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Capacitated waste collection problem solution using an open-source tool

2023Artigo científico Acesso aberto
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dc.contributor.authorSilva, Adriano S.
dc.contributor.authorAlves, Filipe
dc.contributor.authorDíaz de Tuesta, Jose Luis
dc.contributor.authorRocha, Ana Maria A.C.
dc.contributor.authorPereira, Ana I.
dc.contributor.authorSilva, Adrián
dc.contributor.authorGomes, Helder
dc.date.accessioned2023-02-27T16:51:10Z
dc.date.available2023-02-27T16:51:10Z
dc.date.issued2023
dc.description.abstractPopulation in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems.pt_PT
dc.description.sponsorshipThis work has been supported by FCT—Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/05757/2020, UIDP/05757/2020, UIDB/00690/2020, UIDB/50020/2020, and LA/P/0007/2021. Adriano Silva was supported by FCT-MIT Portugal PhD grant SFRH/BD/151346/2021, and Filipe Alves was supported by FCT PhD grant SFRH/BD/143745/2019.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, Adriano S.; Alves, Filipe; Diaz de Tuesta, José Luis; Rocha, Ana Maria A.C.; Pereira, Ana I.; Silva, Adrián M.T.; Gomes, Helder (2023). Capacitated waste collection problem solution using an open-source tool. Computers. 12, p. 1-12pt_PT
dc.identifier.doi10.3390/computers12010015pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/27261
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationMountain Research Center
dc.relationLaboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials
dc.relationDistributed scheduling based on multi-agent system: a swarm approach for collaborative optimization
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWaste collectionpt_PT
dc.subjectMunicipal solid wastept_PT
dc.subjectWaste managementpt_PT
dc.subjectVehicle routing problempt_PT
dc.subjectRoute optimizationpt_PT
dc.subjectGoogle OR-Toolpt_PT
dc.titleCapacitated waste collection problem solution using an open-source toolpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleMountain Research Center
oaire.awardTitleLaboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials
oaire.awardTitleDistributed scheduling based on multi-agent system: a swarm approach for collaborative optimization
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oaire.citation.issue1pt_PT
oaire.citation.startPage15pt_PT
oaire.citation.titleComputerspt_PT
oaire.citation.volume12pt_PT
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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