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...

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Author: Mantilla Mejía, Javier Darío
Format: article
Status:Versión aceptada para publicación
Publication Date:2021
Country:Colombia
Institution:Corporación Universidad de la Costa
Repository:Repositorio REDICUC
Language:Spanish
OAI Identifier:oai:repositorio.cuc.edu.co:11323/8742
Online Access:https://hdl.handle.net/11323/8742
https://doi.org/10.17981/cesta.02.01.2021.05
https://repositorio.cuc.edu.co/
Access Level:Open access
Keyword:VRP applications
Heuristic
Saving matrix
Combinatorial optimization
VRPTW
Aplicaciones VRP
Heurística
Matriz de ahorro
Optimización combinatoria
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/8742
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dc.title.none.fl_str_mv Uso del operador swap genera soluciones eficientes computacionales en un caso de enrutamiento de vehículos con enfoque de ventanas de tiempo
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
Mantilla Mejía, Javier Darío
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.none.fl_str_mv Mantilla Mejía, Javier Darío
author Mantilla Mejía, Javier Darío
author_facet Mantilla Mejía, Javier Darío
author_role author
dc.subject.none.fl_str_mv VRP applications
Heuristic
Saving matrix
Combinatorial optimization
VRPTW
Aplicaciones VRP
Heurística
Matriz de ahorro
Optimización combinatoria
topic VRP applications
Heuristic
Saving matrix
Combinatorial optimization
VRPTW
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.none.fl_str_mv 2021-09-22T15:09:12Z
2021-09-22T15:09:12Z
2021
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
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info:eu-repo/semantics/acceptedVersion
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format article
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dc.identifier.none.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
https://hdl.handle.net/11323/8742
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
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.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv Computer and Electronic Sciences: Theory and Applications
Computer 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/ijies2021.0630.22
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dc.publisher.none.fl_str_mv Corporación Universidad de la Costa
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https://revistascientificas.cuc.edu.co/CESTA/article/view/3378
reponame:Repositorio REDICUC
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spelling Uso 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 approachMantilla Mejía, Javier DaríoVRP applicationsHeuristicSaving matrixCombinatorial optimizationVRPTWAplicaciones VRPHeurísticaMatriz de ahorroOptimización combinatoriaIntroduction— 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-0000-0002-2018-7621-600Corporación Universidad de la CostaBarranquilla2021-09-22T15:09:12Z2021-09-22T15:09:12Z2021Artículo de revistahttp://purl.org/coar/resource_type/c_6501Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/version/c_ab4af688f83e57aa10 páginasapplication/pdfapplication/pdfJ. 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/Computer and Electronic Sciences: Theory and Applicationshttps://revistascientificas.cuc.edu.co/CESTA/article/view/3378reponame:Repositorio REDICUCinstname:Corporación Universidad de la Costainstacron:Corporación Universidad de la CostaspaComputer 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/ijies2021.0630.22605112CESTACC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf22024-09-17T17:00:33Z
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