Routing in waste collection: A simulated annealing algorithm for an Argentinean case study

The management of the collection of Municipal Solid Waste is a complex task for local governments since it consumes a large portion of their budgets. Thus, the use of computer-aided tools to support decision-making can contribute to improve the efficiency of the system and reduce the associated cost...

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Autores principales:	Diego G. Rossit, Adrián A. Toncovich, Matías Fermani
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	municipal solid waste waste collection vehicle routing problem simulated annealing large neighborhood search genetic algorithm mixed-integer programming Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/ed70e36e8c3c4d8391b896f975f9212a
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spelling	oai:doaj.org-article:ed70e36e8c3c4d8391b896f975f9212a2021-11-29T06:26:54ZRouting in waste collection: A simulated annealing algorithm for an Argentinean case study10.3934/mbe.20214701551-0018https://doaj.org/article/ed70e36e8c3c4d8391b896f975f9212a2021-11-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021470?viewType=HTMLhttps://doaj.org/toc/1551-0018The management of the collection of Municipal Solid Waste is a complex task for local governments since it consumes a large portion of their budgets. Thus, the use of computer-aided tools to support decision-making can contribute to improve the efficiency of the system and reduce the associated costs, especially in developing countries, which usually suffer from a shortage of resources. In the present work, a simulated annealing algorithm is proposed to address the problem of designing the routes of waste collection vehicles. The proposed algorithm is compared to a commercial solver based on a mixed-integer programming formulation and two other metaheuristic algorithms, i.e., a state-of-the-art large neighborhood search and a genetic algorithm. The evaluation is carried out on both a well-known benchmark from the literature and real instances of the Argentinean city of BahȪa Blanca. The proposed algorithm was able to solve all the instances, having a performance similar to the large neighborhood procedure, while the genetic algorithm showed the worst results. The simulated annealing algorithm was also able to improve the solutions of the solver in many instances of the real dataset.Diego G. RossitAdrián A. ToncovichMatías FermaniAIMS Pressarticlemunicipal solid wastewaste collectionvehicle routing problemsimulated annealinglarge neighborhood searchgenetic algorithmmixed-integer programmingBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9579-9605 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	municipal solid waste waste collection vehicle routing problem simulated annealing large neighborhood search genetic algorithm mixed-integer programming Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	municipal solid waste waste collection vehicle routing problem simulated annealing large neighborhood search genetic algorithm mixed-integer programming Biotechnology TP248.13-248.65 Mathematics QA1-939 Diego G. Rossit Adrián A. Toncovich Matías Fermani Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
description	The management of the collection of Municipal Solid Waste is a complex task for local governments since it consumes a large portion of their budgets. Thus, the use of computer-aided tools to support decision-making can contribute to improve the efficiency of the system and reduce the associated costs, especially in developing countries, which usually suffer from a shortage of resources. In the present work, a simulated annealing algorithm is proposed to address the problem of designing the routes of waste collection vehicles. The proposed algorithm is compared to a commercial solver based on a mixed-integer programming formulation and two other metaheuristic algorithms, i.e., a state-of-the-art large neighborhood search and a genetic algorithm. The evaluation is carried out on both a well-known benchmark from the literature and real instances of the Argentinean city of BahȪa Blanca. The proposed algorithm was able to solve all the instances, having a performance similar to the large neighborhood procedure, while the genetic algorithm showed the worst results. The simulated annealing algorithm was also able to improve the solutions of the solver in many instances of the real dataset.
format	article
author	Diego G. Rossit Adrián A. Toncovich Matías Fermani
author_facet	Diego G. Rossit Adrián A. Toncovich Matías Fermani
author_sort	Diego G. Rossit
title	Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
title_short	Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
title_full	Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
title_fullStr	Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
title_full_unstemmed	Routing in waste collection: A simulated annealing algorithm for an Argentinean case study
title_sort	routing in waste collection: a simulated annealing algorithm for an argentinean case study
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/ed70e36e8c3c4d8391b896f975f9212a
work_keys_str_mv	AT diegogrossit routinginwastecollectionasimulatedannealingalgorithmforanargentineancasestudy AT adrianatoncovich routinginwastecollectionasimulatedannealingalgorithmforanargentineancasestudy AT matiasfermani routinginwastecollectionasimulatedannealingalgorithmforanargentineancasestudy
_version_	1718407559651524608

Routing in waste collection: A simulated annealing algorithm for an Argentinean case study

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