Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo
Introduction— Vehicle routing scheduling with service compliance is a necessity for logistics companies in search of their competitive advantage. Objective— The objective of the following work is to determine the routing of vehicles with time windows for a homogeneous fleet applied to the last, mile...
- Autores:
-
Mantilla Mejía, Javier Darío
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8742
- Acceso en línea:
- https://hdl.handle.net/11323/8742
https://doi.org/10.17981/cesta.02.01.2021.05
https://repositorio.cuc.edu.co/
- Palabra clave:
- VRP applications
Heuristic
Saving matrix
Combinatorial optimization
VRPTW
Aplicaciones VRP
Heurística
Matriz de ahorro
Optimización combinatoria
- Rights
- openAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_d4b4e67ba91aebd5dada23b2702a0cfe |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/8742 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| dc.title.translated.eng.fl_str_mv |
The use of the swap operator generates efficient computational solutions in a vehicle routing case with a time window approach |
| title |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| spellingShingle |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo VRP applications Heuristic Saving matrix Combinatorial optimization VRPTW Aplicaciones VRP Heurística Matriz de ahorro Optimización combinatoria |
| title_short |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| title_full |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| title_fullStr |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| title_full_unstemmed |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| title_sort |
Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo |
| dc.creator.fl_str_mv |
Mantilla Mejía, Javier Darío |
| dc.contributor.author.spa.fl_str_mv |
Mantilla Mejía, Javier Darío |
| dc.subject.proposal.eng.fl_str_mv |
VRP applications Heuristic Saving matrix Combinatorial optimization VRPTW |
| topic |
VRP applications Heuristic Saving matrix Combinatorial optimization VRPTW Aplicaciones VRP Heurística Matriz de ahorro Optimización combinatoria |
| dc.subject.proposal.spa.fl_str_mv |
Aplicaciones VRP Heurística Matriz de ahorro Optimización combinatoria |
| description |
Introduction— Vehicle routing scheduling with service compliance is a necessity for logistics companies in search of their competitive advantage. Objective— The objective of the following work is to determine the routing of vehicles with time windows for a homogeneous fleet applied to the last, mile distribution case with 300 clients, considering the minimization of operating costs, distribution costs and, downtime costs. Methodology— The problem is approached through the approach of a mixed-integer linear programming mathematical model, and the development of an algorithm through the use of the savings method and the use of the swap operator. Results— In the construction phase, the savings algorithm achieves an initial cost focused on the minimum distance. In the upgrade phase, the swap operator improves the initially established solution, very quickly. For a case of 300 clients, 12 iterations were carried out, obtaining an improvement of 71.41% over the initial cost. Conclusions— For calculations of VRPTW cases with 300 nodes, the swap operator achieves computational times of less than 30 seconds. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-22T15:09:12Z |
| dc.date.available.none.fl_str_mv |
2021-09-22T15:09:12Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.citation.spa.fl_str_mv |
J. Mantilla, “Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo”, J. Comput. Electron. Sci.: Theory Appl., vol. 2, no. 1, pp. 51–60, 2021. https://doi.org/10.17981/cesta.02.01.2021.05 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/8742 |
| dc.identifier.url.spa.fl_str_mv |
https://doi.org/10.17981/cesta.02.01.2021.05 |
| dc.identifier.doi.spa.fl_str_mv |
10.17981/cesta.02.01.2021.05 |
| dc.identifier.eissn.spa.fl_str_mv |
