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.
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Acknowledgments
The work of J. Toutouh has been partially funded by MINECO and FEDER projects TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R, Spain. University of Malaga. International Campus of Excellence Andalucia TECH. The work of S. Nesmachnow is partly supported by ANII and PEDECIBA, Uruguay. The work of D. Rossit is partly funded by the Department of Engineering of Universidad Nacional del Sur, Argentina.
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Toutouh, J., Rossit, D., Nesmachnow, S. (2019). Computational Intelligence for Locating Garbage Accumulation Points in Urban Scenarios. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_34
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