Computational intelligence for locating garbage accumulation points in urban scenarios

Abstract

This article presents computational intelligence methods for solving the problem of locating garbage accumulation points in urban scenarios, which is a relevant problem in nowadays smart cities to optimize budget and reduce negative environmental and social impacts. The problem model considers reducing the investment costs, enhance the proportion of citizens served by bins, and the accessibility to the system. A family of heuristics based on the generic PageRank schema and a mutiobjective evolutionary algorithm are proposed. Experimental evaluation performed on real scenarios on the city of Montevideo, Uruguay, demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives and improving over the current planning.