2745-0090 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
J. Mantilla, “Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo”, J. Comput. Electron. Sci.: Theory Appl., vol. 2, no. 1, pp. 51–60, 2021. https://doi.org/10.17981/cesta.02.01.2021.05 10.17981/cesta.02.01.2021.05 2745-0090 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/8742 https://doi.org/10.17981/cesta.02.01.2021.05 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Computer and Electronic Sciences: Theory and Applications Computer and Electronic Sciences: Theory and Applications |
| dc.relation.references.spa.fl_str_mv |
[1] W. Li, K. Li, P. N. R. Kumar & Q. Tian, “Simultaneous product and service delivery vehicle routing problem with time windows and order release dates,” Appl Math Model, vol. 89, Part 1, pp. 669–687, jan. 2021. https://doi.org/10.1016/j.apm.2020.07.045 [2] A. Moura & J. F. Oliveira, “An integrated approach to the Vehicle Routing and Container Loading Problems,” OR Spectr, vol. 31, no. 4, pp. 775–800, 2009. https://doi.org/10.1007/s00291-008-0129-4 [3] A. Expósito, J. Brito & J. A. Moreno, “Quality of service objectives for vehicle routing problem with time windows,” Appl Soft Comput Jl, no. 84, p. 105707, 2019. https://doi.org/10.1016/j.asoc.2019.105707 [4] W. Zhang, D. Yang & G. Zhang, “Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW,” Expert Syst Appl, vol. 145, pp. 113–151, 2020. https://doi.org/10.1016/j.eswa.2019.113151 [5] G. B. Dantzig & J. H. Ramser, “The Truck Dispatching Problem,” Informs, vol. 6, no. 1, pp. 80–91, 1959. https://doi.org/10.1287/ mnsc.6.1.80 [6] J. Gupta & C. Diwaker, “Evaluation of Capacitated Vehicle Routing Problem with Time Windows using ACO-GA,” IJRCSSE, vol. 7, no. 6, pp. 610–615, 2017. https://doi.org/10.23956/ijarcsse/V7I6/0319 [7] R. Baños & J. Ortega, “A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows,” CAIE, vol. 65, no. 2, pp. 286–296, 2013. http://dx.doi.org/10.1016/j.cie.2013.01.007 [8] Z. J. Czech & P. Czarnas, “Parallel simulated annealing for the vehicle routing problem with time windows,” presented at 10th Euromicro Workshop on Parallel Distributed and Network-based Processing, PDP, CI, Es, pp. 376–383, 9-11 Jan. 2002. http://dx.doi.org/10.1109/EMPDP.2002.994313 [9] J. Schulze & T. Fahle, “A parallel algorithm for the vehicle routing problem with time window constraints,” Ann Oper Res, vol. 86, p. 585–607, 1999. https://doi.org/10.1023/A:1018948011707 [10] M. M. Solomon, “Algorithms for the Vehicle Routing and Scheduling Problems with Time Window,” Oper Res, vol. 35, no. 2, pp. 254–265, 1987. https://doi.org/10.1287/opre.35.2.254 [11] J. Berger, M. Salois & R. Begin, “A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows,” Conference presented at Canadian AI 1998 Advances in Artificial Intelligence, CSCSI, YVR, BC, Ca, 1998. https://doi.org/10.1007/3- 540-64575-6_44 [12] O. Braysy & M. Gendreau, “Tabu search heuristics for the vehicle routing with time windows,” BEIO, vol. 10, no. 2, pp. 211– 237, 2002. https://doi.org/10. 211-237. 10.1007/BF02579017 [13] W.-K. Ho, J. Chin & A. Lim, “A hybrid search algorithm for the vehicle routing problem with time windows,” Int J Artif Intell Tools, vol. 10, no. 3, pp. 431–449, 2001. https://doi.org/10.1142/S021821300100060X [14] H. Gehring & J. Homberger, “Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows,” J Heuristics, vol. 8, pp. 251–276, 2002. https://doi.org/10.1023/A:1015053600842 [15] O. Bräysy, “A Reactive Variable Neighborhood Search for the Vehicle Routing Problem with Time Windows,” JOC, vol. 15, no. 4, pp. 347–368, 2003. https://doi.org/10.1287/ijoc.15.4.347.24896 [16] A. Moura & J. F. Oliveira, “Uma heurística composta para a determinação de rotas para veículos em problemas com janelas temporais e entregas erecolhas,” Inv Op, vol. 24, pp. 45–62, 2004. Extraído de apdio.pt/documents/10180/15407/IOvol24n1.pdf [17] D. Mestera & O. Braysy, “Active guided evolution strategies for large-scale vehicle routing problems with time windows,” Comp Oper