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Un Algoritmo de Estimación de Distribuciones copulado con la Distribución Generalizada de Mallows para el Problema de Ruteo de Autobuses Escolares con Selección de Paradas

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Un Algoritmo de Estimación de Distribuciones copulado con la Distribución Generalizada de Mallows para el Problema de Ruteo de Autobuses Escolares con Selección de Paradas

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dc.contributor.author Pérez-Rodríguez, Ricardo es_ES
dc.contributor.author Hernández-Aguirre, Arturo es_ES
dc.date.accessioned 2020-05-15T11:28:03Z
dc.date.available 2020-05-15T11:28:03Z
dc.date.issued 2017-07-09
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/143392
dc.description.abstract [ES] Aunque los algoritmos de estimación de distribuciones fueron originalmente diseñados para resolver problemas con dominio de valores reales o enteros, en esta contribución se utilizan para la resolución de un problema basado en permutaciones. El ruteo de autobuses escolares con selección de paradas es resuelto utilizando la distribución generalizada de Mallows como un intento para describir y obtener una distribución de probabilidad explicita sobre un conjunto de rutas de autobuses escolares. Además, un operador de mutación es considerado para mejorar la estimación de la permutación central, un parámetro de la distribución de Mallows. Diferentes y diversas instancias sirvieron como parámetro de entrada y prueba para mostrar que problemas basados en permutaciones tales como el ruteo de autobuses escolares con selección de paradas pueden ser resueltos por medio de un modelo de probabilidad, y mejorar la estimación de la permutación central ayuda al desempeño del algoritmo. es_ES
dc.description.abstract [EN] Although the estimation of distribution algorithms were originally designed for solving integer or real-valued domains, this contribution applies the algorithms mentioned to deal with a permutation-based problem, called school bus routing problem with bus stop selection, using the generalized Mallows distribution as an attempt to describe and obtain an explicit probability distribution over a set of school bus routes. In addition, a mutation operator is considered for improving the estimation of the central permutation, a parameter of the Mallows distribution. Different and diverse instances served as input and test parameters in order to show that permutation-based optimization problems such as the school bus routing problem with bus stop selection can be solved by means of a probability model, and improving the estimation of the central permutation helps the performance of the algorithm. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Estimation of distribution algorithm es_ES
dc.subject Mallows distribution es_ES
dc.subject Vehicle routing problem es_ES
dc.subject School bus routing problem es_ES
dc.subject Algoritmo de estimación de distribuciones es_ES
dc.subject Distribución de Mallows es_ES
dc.subject Problema de ruteo de vehículos es_ES
dc.subject Problema de ruteo de autobuses escolares es_ES
dc.title Un Algoritmo de Estimación de Distribuciones copulado con la Distribución Generalizada de Mallows para el Problema de Ruteo de Autobuses Escolares con Selección de Paradas es_ES
dc.title.alternative An estimation of distribution algorithm coupled with the generalized Mallows distribution for a school bus routing problem with bus stop selection. es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.riai.2017.05.002
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Pérez-Rodríguez, R.; Hernández-Aguirre, A. (2017). Un Algoritmo de Estimación de Distribuciones copulado con la Distribución Generalizada de Mallows para el Problema de Ruteo de Autobuses Escolares con Selección de Paradas. Revista Iberoamericana de Automática e Informática industrial. 14(3):288-298. https://doi.org/10.1016/j.riai.2017.05.002 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.riai.2017.05.002 es_ES
dc.description.upvformatpinicio 288 es_ES
dc.description.upvformatpfin 298 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\9211 es_ES
dc.description.references Afifi S., Dang D.-C., Moukrim, A., 2015. Heuristic solutions for the vehicle routing problem with time windows and synchronized visits. Optimization Letters, DOI 10.1007/s11590-015-0878-3. es_ES
