Published March 11, 2021 | Version Submited
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Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios

  • 1. Universidad Nacional del Sur (UNS)-CONICET
  • 2. Massachusetts Institute of Technology
  • 3. Universidad de la República

Description

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bahía Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

Notes

This is the pre-print version of the paper published in the Waste Management journal. Cite as: Rossit, D. G., Toutouh, J., & Nesmachnow, S. (2020). Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios. Waste Management, 105, 467-481.

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Funding

NeCOL – NeCOL: An Innovative Methodology for Building Better Deep Learning Tools for Real Word Applications 799078
European Commission