Res, vol. 32, no. 6 p. 1593–1614, 2005. https://doi.org/10.1016/j.cor.2003.11.017 [18] I. Soenandi & B. Marpaung, “Capacitated Vehicle Routing Problem with Time Windows dengan Menggunakan Ant Colony Optimization,” JSMI, vol. 3, no. 1, pp. 59–66, 2019. https://doi.org/10.30656/jsmi.v3i1.1469 [19] Y. Shen, M. Liu 1, J. Yang, Y. Shi & M. Middendorf, “A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows,” IEEE Access, vol. 8, pp. 93882–93893, Mar. 2020. https://doi.org/10.1109/ACCESS.2020.2984660 [20] B. Moradi, “The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution,” Soft Comput, vol. 24, no. 9, p. 6741–6769, May. 2020. https://doi.org/10.1007/s00500-019-04312-9 [21] T.-S. Khoo, B. B. Mohammad, V. Wong, Y.-H. Tay & M. Nair, “A Two-Phase Distributed Ruin-and-Recreate Genetic Algorithm for Solving the Vehicle Routing Problem With Time Windows,” IEEE Access, vol. 8, p. 169851–169871, Sep. 2020. https://doi.org/10.1109/ACCESS.2020.3023741 [22] G. Clark y J. W. Wright, “Scheduling of vehicles from a central depot to a number of delivery points,” Oper Res, vol. 12, no. 4, pp. 568–581, 1964. https://doi.org/10.1287/opre.12.4.568 [23] S. Mortada & Y. Yusof, “A Neighbourhood Search for Artificial Bee Colony in Vehicle Routing Problem with Time Windows,” Int J Intell Eng Syst, vol. 14, no. 3, pp. 255–266, 2021. https://doi.org/10.22266/ijies2021.0630.22 |
| dc.relation.citationendpage.none.fl_str_mv |
60 |
| dc.relation.citationstartpage.none.fl_str_mv |
51 |
| dc.relation.citationissue.spa.fl_str_mv |
1 |
| dc.relation.citationvolume.spa.fl_str_mv |
2 |
| dc.relation.ispartofjournalabbrev.spa.fl_str_mv |
CESTA |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
10 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.publisher.place.spa.fl_str_mv |
Barranquilla |
| dc.source.spa.fl_str_mv |
Computer and Electronic Sciences: Theory and Applications |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://revistascientificas.cuc.edu.co/CESTA/article/view/3378 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/22622b4b-571b-4ca9-8d06-f92a62481e43/download https://repositorio.cuc.edu.co/bitstreams/3ba2be38-5f15-4bda-b5e5-1168ab45ee9b/download https://repositorio.cuc.edu.co/bitstreams/d71c0a57-a2af-41e3-ba03-3c866a13a658/download https://repositorio.cuc.edu.co/bitstreams/eb4a5a71-a69b-4a4e-90cd-b2f11e35a2ab/download https://repositorio.cuc.edu.co/bitstreams/54a8499e-8884-4e51-9455-d52adde58b59/download |
| bitstream.checksum.fl_str_mv |
a31a1dc50ebf4ac36ee2cd64cc8f6d2e 42fd4ad1e89814f5e4a476b409eb708c e30e9215131d99561d40d6b0abbe9bad 88779e64ac162de641a3e2c3216c69b1 90b70dd303d7de49d828f1de780b83ae |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760790903980032 |
| spelling |
Mantilla Mejía, Javier Darío2021-09-22T15:09:12Z2021-09-22T15:09:12Z2021J. Mantilla, “Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo”, J. Comput. Electron. Sci.: Theory Appl., vol. 2, no. 1, pp. 51–60, 2021. https://doi.org/10.17981/cesta.02.01.2021.05https://hdl.handle.net/11323/8742https://doi.org/10.17981/cesta.02.01.2021.0510.17981/cesta.02.01.2021.052745-0090Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Introduction— Vehicle routing scheduling with service compliance is a necessity for logistics companies in search of their competitive advantage. Objective— The objective of the following work is to determine the routing of vehicles with time windows for a homogeneous fleet applied to the last, mile distribution case with 300 clients, considering the minimization of operating costs, distribution costs and, downtime costs. Methodology— The problem is approached through the approach of a mixed-integer linear programming mathematical model, and the development of an algorithm through the use of the savings method and the use of the swap operator. Results— In the construction phase, the savings algorithm achieves an initial cost focused on the minimum distance. In the upgrade phase, the swap operator improves the initially established solution, very quickly. For a case of 300 clients, 