dc.description.references Aquino-Santos R., González-Potes A., Villaseñor-González L.A., Crespo A., Sánchez J., Gallardo J.R., 2009. Simulación de Algoritmos para regular el Flujo Vehicular y la Comunicación entre Vehículos Móviles Autónomos utilizando Redes Ad Hoc. RIAI 6(1), 75-83. es_ES
dc.description.references Barbucha D., 2014. Team of A-Teams Approach for Vehicle Routing Problem with Time Windows. In Terrazas G., Otero F., Masegosa A., (Eds.), Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), Springer International Publishing, Vol. 512, pp. 273-286. es_ES
dc.description.references Berghida M., Boukra A., 2015. EBBO: an enhanced biogeography-based optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. The International Journal of Advanced Manufacturing Technology 77(9-12), 1711-1725. es_ES
dc.description.references Borda J., 1784. Memoire sur les elections au scrutin. Histoire de l'Academie Royale des Science. es_ES
dc.description.references Ceberio J., Irurozki E., Mendiburu A., Lozano J., 2014. A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem. IEEE Transaction on evolutionary computation 18(2), 286-300. es_ES
dc.description.references Ceberio J., Mendiburu A., Lozano J., 2011. Introducing the Mallows Model on Estimation of Distribution Algorithms. In Bao-Liang L., Liqing Z., Kwok J., (Eds.), Neural Information Processing. 18th International Conference ICONIP 2011, Shanghai China, Berlin: Springer Berlin Heidelberg, pp. 461-470. es_ES
dc.description.references Chakraborty, U.K., Dastidar, D.G., 1993. Using reliability analysis to estimate the number of generations to convergence in genetic algorithms. Information Processing Letters. 46, 199-209. es_ES
dc.description.references Cruz-Ramírez N., Martínez-Morales M., 1997. Un algoritmo para generar redes Bayesianas a partir de datos estadísticos. Primer Encuentro Nacional de Computación, ENC 97. Querétaro, México. es_ES
dc.description.references de Armas J., Melián-Batista B., 2015. Constrained dynamic vehicle routing problems with time windows. Soft Computing, DOI 10.1007/s00500-014- 1574-4. es_ES
dc.description.references Díaz-Parra O., Ruiz-Vanoye J., Buenabad-Arias M., Canepa-Saenz A., 2013. Vertical Transfer Algorithm for the School Bus Routing Problem. In Gavrilova M., Tan C., Abraham A., (Eds.), Transactions on Computational Science XXI, Springer Berlin Heidelberg, Vol. 8160, pp. 211-229. es_ES
dc.description.references Euchi J., Mraihi R., 2012. The urban bus routing problem in the Tunisian case by the hybrid artificial ant colony algorithm. Swarm and Evolutionary Computation 2, 15-24. es_ES
dc.description.references Fligner M., Verducci J., 1986. Distance based ranking models. J. Royal Stat. Soc. 48(3), 359-369. es_ES
dc.description.references Fligner M., Verducci J., 1988. Multistage ranking models. J. Amer. Stat. Assoc. 83(403), 892-901. es_ES
dc.description.references Gan X., Kuang J., Niu B., 2014. Multi-type Vehicle Routing Problem with Time Windows. In Huang D.-S., Jo K.-H., Wang L., (Eds.), Intelligent Computing Methodologies, Springer International Publishing, Vol. 8589, pp. 808-815. es_ES
dc.description.references Gintner V., Kliewer N., Suhl L., 2008. A Crew Scheduling Approach for Public Transit Enhanced with Aspects from Vehicle Scheduling. In Hickman M., Mirchandani P., Voß S., (Eds.), Computer-aided Systems in Public Transport, Springer Berlin Heidelberg, Vol. 600, pp. 25-42. es_ES
dc.description.references Kliewer N., Mellouli T., Suhl L., 2006. A time-space network based exact optimization model for multi-depot bus scheduling. European Journal of Operational Research 175(3), 1616-1627. es_ES
dc.description.references Kwan A., Kwan R., Wren A., 1999. Driver Scheduling Using Genetic Algorithms with Embedded Combinatorial Traits. In Wilson N., (Ed.), Computer-Aided Transit Scheduling, Springer Berlin Heidelberg, Vol. 471, pp. 81-102. es_ES
dc.description.references Larrañaga P., Lozano J., 2002. Estimation of distribution algorithms: a new tool for evolutionary computation. Kluwer Academic Publishers. es_ES
dc.description.references Li J., Li Y., Pardalos P., 2014. Multi-depot vehicle routing problem with time windows under shared depot resources. Journal of Combinatorial Optimization, DOI 10.1007/s10878-014-9767-4. es_ES
dc.description.references Mallows C., 1957. Nonnull ranking models. Biometrika 44(1-2), 114-130. es_ES
dc.description.references Meila M., Phadnis K., Patterson A., Bilmes J., 2007. Consensus ranking under the exponential model. Proc. 22nd Conf. Uncertainty Artif. Intell., Vancouver, pp. 285-294. es_ES
