Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows
Descripción del Articulo
The delivery of products on time while reducing transportation costs has become an issue for retail companies in Latin America due to the rise of the e-commerce market in recent years. The Vehicle Routing Problem (VRP) is one of the most studied topics in operations research. This work addresses the...
Autores: | , , |
---|---|
Formato: | artículo |
Fecha de Publicación: | 2022 |
Institución: | Universidad Peruana de Ciencias Aplicadas |
Repositorio: | UPC-Institucional |
Lenguaje: | inglés |
OAI Identifier: | oai:repositorioacademico.upc.edu.pe:10757/660094 |
Enlace del recurso: | http://hdl.handle.net/10757/660094 |
Nivel de acceso: | acceso embargado |
Materia: | Ant colony optimization Capacitated vehicle routing problem with time windows K-means Vehicle routing problem Vehicle scheduling problem |
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UPC-Institucional |
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dc.title.es_PE.fl_str_mv |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
title |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
spellingShingle |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows Huamán, Airton Ant colony optimization Capacitated vehicle routing problem with time windows K-means Vehicle routing problem Vehicle scheduling problem |
title_short |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
title_full |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
title_fullStr |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
title_full_unstemmed |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
title_sort |
Multiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windows |
author |
Huamán, Airton |
author_facet |
Huamán, Airton Huancahuari, Marco Wong, Lenis |
author_role |
author |
author2 |
Huancahuari, Marco Wong, Lenis |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Huamán, Airton Huancahuari, Marco Wong, Lenis |
dc.subject.es_PE.fl_str_mv |
Ant colony optimization Capacitated vehicle routing problem with time windows K-means Vehicle routing problem Vehicle scheduling problem |
topic |
Ant colony optimization Capacitated vehicle routing problem with time windows K-means Vehicle routing problem Vehicle scheduling problem |
description |
The delivery of products on time while reducing transportation costs has become an issue for retail companies in Latin America due to the rise of the e-commerce market in recent years. The Vehicle Routing Problem (VRP) is one of the most studied topics in operations research. This work addresses the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The problem focuses on finding optimal routes for each vehicle to serve customers on time and minimal transportation costs under capacity and time constraints. Previous research has addressed the issue by proposing non-exact and exact techniques. This paper aims to select a proper approach and algorithms to present a model to solve the CVRPTW in real-world scenarios by incorporating a Google distance matrix, the empirical knowledge of delivery zones, and a solution relatively easy to deploy in a cloud environment. The proposed model consists of four phases: order scheduling, client clustering, delivery route generation, and operator assignment. We use the K-means algorithm to cluster customers and assign them to vehicles and the Ant Colony Optimization (ACO) algorithm to generate optimal routes. The proposed model was validated through a case study for a retail company in Lima, Perú. The results show that the proposed model reduces the route generation execution time by 95% of the average time. It also cuts travel distance and time by around 182 km and 532 min in 5-day periods. |
publishDate |
2022 |
dc.date.accessioned.none.fl_str_mv |
2022-06-03T16:20:56Z |
dc.date.available.none.fl_str_mv |
2022-06-03T16:20:56Z |
dc.date.issued.fl_str_mv |
2022-01-01 |
dc.type.es_PE.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.other.es_PE.fl_str_mv |
Articulo científico |
format |
article |
dc.identifier.issn.none.fl_str_mv |
18650929 |
dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-031-04447-2_10 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10757/660094 |
dc.identifier.eissn.none.fl_str_mv |
18650937 |
dc.identifier.journal.es_PE.fl_str_mv |
Communications in Computer and Information Science |
dc.identifier.eid.none.fl_str_mv |
2-s2.0-85128946489 |
dc.identifier.scopusid.none.fl_str_mv |
SCOPUS_ID:85128946489 |