12 iterations were carried out, obtaining an improvement of 71.41% over the initial cost. Conclusions— For calculations of VRPTW cases with 300 nodes, the swap operator achieves computational times of less than 30 seconds.Introducción— La programación de ruteo de vehículos con cumplimiento de servicio es una necesidad de las empresas de logística en busca de su ventaja competitiva. Objetivo— El objetivo del siguiente trabajo es determinar el ruteo de vehículos con ventanas de tiempo para una flota homogénea aplicado a un caso de distribución última milla con 300 clientes, considerando la minimización de los costos operativos, costos de distribución y costos de tiempos de inactividad. Métodología— Se aborda el problema a través del planteamiento de un modelo matemático de programación lineal entera mixta, y el desarrollo de un algoritmo mediante uso del método de ahorros y el uso del operador swap. Resultados— En la fase de construcción, el algoritmo de ahorros logra un costo inicial enfocado en la distancia mínima. En la fase de mejoramiento, el operador swap mejora la solución inicial establecida, de forma muy rápida. Para un caso de 300 clientes, se realizaron 12 iteraciones obteniendo una mejora del 71.41% sobre el costo inicial. Conclusiones— Para cálculos de casos de VRPTW con 300 nodos, el operador swap consigue tiempos computacionales menores a 30 segundos.Mantilla Mejía, Javier Darío-will be generated-orcid-0000-0002-2018-7621-60010 páginasapplication/pdfspaCorporación Universidad de la CostaBarranquillaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Computer and Electronic Sciences: Theory and Applicationshttps://revistascientificas.cuc.edu.co/CESTA/article/view/3378Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempoThe use of the swap operator generates efficient computational solutions in a vehicle routing case with a time window approachArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionComputer and Electronic Sciences: Theory and ApplicationsComputer and Electronic Sciences: Theory and Applications[1] W. Li, K. Li, P. N. R. Kumar & Q. Tian, “Simultaneous product and service delivery vehicle routing problem with time windows and order release dates,” Appl Math Model, vol. 89, Part 1, pp. 669–687, jan. 2021. https://doi.org/10.1016/j.apm.2020.07.045[2] A. Moura & J. F. Oliveira, “An integrated approach to the Vehicle Routing and Container Loading Problems,” OR Spectr, vol. 31, no. 4, pp. 775–800, 2009. https://doi.org/10.1007/s00291-008-0129-4[3] A. Expósito, J. Brito & J. A. Moreno, “Quality of service objectives for vehicle routing problem with time windows,” Appl Soft Comput Jl, no. 84, p. 105707, 2019. https://doi.org/10.1016/j.asoc.2019.105707[4] W. Zhang, D. Yang & G. Zhang, “Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW,” Expert Syst Appl, vol. 145, pp. 113–151, 2020. https://doi.org/10.1016/j.eswa.2019.113151[5] G. B. Dantzig & J. H. Ramser, “The Truck Dispatching Problem,” Informs, vol. 6, no. 1, pp. 80–91, 1959. https://doi.org/10.1287/ mnsc.6.1.80[6] J. Gupta & C. Diwaker, “Evaluation of Capacitated Vehicle Routing Problem with Time Windows using ACO-GA,” IJRCSSE, vol. 7, no. 6, pp. 610–615, 2017. https://doi.org/10.23956/ijarcsse/V7I6/0319[7] R. Baños & J. Ortega, “A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows,” CAIE, vol. 65, no. 2, pp. 286–296, 2013. http://dx.doi.org/10.1016/j.cie.2013.01.007[8] Z. J. Czech & P. Czarnas, “Parallel simulated annealing for the vehicle routing problem with time windows,” presented at 10th Euromicro Workshop on Parallel Distributed and Network-based Processing, PDP, CI, Es, pp. 376–383, 9-11 Jan. 2002. http://dx.doi.org/10.1109/EMPDP.2002.994313[9] J. Schulze & T. Fahle, “A parallel algorithm for the vehicle routing problem with time window constraints,” Ann Oper Res, vol. 86, p. 585–607, 1999. https://doi.org/10.1023/A:1018948011707[10] M. M. Solomon, “Algorithms for the Vehicle Routing and Scheduling Problems with Time Window,” Oper Res, vol. 35, no. 2, pp. 254–265, 1987. https://doi.org/10.1287/opre.35.2.254[11] J. Berger, M. Salois & R. Begin, “A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows,” Conference presented at Canadian AI 1998 Advances in Artificial Intelligence, CSCSI, YVR, BC, Ca, 1998. https://doi.org/10.1007/3- 540-64575-6_44[12] O. Braysy & M. Gendreau, “Tabu search heuristics for the vehicle routing with time windows,” BEIO, vol. 10, no. 2, pp. 211– 237, 2002. https://doi.org/10. 