10.1007/s10472-019-09647-5
https://zenodo.org/records/4574025
oai:zenodo.org:4574025
Jamal Toutouh
Jamal Toutouh
0000-0003-1152-0346
Massachusetts Institute of Technology
Diego Gabriel Rossit
Diego Gabriel Rossit
0000-0002-8531-445X
Universidad Nacional del Sur
Sergio Nesmachnow
Sergio Nesmachnow
0000-0002-8146-4012
Universidad de la Repúilica
Soft computing methods for multiobjective location of garbage accumulation points in smart cities
Zenodo
2019
Smart cities
Municipal solid waste
Multiobjetive optimization
2019-06-20
eng
Published
Creative Commons Attribution 4.0 International
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
European Commission
10.13039/501100000780
799078
NeCOL: An Innovative Methodology for Building Better Deep Learning Tools for Real Word Applications