dc.identifier.isni.none.fl_str_mv |
0000 0001 2196 144X |
identifier_str_mv |
18650929 10.1007/978-3-031-04447-2_10 18650937 Communications in Computer and Information Science 2-s2.0-85128946489 SCOPUS_ID:85128946489 0000 0001 2196 144X |
url |
http://hdl.handle.net/10757/660094 |
dc.language.iso.es_PE.fl_str_mv |
eng |
language |
eng |
dc.relation.url.es_PE.fl_str_mv |
https://link.springer.com/chapter/10.1007/978-3-031-04447-2_10 |
dc.rights.es_PE.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.es_PE.fl_str_mv |
application/html |
dc.publisher.es_PE.fl_str_mv |
Springer Science and Business Media Deutschland GmbH |
dc.source.es_PE.fl_str_mv |
Universidad Peruana de Ciencias Aplicadas (UPC) Repositorio Académico - UPC |
dc.source.none.fl_str_mv |
reponame:UPC-Institucional instname:Universidad Peruana de Ciencias Aplicadas instacron:UPC |
instname_str |
Universidad Peruana de Ciencias Aplicadas |
instacron_str |
UPC |
institution |
UPC |
reponame_str |
UPC-Institucional |
collection |
UPC-Institucional |
dc.source.journaltitle.none.fl_str_mv |
Communications in Computer and Information Science |
dc.source.volume.none.fl_str_mv |
1577 CCIS |
dc.source.beginpage.none.fl_str_mv |
141 |
dc.source.endpage.none.fl_str_mv |
157 |
bitstream.url.fl_str_mv |
https://repositorioacademico.upc.edu.pe/bitstream/10757/660094/1/license.txt |
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8a4605be74aa9ea9d79846c1fba20a33 |
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MD5 |
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Repositorio académico upc |
repository.mail.fl_str_mv |
upc@openrepository.com |
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2062ca47657d4766f00b3733ebfba6fc30082168270898273e207a4c1c6735b25e3300f1524a3bbf68b7e2680e1ab2f7ba0bfd300Huamán, AirtonHuancahuari, MarcoWong, Lenis2022-06-03T16:20:56Z2022-06-03T16:20:56Z2022-01-011865092910.1007/978-3-031-04447-2_10http://hdl.handle.net/10757/66009418650937Communications in Computer and Information Science2-s2.0-85128946489SCOPUS_ID:851289464890000 0001 2196 144XThe delivery of products on time while reducing transportation costs has become an issue for retail companies in Latin America due to the rise of the e-commerce market in recent years. The Vehicle Routing Problem (VRP) is one of the most studied topics in operations research. This work addresses the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The problem focuses on finding optimal routes for each vehicle to serve customers on time and minimal transportation costs under capacity and time constraints. Previous research has addressed the issue by proposing non-exact and exact techniques. This paper aims to select a proper approach and algorithms to present a model to solve the CVRPTW in real-world scenarios by incorporating a Google distance matrix, the empirical knowledge of delivery zones, and a solution relatively easy to deploy in a cloud environment. The proposed model consists of four phases: order scheduling, client clustering, delivery route generation, and operator assignment. We use the K-means algorithm to cluster customers and assign them to vehicles and the Ant Colony Optimization (ACO) algorithm to generate optimal routes. The proposed model was validated through a case study for a retail company in Lima, Perú. The results show that the proposed model reduces the route generation execution time by 95% of the average time. It also cuts travel distance and time by around 182 km and 532 min in 5-day periods.application/htmlengSpringer Science and Business Media Deutschland GmbHhttps://link.springer.com/chapter/10.1007/978-3-031-04447-2_10info:eu-repo/semantics/embargoedAccessUniversidad Peruana de Ciencias Aplicadas (UPC)Repositorio Académico - UPCCommunications in Computer and Information Science1577 CCIS141157reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCAnt colony optimizationCapacitated vehicle routing problem with time windowsK-meansVehicle routing problemVehicle scheduling problemMultiphase model based on K-means and ant colony optimization to solve the capacitated vehicle routing problem with time windowsinfo:eu-repo/semantics/articleArticulo científicoLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/660094/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/660094oai:repositorioacademico.upc.edu.pe:10757/6600942022-10-20 13:02:07.358Repositorio académico upcupc@openrepository.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 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).