Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios

TL;DR

This paper presents an exact multi-objective optimization approach and heuristic methods based on PageRank for locating waste bins in urban areas, aiming to improve efficiency, reduce costs, and enhance accessibility in waste management.

Contribution

It introduces a novel multi-objective optimization framework and heuristic algorithms tailored for waste bin placement in real urban scenarios, considering multiple trade-offs.

Findings

The approaches effectively generate Pareto-efficient solutions.

Heuristic methods outperform some traditional techniques in real scenarios.

Results demonstrate the methods' competitiveness in urban waste management.

Abstract

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 Bah\'ia 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.