins. A specific problem model is presented, which accounts for reducing the
investment costs, enhance the number of citizens served by the installed bins,
and the accessibility to the system. A family of single- and multi-objective
heuristics based on the PageRank method and two mutiobjective evolutionary
algorithms are proposed. Experimental evaluation performed on real scenarios on
the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates
the effectiveness of the proposed approaches. The methods allow computing
plannings with different trade-off between the problem objectives. The computed
results improve over the current planning in Montevideo and provide a
reasonable budget cost and quality of service for Bahia Blanca.

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