Comparison of Multiobjective Evolutionary Algorithms for Prioritized Urban Waste Collection in Montevideo, Uruguay

Abstract

Urban waste collection is an important problem in modern cities, where efficient techniques are demanded to reduce large budgetary expenses, and avoid environmental and social problems. This article presents two state-of-the-art multiobjective evolutionary algorithms to solve a variant of the urban waste collection problem considering priorities and the conflicting goals of minimizing the total distance while maximizing the Quality of Service. The main results for real scenarios in Montevideo, Uruguay, show that accurate trade-off solutions outperformed greedy approaches, including the current routing methodology applied by local authorities. The competitiveness of the evolutionary algorithms was also confirmed when solving a prototype scenario using dynamic information.