dc.description.references Minocha B., Tripathi S., 2014. Solving School Bus Routing Problem Using Hybrid Genetic Algorithm: A Case Study. In Babu B., Nagar A., Deep K., Pant M., Bansal J., Ray K., Gupta U., (Eds.), Proceedings of the Second International Conference on Soft Computing for Problem Solving SocProS 2012, Springer India, Vol. 236, pp. 93-103. es_ES
dc.description.references Nalepa J., Blocho M., 2015. Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows. Soft Computing, DOI 10.1007/s00500-015-1642-4. es_ES
dc.description.references Niu H., 2013. Application of Genetic Algorithm to Optimize Transit Schedule under Time-Dependent Demand. In Wang W., Wets G., (Eds.), Computational Intelligence for Traffic and Mobility, Atlantis Press, Vol. 8, pp. 71-88. es_ES
dc.description.references Pacheco J., Caballero R., Laguna M., Molina J., 2013. Bi-Objective Bus Routing: An Application to School Buses in Rural Areas. Transportation Science 47(3), 397-411. es_ES
dc.description.references Park J., Kim B., 2010. The school bus routing problem: A review. European Journal of Operational Research 202(2), 311-319. es_ES
dc.description.references Pérez-Rodríguez R., Hernández-Aguirre A., 2016. Probability model to solve the school bus routing problem with stops selection. International Journal of Combinatorial Optimization Problems and Informatics 7(1), 30-39. es_ES
dc.description.references Prins C., 2004. A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research 31(12), 1985-2002. es_ES
dc.description.references Riera-Ledesma J., Salazar-González J., 2012. Solving school bus routing using the multiple vehicle traveling purchaser problem: A branch-and-cut approach. Computers & Operations Research 39(2), 391-404. es_ES
dc.description.references Schittekat P., Kinable J., Sörensen K., Sevaux M., Spieksma F., Springael J., 2013. A metaheuristic for the school bus routing problem with bus stop selection. European Journal of Operational Research 229(2), 518-528. es_ES
dc.description.references Schwarze S., Voß S., 2015. A Bicriteria Skill Vehicle Routing Problem with Time Windows and an Application to Pushback Operations at Airports. In Dethloff J., Haasis H.-D., Kopfer H., Kotzab H., Schönberger J., (Eds.), Logistics Management (Vols. Products, Actors, Technology - Proceedings of the German Academic Association for Business Research, Bremen, 2013), Springer International Publishing, pp. 289-300. es_ES
dc.description.references Soonpracha K., Mungwattana A., Manisri T., 2015. A Re-constructed MetaHeuristic Algorithm for Robust Fleet Size and Mix Vehicle Routing Problem with Time Windows under Uncertain Demands. In Handa H., Ishibuchi H., Ong Y.-S., Tan K.-C., (Eds.), Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Springer International Publishing, Vol. 2, pp. 347-361. es_ES
dc.description.references Suiter J., Cooley D., 2001. Optimal Municipal Bus Routing Using a Genetic Algorithm. In Kůrková V., Neruda R., Kárný M., Steele N., (Eds.), Artificial Neural Nets and Genetic Algorithms, Springer Vienna, pp. 312- 315. es_ES
dc.description.references Thangiah S., Fergany A., Wilson B., Pitluga A., Mennell W., 2008. School Bus Routing in Rural School Districts. In Hickman M., Mirchandani P., Voß S., (Eds.), Computer-aided Systems in Public Transport, Springer Berlin Heidelberg, Vol. 600, pp. 209-232. es_ES
dc.description.references Toth P., Vigo D., (Eds.), 2001. The Vehicle Routing Problem. Philadelphia, PA, USA: SIAM Monographs on Discrete Mathematics and Applications. Society for Industrial and Applied Mathematics. es_ES
dc.description.references Widuch J., 2012. A label correcting algorithm for the bus routing problem. Fundamenta Informaticae 118(3), 305-326. es_ES
dc.description.references Widuch J. 2013. A label correcting algorithm with storing partial solutions to solving the bus routing problem. Informatica 24(3), 461-484. es_ES
dc.description.references Yang C., Guo Z.-x., Liu, L.-y., 2015. Comparison Study on Algorithms for Vehicle Routing Problem with Time Windows. In Qi E., Shen J., Dou R., Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014, Atlantis Press, pp. 257-260. es_ES
dc.description.references Yoshihara I., 2003. Scheduling of Bus Drivers' Service by a Genetic Algorithm. In Ghosh A., Tsutsui S., (Eds.), Advances in Evolutionary Computing, Springer Berlin Heidelberg, pp. 799-817.Able, B., 1945. Nombre del artículo. Nombre de la revista 35, 123-126. DOI: 10.3923/ijbc.2010.190.202 es_ES


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