211-237. 10.1007/BF02579017[13] W.-K. Ho, J. Chin & A. Lim, “A hybrid search algorithm for the vehicle routing problem with time windows,” Int J Artif Intell Tools, vol. 10, no. 3, pp. 431–449, 2001. https://doi.org/10.1142/S021821300100060X[14] H. Gehring & J. Homberger, “Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows,” J Heuristics, vol. 8, pp. 251–276, 2002. https://doi.org/10.1023/A:1015053600842[15] O. Bräysy, “A Reactive Variable Neighborhood Search for the Vehicle Routing Problem with Time Windows,” JOC, vol. 15, no. 4, pp. 347–368, 2003. https://doi.org/10.1287/ijoc.15.4.347.24896[16] A. Moura & J. F. Oliveira, “Uma heurística composta para a determinação de rotas para veículos em problemas com janelas temporais e entregas erecolhas,” Inv Op, vol. 24, pp. 45–62, 2004. Extraído de apdio.pt/documents/10180/15407/IOvol24n1.pdf[17] D. Mestera & O. Braysy, “Active guided evolution strategies for large-scale vehicle routing problems with time windows,” Comp Oper Res, vol. 32, no. 6 p. 1593–1614, 2005. https://doi.org/10.1016/j.cor.2003.11.017[18] I. Soenandi & B. Marpaung, “Capacitated Vehicle Routing Problem with Time Windows dengan Menggunakan Ant Colony Optimization,” JSMI, vol. 3, no. 1, pp. 59–66, 2019. https://doi.org/10.30656/jsmi.v3i1.1469[19] Y. Shen, M. Liu 1, J. Yang, Y. Shi & M. Middendorf, “A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows,” IEEE Access, vol. 8, pp. 93882–93893, Mar. 2020. https://doi.org/10.1109/ACCESS.2020.2984660[20] B. Moradi, “The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution,” Soft Comput, vol. 24, no. 9, p. 6741–6769, May. 2020. https://doi.org/10.1007/s00500-019-04312-9[21] T.-S. Khoo, B. B. Mohammad, V. Wong, Y.-H. Tay & M. Nair, “A Two-Phase Distributed Ruin-and-Recreate Genetic Algorithm for Solving the Vehicle Routing Problem With Time Windows,” IEEE Access, vol. 8, p. 169851–169871, Sep. 2020. https://doi.org/10.1109/ACCESS.2020.3023741[22] G. Clark y J. W. Wright, “Scheduling of vehicles from a central depot to a number of delivery points,” Oper Res, vol. 12, no. 4, pp. 568–581, 1964. https://doi.org/10.1287/opre.12.4.568[23] S. Mortada & Y. Yusof, “A Neighbourhood Search for Artificial Bee Colony in Vehicle Routing Problem with Time Windows,” Int J Intell Eng Syst, vol. 14, no. 3, pp. 255–266, 2021. https://doi.org/10.22266/ijies2021.0630.22605112CESTAVRP applicationsHeuristicSaving matrixCombinatorial optimizationVRPTWAplicaciones VRPHeurísticaMatriz de ahorroOptimización combinatoriaPublicationORIGINALUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdfUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdfapplication/pdf1076583https://repositorio.cuc.edu.co/bitstreams/22622b4b-571b-4ca9-8d06-f92a62481e43/downloada31a1dc50ebf4ac36ee2cd64cc8f6d2eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/3ba2be38-5f15-4bda-b5e5-1168ab45ee9b/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/d71c0a57-a2af-41e3-ba03-3c866a13a658/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdf.jpgUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdf.jpgimage/jpeg68045https://repositorio.cuc.edu.co/bitstreams/eb4a5a71-a69b-4a4e-90cd-b2f11e35a2ab/download88779e64ac162de641a3e2c3216c69b1MD54TEXTUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdf.txtUso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo.pdf.txttext/plain27523https://repositorio.cuc.edu.co/bitstreams/54a8499e-8884-4e51-9455-d52adde58b59/download90b70dd303d7de49d828f1de780b83aeMD5511323/8742oai:repositorio.cuc.edu.co:11323/87422024-09-17 12:00:33.81http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |