Modelo dinámico multivariable de la distribución urbana de mercancías utilizando microsimulación e inferencia difusa

Vista sencilla de ítem
dc.contributor.advisorSerna Urán, Conrado Augustospa
dc.contributor.advisorArango Serna, Martín Daríospa
dc.contributor.authorGómez Marín, Cristian Giovannyspa
dc.contributor.corporatenameUniversidad Nacional de Colombia - Sede Medellínspa
dc.contributor.researchgroupLogística Industrial-Organizacional \'GICO\'spa
dc.date.accessioned2020-09-21T19:52:24Zspa
dc.date.available2020-09-21T19:52:24Zspa
dc.date.issued2020-07-31spa
dc.description.abstractThis doctoral thesis presents an urban goods distribution model that allows to include multiple dynamic variables in the pick-up and delivery processes, which emerge from the processes scenarios where these are performed. The dynamic variables considered are demand and time. The demand variables include new order arrives, orders cancellations, changes in order quantities and time windows, the time variables consider changes in travel and service time. The variables changes can impact the vehicles total tour time and the fulfilment of the time windows constraints. The proposed model uses the integration between microsimulation and multi-agent systems to represent the decentralized collaboration process for the information management and the coordination and integration process among the stakeholders to respond to the different changes in the analyzed variables and assess the impact of those changes on the final performance of the distribution process in terms of cost and service levels, additionally, it uses fuzzy inference on vehicles behaviors for the different changes in the operation on travel time, service time and time window variables, to decide the answer to those changes and achieve an equilibrium between the service level and the operation cost.spa
dc.description.abstractEn esta tesis doctoral se presenta un modelo de distribución urbana de mercancías que responde al problema de la falta de colaboración, coordinación en integración entre los múltiples actores y sus procesos de comunicación para responder a la incertidumbre y comportamiento dinámico de los procesos de recolección y entrega de mercancía en entornos urbanos. Como parte de la solución se incluyen el proceso de colaboración descentralizada para el manejo de la información en múltiples variables de demanda y de tiempo. Con relación a la demanda se incluyen llegada de órdenes nuevas, cambio de cantidades, cancelación de órdenes de los clientes, ventanas de tiempo para la prestación del servicio y con relación al tiempo se consideran los cambios en los tiempos de viaje y de servicio; los cambios en estas variables pueden afectar los tiempos de ruta de los vehículos y con ello el cumplimiento de las ventanas de tiempo. El modelo propuesto utiliza la integración entre la microsimulación y los sistemas multi-agente para representar los procesos de colaboración descentralizada para la gestión de la información y de coordinación e integración entre los actores para dar respuesta a los diferentes cambios en las variables analizadas y evaluar el impacto de estos cambios en el desempeño final del proceso de distribución en términos de costos y de nivel de servicio; adicionalmente, la inferencia difusa es utilizada en los comportamientos de los vehículos ante los diferentes cambios que suceden en la operación en las variables de tiempo de viaje, tiempo de servicio, ventanas de tiempo para decidir la respuesta a estos cambios de manera que se logre un equilibrio entre servicio al cliente y costo de operación.spa
dc.description.additionalLínea de Investigación: Dirección de operacionesspa
dc.description.degreelevelDoctoradospa
dc.description.projectModelo dinámico multivariable de la distribución urbana de mercancías utilizando microsimulación e inferencia difusaspa
dc.format.extent214spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationGómez-Marín, C. G. (2020). Modelo dinámico multivariable de la distribución urbana de mercancías utilizando microsimulación e inferencia difusa (Tesis de doctorado). Universidad Nacional de Colombia.spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78481
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentDepartamento de Ingeniería de la Organizaciónspa
dc.publisher.programMedellín - Minas - Doctorado en Ingeniería - Industria y Organizacionesspa
dc.relation.referencesComi, A., Buttarazzi, B., Schiraldi, M. M., Innarella, R., Varisco, M., & Rosati, L. (2017). DynaLOAD: a simulation framework for planning, managing and controlling urban delivery bays. Transportation Research Procedia, 22, 335–344.spa
dc.relation.referencesAbed, O., Bellemans, T., Janssens, G., Patil, B., Yasar, A., Janssens, D., & Wets, G. (2013). A micro simulated and demand driven supply chain model to calculate regional production and consumption matrices. Procedia Computer Science, 19(Ant), 404–411. https://doi.org/10.1016/j.procs.2013.06.055spa
dc.relation.referencesAbsalón, C., & Urzúa, C. M. (2012). Modelos de microsimulación para el análisis de las políticas públicas. Gestión y Política Pública, 21(1), 87–106.spa
dc.relation.referencesComi, A., Site, P. D., & Filippi, F. (2012). Urban freight transport demand modelling : a state of the art. European Transport/Trasporti Europei, 7(51), 1–17.spa
dc.relation.referencesAdewumi, A. O., & Adeleke, O. J. (2018). A survey of recent advances in vehicle routing problems. International Journal of Systems Assurance Engineering and Management, 9(1), 155–172. https://doi.org/10.1007/s13198-016-0493-4spa
dc.relation.referencesAditjandra, P. T., Galatioto, F., Bell, M. C., & Zunder, T. H. (2016). Evaluating the impacts of urban freight traffic: Application of micro-simulation at a large establishment. European Journal of Transport and Infrastructure Research, 16(1), 4–22.spa
dc.relation.referencesAhkamiraad, A., & Wang, Y. (2018). Capacitated and multiple cross-docked vehicle routing problem with pickup, delivery, and time windows. Computers and Industrial Engineering, 119(November 2016), 76–84. https://doi.org/10.1016/j.cie.2018.03.007spa
dc.relation.referencesAkeb, H., Moncef, B., & Durand, B. (2018). Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris. Transportation Research Part E: Logistics and Transportation Review, 119(August 2017), 223–233. https://doi.org/10.1016/j.tre.2018.04.007spa
dc.relation.referencesAkyuz, G. A., & Gursoy, G. (2019). Strategic management perspectives on supply chain. Management Review Quarterly, (November 2018). https://doi.org/10.1007/s11301-019-00165-6spa
dc.relation.referencesAllen, J., Piecyk, M., Piotrowska, M., Mcleod, F., Cherrett, T., Ghali, K., … Austwick, M. (2018). Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of London. Transportation Research Part D: Transport and Environment, 61(Part B), 325–338. https://doi.org/10.1016/j.trd.2017.07.020spa
dc.relation.referencesAlvárez, P., Lerga, I., Serrano-Hernandez, A., & Faulin, J. (2018). The impact of traffic congestion when optimising delivery routes in real time. A case study in Spain. International Journal of Logistics Research and Applications, 0(0), 1–13. https://doi.org/10.1080/13675567.2018.1457634spa
dc.relation.referencesAlves, R., da Silva Lima, R., Custódio de Sena, D., Ferreira de Pinho, A., & Holguín-Veras, . (2019). Agent-Based Simulation Model for Evaluating Urban Freight Policy to E-Commerce. Sustainability, 11(15), 4020. https://doi.org/10.3390/su11154020spa
dc.relation.referencesAnand, N., Duin, R. Van, & Tavasszy, L. (2019). Carbon credits and urban freight consolidation : An experiment using agent based simulation. Research in Transportation Economics, 100797. https://doi.org/10.1016/j.retrec.2019.100797 Anand, N., Quak, H., van Duin, R., & Tavasszy, L. (2012). City Logistics Modeling Efforts: Trends and Gaps - A Review. Procedia - Social and Behavioral Sciences, 39, 101–115. https://doi.org/10.1016/j.sbspro.2012.03.094 Anand, N., van Duin, R., & Tavasszy, L. (2014). Ontology-based multi-agent system for urban freight transportation. International Journal of Urban Sciences, 18(2), 133–153. https://doi.org/10.1080/12265934.2014.920696spa
dc.relation.referencesAnderson, P., Farooq, B., Efthymiou, D., & Bierlaire, M. (2014). Associations generation in synthetic population for transportation applications. Transportation Research Record: Journal of the Transportation Research Board, 2429, 38–50. https://doi.org/10.3141/2429-05spa
dc.relation.referencesAntún, J. P. (2013). Distribución Urbana de Mercancías: Estrategias con Centros Logísticos. In IDB Technical Note.spa
dc.relation.referencesAntunes-Lessa, D., Rodrigues de oliveira, E., Ferreira, B., Calazans, G., & Kelli de Oliveira, L. (2015). Análise da dinâmica da logística urbana ante uma situacao de vulnerabilidade explorando a abordagem multiagente. XXIX Congreso Nacional de Pesquisa Em Transporte Da Anpet, 569–580. Ouro Petro.spa
dc.relation.referencesArabani, A. ., & Farahani, R. . (2012). Facility location dynamics: an overview of classifications and applications. 62:408–420. Computers and Industrial Engineering, 62, 408:420.spa
dc.relation.referencesArango-Serna, M. D., Gómez-Marín, C. G., & Serna-Urán, C. A. (2017). Modelos logísticos aplicados a la distribución urbana de mercancías. Revista EIA, 14(28), 57–76. https://doi.org/10.24050/ reia.v14i28.1055spa
dc.relation.referencesArango-Serna, M. D., Gómez-Marín, C. G., Serna-Urán, C. A., & Zapata-Cortés, J. A. (2018). Multi-agent model for urban goods distribution. Research in Computing Science, 147(3), 35–44.spa
dc.relation.referencesArango-Serna, M. D., Romano, C. A., & Zapata-Cortés, J. A. (2016). Distribución colaborativa de mercancías utilizando el modelo IRP. DYNA, 83(196), 204–212. https://doi.org/10.15446/dyna.v83n196.52492 Arango-Serna, M. D., & Serna-Urán, C. A. (2016). Un Nuevo Protocolo de Negociación Basado en Inferencia Difusa Aplicado a la Cadena de Suministros. Universidad, Ciencia y Tecnología, 20(81), 176–187. Arango-Serna, M. D., Serna-Urán, C. A., & Alvárez-Uribe, K. C. (2012). Collaborative autonomous systems in models of urban logistics. DYNA, 79(172), 171–179. Arango-Serna, M. D., Serna-Urán, C. A., & Zapata-Cortés, J. A. (2018). Multi-agent system modeling for the coordination of processes of distribution of goods using a memetic algorithm. In G.-A. J., G. Alor-Hernández, A.spa
dc.relation.referencesArango-Serna, M. D., Zapata-Cortés, J. A., & Gutíerrez, D. (2015). Modeling The Inventory Routing Problem ( IRP ) With Multiple Depots With Genetic Algorithms. IEEE Latin America Transactions, 13(12), 3959–3965. Arango-Serna, M. D., Zapata-Cortés, J. A., & Serna-Urán, C. A. (2018a). Collaborative multiobjective model for urban goods distribution optimization. In García-Alcaraz J., G. Alor-Hernández, A. Maldonado-Macías, & C. Sánchez-Ramírez (Eds.), New Perspectives on Applied Industrial Tools and Techniques. Management and Industrial Engineering (pp. 47–70). Springer, Cham. Arango-Serna, M. D., Zapata-Cortés, J. A., & Serna-Urán, C. A. (2018b). Collaborative Multiobjective Model for Urban Goods Distribution Optimization Á Optimization Á City logistics Á Supply chain. (51), 47–70. https://doi.org/10.1007/978-3-319-56871-3spa
dc.relation.referencesAttanasio, A., Bregman, J., Ghiani, G., & Manni, E. (2007). Real-time fleet managment at eCourier Ltd. In V. Zeimpekis, C. . Tarantilis, G. . Giaglis, & I. Minis (Eds.), Dynamic Fleet Management; Concepts, Systems, Algorithms and Case Studies (pp. 219–238). US: Springer.spa
dc.relation.referencesAwasthi, A., Adetiloye, T., & Crainic, T. G. (2016). Collaboration partner selection for city logistics planning under municipal freight regulations. Applied Mathematical Modelling, 40, 510–525. https://doi.org/10.1016/j.apm.2015.04.058spa
dc.relation.referencesBaindur, D., & Viegas, J. M. (2011). An agent based model concept for assessing modal share in inter-regional freight transport markets. Journal of Transport Geography, 19(6), 1093–1105. https://doi.org/10.1016/j.jtrangeo.2011.05.006spa
dc.relation.referencesBallas, D., Broomhead, T., & Jones, P. M. (2019). Spatial microsimulation and agent-based modelling.pdf. In H. Briassoulis, D. Kavroudakis, & N. Soulakellis (Eds.), The Practice Spatial Analysis (pp. 69–80). Springer International Publishing.spa
dc.relation.referencesBarbucha, D. (2016). An improved agent-based approach to the dynamic vehicle routing problem. In I. et al. Czarnowski (Ed.), Intelligent Decision Technologies 2016 (Vol. 56, pp. 361–370). https://doi.org/10.1007/978-3-319-39630-9spa
dc.relation.referencesBarceló, Jaime, Grzybowska, H., & Pardo, S. (2007). Vehicle routing and scheduling models, simulation and city logistics. In V. Zeimpekis, C. . Tarantilis, G. . Giaglis, & I. Minis (Eds.), Dynamic Fleet Management; Concepts, Systems, Algorithms and Case Studies (pp. 163–195). https://doi.org/10.1007/978-0-387-71722-7_8 Barceló, Jaume. (2010). Models, traffic Models, simulation, and traffic Simulation. In Jaume Barceló (Ed.), Fundamentals of Traffic Simulation (pp. 1–62). https://doi.org/10.1007/978-1-4419-6142-6_1spa
dc.relation.referencesBarkaoui, M., Berger, J., & Boukhtouta, A. (2015). Customer satisfaction in dynamic vehicle routing problem with time windows. Applied Soft Computing Journal, 35, 423–432. https://doi.org/10.1016/j.asoc.2015.06.035 Barkaoui, M., & Gendreau, M. (2013). An adaptive evolutionary approach for real-time vehicle routing and dispatching. Computers and Operations Research, 40(7), 1766–1776. https://doi.org/10.1016/j.cor.2013.01.022spa
dc.relation.referencesBasso, F., D’Amours, S., Rönnqvist, M., & Weintraub, A. (2019). A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics. International Transactions in Operational Research, 26(3), 775–793. https://doi.org/10.1111/itor.12577spa
dc.relation.referencesBean, W. L., & Joubert, J. W. (2018). A systematic evaluation of freight carrier response to receiver reordering behaviour. Computers and Industrial Engineering, 124(July), 207–219. https://doi.org/10.1016/j.cie.2018.07.030spa
dc.relation.referencesBehrends, S. (2016). Recent Developments in Urban Logistics Research – A Review of the Proceedings of the International Conference on City Logistics 2009 – 2013. Transportation Research Procedia, 12, 278–287. https://doi.org/10.1016/j.trpro.2016.02.065spa
dc.relation.referencesBektaș, T., Crainic, T. G., & Van Woensel, T. (2015). From managing urban freight to smart city logistics networks. Cirrelt, 17. Retrieved from https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2015-17.pdfspa
dc.relation.referencesBellifemine, F., Caire, G., & Greenwood, D. (2007). Developing Multi-agent Systems with JADE. West Sussex: John Wiley & Sons, ltd. Bellifemine, F., Caire, G., Tiziana, T., & Rimassa, G. (2010). Jade Programmer ’ S Guide. Http://Jade.Tilab.Com/Doc/Programmersguide.Pdf, pp. 1–49. TILab S.p.A.spa
dc.relation.referencesBelykh, D. L., & Botvin, G. A. (2018). Multi-agent framework for supply chain dynamics modelling with information sharing and demand forecast. In Communications in Computer and Information Science (Vol. 858). https://doi.org/10.1007/978-3-030-02843-5_29spa
dc.relation.referencesBeuck, U., Rieser, M., Strippgen, D., Balmer, M., & Nagel, K. (2008). Preliminary results of a multi-agent traffic simulation for berlin. In S. Albeverio, D. Andrey, P. Giordano, & A. Vancheri (Eds.), The Dynamics of Complex Urban Systems: An Interdisciplinary Approach (pp. 75–94). https://doi.org/10.1007/978-3-7908-1937-3_5spa
dc.relation.referencesBjörklund, M., Abrahamsson, M., & Johansson, H. (2017). Critical factors for viable business models for urban consolidation centres. Research in Transportation Economics, 64, 36–47. https://doi.org/10.1016/j.retrec.2017.09.009spa
dc.relation.referencesBoccia, M., Crainic, T. G., Sforza, A., & Sterle, C. (2018). Multi-commodity location-routing: Flow intercepting formulation and branch-and-cut algorithm. Computers and Operations Research, 89, 94–112. https://doi.org/10.1016/j.cor.2017.08.013spa
dc.relation.referencesBouhana, A., Chabchoub, H., Abed, M., & Fekih, A. (2013). A multi-criteria decision making approach based on fuzzy theory and fuzzy preference relations for urban distribution centers’ location selection under uncertain environments. 2013 International Conference on Advanced Logistics and Transport, 556–561. https://doi.org/10.1109/ICAdLT.2013.6568519spa
dc.relation.referencesBoussier, J., Cucu, T., Ion, L., & Breuil, D. (2011). Simulation of goods delivery process. International Journal of Physical Distribution & Logistics Management, 41(9), 913–930.spa
dc.relation.referencesBowersox, D. J., Daugherty, P. J., Dröge, C. L., Germain, R. N., & Rogers, D. S. (2013). Logistical Excellence: It’s Not Business As Usual - Donald J. Bowersox - Google Libros. Burlington: Digital Press.spa
dc.relation.referencesBozzo, R., Conca, A., & Marangon, F. (2014). Decision support system for city logistics: Literature review, and guidelines for an ex-ante model. Transportation Research Procedia, 3(July), 518–527. https://doi.org/10.1016/j.trpro.2014.10.033spa
dc.relation.referencesBraekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers and Industrial Engineering, 99, 300–313. https://doi.org/10.1016/j.cie.2015.12.007spa
dc.relation.referencesBray, S., Caggiani, L., Dell’Orco, M., & Ottomanelli, M. (2014). Measuring Transport Systems Efficiency under Uncertainty by Fuzzy Sets Theory based Data Envelopment Analysis. Procedia - Social and Behavioral Sciences, 111, 770–779. https://doi.org/10.1016/j.sbspro.2014.01.111spa
dc.relation.referencesBrito, J. A. (2011). Optimización de rutas de distribución con información y restricciones difusas (Tesis de doctorado). Universidad de Laguna.spa
dc.relation.referencesBruglieri, M., Colorni, A., Lia, F., & Luè, A. (2014). A multi-objective time-dependent route planner: A real world application to Milano city. Transportation Research Procedia, 3(July), 460–469. https://doi.org/10.1016/j.trpro.2014.10.027spa
dc.relation.referencesButrina, P., Girón-Valderrama, G. C., Machado-León, J. L., Goodchild, A., & Ayyalasomayajula, P. C. (2017). From the last mile to the last 800 feet : key factors in urban pick-up and delivery of goods. Transportation Research Record: Journal of the Transportation Research Board, 2609, 85–92. https://doi.org/10.3141/2609-10spa
dc.relation.referencesCagliano, A. C., Carlin, A., Mangano, G., & Rafele, C. (2017). Analyzing the diffusion of eco-friendly vans for urban freight distribution. The International Journal of Logistics Management, 28(4), 1218–1242. https://doi.org/10.1108/IJLM-05-2016-0123 Cagliano, A. C., De Marco, A., Mangano, G., & Zenezini, G. (2017). Levers of logistics service providers’ efficiency in urban distribution. Operations Management Research, 10(3–4), 104–117. https://doi.org/10.1007/s12063-017-0125-4spa
dc.relation.referencesCaire, G. (2009). JADE Tutorial for beginners - Programming. JADE Tutorial for Beginners, (June), 1–23.spa
dc.relation.referencesCardenas, I., Borbon-Galvez, Y., Verlinden, T., Van de Voorde, E., Vanelslander, T., & Dewulf, W. (2017). City logistics, urban goods distribution and last mile delivery and collection. Competition and Regulation in Network Industries, 18(1–2), 22–43. https://doi.org/10.1177/1783591717736505spa
dc.relation.referencesCastrellón-Torres, J. P., García-Alcaraz, J. L., & Adarme-Jaimes, W. (2015). Consolidación de carga como mecanismo de coordinación en cadenas de suministro de perecederos: Estudio de simulación Resumen. DYNA, 82(189), 233–242. https://doi.org/10.15446/dyna.v82n189.48551spa
dc.relation.referencesCavalcante, R. A. (2013). Freight market interactions simulation (FREMIS): An agent- based modeling framework (PhD Thesis) (University of Toronto; Vol. 19). https://doi.org/10.1016/j.procs.2013.06.116 Cavalcante, R. A., & Roorda, M. J. (2013). Shipper/Carrier Interactions Data Collection: Web-Based Respondent Customized Stated Preference (WRCSP) Survey. In Transport Survey Methods: Best Practice for Decision Making (pp. 257–278). https://doi.org/10.1108/9781781902882-013spa
dc.relation.referencesCevirici, A., & Moller-Madsen, H. (2007). Solving Logistic Problem with Multi-Agent System. The Maersk Mc-Kinney Moeller Institute - MIP- University of Southern Denmark - SDU.spa
dc.relation.referencesChen, H. K., Hsueh, C. F., & Chang, M. S. (2006). The real-time time-dependent vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 42(5), 383–408. https://doi.org/10.1016/j.tre.2005.01.003spa
dc.relation.referencesChopra, S., & Meindl, P. (2013). Administración de la cadena de suministro. In P. Education (Ed.), Estrategia, planeación y operación (3a ed). Méxio.spa
dc.relation.referencesChow, J. Y. J. (2018). Network Design. In Informed Urban Transport Systems. Classic and Emerging Applied Sciences in a Smart Cities Era (pp. 273–340). https://doi.org/10.1016/B978-0-12-813613-3.00007-3spa
dc.relation.referencesCirovic, G., Pamucar, D., & Bozanic, D. (2014). Green logistic vehicle routing problem : Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert System with Applications, 41, 4245–4258. https://doi.org/10.1016/j.eswa.2014.01.005spa
dc.relation.referencesCollins, A. T. (2015). Behavioural Influences on the Environmental Impact of Collection/Delivery Points. In B. Fahimnia, M. G. H. Bell, D. A. Henser, & J. Sarkis (Eds.), Green Logistics and Transportation (pp. 15–34). Springer International Publishing.spa
dc.relation.referencesCrainic, T. G., Gendreau, M., & Jean-Yves, P. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies., 17(6), 541–557. Crainic, T. G., Perboli, G., Mancini, S., & Tadei, R. (2010). Two-Echelon Vehicle Routing Problem: A satellite location analysis. Procedia Social and Behavioral Sciences, 2(00), 5944–5955. https://doi.org/10.1016/j.sbspro.2010.04.009 Crainic, T. G., Perboli, G., & Rosano, M. (2018). Simulation of intermodal freight transportation systems: a taxonomy. European Journal of Operational Research, 270(2), 401–418. https://doi.org/10.1016/j.ejor.2017.11.061 Crainic, T. G., Ricciardi, N., & Storchi, G. (2007). Models for evaluating and planning city logistics systems. Transportation Science, 43(4), 432–454. https://doi.org/10.1287/trsc.1090.0279spa
dc.relation.referencesCrespo Márquez, A. (2010). Dynamic Modelling for Supply Chain Managment. Sevilla: Springer-Verlag.spa
dc.relation.referencesDablanc, L., Morganti, E., Arvidsson, N., Woxenius, J., Browne, M., & Saidi, N. (2017). The rise of on-demand ‘Instant Deliveries’ in European cities. Supply Chain Forum, 18(4), 203–217. https://doi.org/10.1080/16258312.2017.1375375spa
dc.relation.referencesDaganzo, C. (2005). Logistics Systems Analysis (4th ed.). Berlin Heidelberg: Springer.spa
dc.relation.referencesDanielis, R., Maggi, E., Rotaris, L., & Valeri, E. (2013). Urban freight distribution: Urban supply chains and transportation Policies. In M. Ben-Akiva, H. Meersman, & E. Van de Voorde (Eds.), Freight Transport Modeling (pp. 377–403). Emerald Group Publishing Limited.spa
dc.relation.referencesde Armas, J., & Melián-Batista, B. (2015). Variable Neighborhood Search for a Dynamic Rich Vehicle Routing Problem with time windows. Computers & Industrial Engineering, 85, 120–131. https://doi.org/10.1016/j.cie.2015.03.006spa
dc.relation.referencesde Bok, M., & Tavasszy, L. (2018). An empirical agent-based simulation system for urban goods transport. Procedia Computer Science, 130, 136–133. https://doi.org/10.1016/j.procs.2018.04.021spa
dc.relation.referencesde Jong, G., & Ben-Akiva, M. (2007). A micro-simulation model of shipment size and transport chain choice. Transportation Research Part B: Methodological, 41(9), 950–965. https://doi.org/10.1016/j.trb.2007.05.002 de Jong, G., Vierth, I., Tavasszy, L., & Ben-Akiva, M. (2013). Recent developments in national and international freight transport models within Europe. Transportation, 40(2), 347–371. https://doi.org/10.1007/s11116-012-9422-9spa
dc.relation.referencesDe Marco, A., Mangano, G., & Zenezini, G. (2018). Classification and benchmark of City Logistics measures: an empirical analysis. International Journal of Logistics Research and Applications, 21(1), 1–19. https://doi.org/10.1080/13675567.2017.1353068spa
dc.relation.referencesDe Oliveira, L. K., Lessa, D. A., Oliveira, E., Ferreira, B., & Calazans, G. (2017). Multi-agent modelling approach for evaluating the city logistics dynamic in a vulnerability situation: An exploratory study in Belo Horizonte (Brazil). Transportation Research Procedia, 25, 1046–1060. https://doi.org/10.1016/j.trpro.2017.05.478spa
dc.relation.referencesde Souza, R., Goh, M., Lau, H.-C., Ng, W.-S., & Tan, P.-S. (2014). Collaborative Urban Logistics – Synchronizing the Last Mile a Singapore Research Perspective. Procedia - Social and Behavioral Sciences, 125, 422–431. https://doi.org/http://dx.doi.org/10.1016/j.sbspro.2014.01.1485spa
dc.relation.referencesDeflorio, F., Gonzalez-Feliu, J., Perboli, G., & Tadei, R. (2012). The influence of time windows on the costs of urban freight distribution services in city logistics applications. European Journal of Transport and Infrastructure Research, 12(3), 256–274.spa
dc.relation.referencesDomínguez, A. (2013). Modelización del comportamiento de los comerciantes ante nuevas políticas de reparto urbano de mercancías (Tesis de doctorado). Universidad de Cantabria.spa
dc.relation.referencesDrexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283–308. https://doi.org/10.1016/j.ejor.2014.08.030spa
dc.relation.referencesDucret, R. (2014). Parcel deliveries and urban logistics: Changes and challenges in the courier express and parcel sector in Europe - The French case. Research in Transportation Business and Management, 11. https://doi.org/10.1016/j.rtbm.2014.06.009spa
dc.relation.referencesEdwards, J., Wang, Y., Potter, A., & Cullinane, S. (2010). E-business, e-logistics and the environment. In A. Mckinnon, S. Cullinane, M. Browne, & A. Witheing (Eds.), Green Logistics: Improving the Environmental Sustainability (Vol. 215, pp. 322–338). https://doi.org/10.1001/jama.1971.03180240003002spa
dc.relation.referencesEhmke, J. F. (2012). Integration of Information and Optimization Models for Routing in City Logistics (Internatio; F. S. Hillier & C. C. Price, Eds.). https://doi.org/10.1007/978-1-4614-0806-2 Ehmke, J. F., & Mattfeld, D. C. (2011). Integration of information and optimization models for vehicle routing in urban areas. Procedia - Social and Behavioral Sciences, 20, 110–119. https://doi.org/10.1016/j.sbspro.2011.08.016 Ehmke, J. F., Steinert, A., & Mattfeld, D. C. (2012). Advanced routing for city logistics service providers based on time-dependent travel times. Journal of Computational Science, 3(4), 193–205. https://doi.org/10.1016/j.jocs.2012.01.006spa
dc.relation.referencesEscuín, D., Millán, C., & Larrodé, E. (2012). Modelization of Time-Dependent Urban Freight Problems by Using a Multiple Number of Distribution Centers. Networks and Spatial Economics, 12(3), 321–336. https://doi.org/10.1007/s11067-009-9099-6spa
dc.relation.referencesEstrada, M. Á. (2007). Análisis de Estrategias Eficientes en la Logística de Distribución de Paquetería (Tesis de doctorado). Universidad Politécnica de Cataluña.spa
dc.relation.referencesEuchi, J., Yassine, A., & Chabchoub, H. (2015a). The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach. Swarm and Evolutionary Computation, 21, 41–53. https://doi.org/http://dx.doi.org/10.1016/j.swevo.2014.12.003 Euchi, J., Yassine, A., & Chabchoub, H. (2015b). The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach. Swarm and Evolutionary Computation, 21, 41–53. https://doi.org/10.1016/j.swevo.2014.12.003spa
dc.relation.referencesFarahani, R. Z., Hekmatfar, M., Fahimnia, B., & Kazemzadeh, N. (2014). Hierarchical facility location problem: Models, classifications, techniques, and applications. Computers and Industrial Engineering, 68(1), 104–117. https://doi.org/10.1016/j.cie.2013.12.005 Farahani, R. Z., Rezapour, S., & Kardar, L. (2011). Logistics Operations and Management. In Logistics Operations and Management. https://doi.org/10.1016/B978-0-12-385202-1.00008-6spa
dc.relation.referencesFarias, G. P., Dimuro, G. P., & Rocha Costa, A. C. (2010). BDI agents with fuzzy perception for simulating decision making in environments with imperfect information. Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010), 627, 1–8. Lyon, France: Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010).spa
dc.relation.referencesFatnassi, E., Chaouachi, J., & Klibi, W. (2015). Planning and operating a shared goods and passengers on-demand rapid transit system for sustainable city-logistics. Transportation Research Part B: Methodological, 81, 440–460. https://doi.org/10.1016/j.trb.2015.07.016spa
dc.relation.referencesFaure, L., Battaia, G., Marqués, G., Guillaume, R., Vega-Mejía, C. A., Montova-Torres, J. R., … Quintero-Araújo, C. L. (2013). How to anticipate the level of activity of a sustainable collaborative network: The case of urban freight delivery through logistics platforms. IEEE International Conference on Digital Ecosystems and Technologies, (August), 126–131. https://doi.org/10.1109/DEST.2013.6611341spa
dc.relation.referencesFebbraro, A. Di, Sacco, N., & Saeednia, M. (2016). An agent-based framework for cooperative planning of intermodal freight transport chains. Transportation Research Part C, 64, 72–85. https://doi.org/10.1016/j.trc.2015.12.014spa
dc.relation.referencesFerguson, M., Maoh, H., Ryan, J., Kanaroglou, P., & Rashidi, T. H. (2012). Transferability and enhancement of a microsimulation model for estimating urban commercial vehicle movements. Journal of Transport Geography, 24, 358–369. https://doi.org/10.1016/j.jtrangeo.2012.04.013spa
dc.relation.referencesFerrucci, F., & Bock, S. (2015). A general approach for controlling vehicle en-route diversions in dynamic vehicle routing problems. Transportation Research Part B: Methodological, 77, 76–87. https://doi.org/http://dx.doi.org/10.1016/j.trb.2015.03.003 Ferrucci, F., & Bock, S. (2016). Pro-active real-time routing in applications with multiple request patterns. European Journal of Operational Research, 253(2), 356–371. https://doi.org/http://dx.doi.org/10.1016/j.ejor.2016.02.016 Ferrucci, F., Bock, S., & Gendreau, M. (2013). A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods. European Journal of Operational Research, 225(1), 130–141. https://doi.org/http://dx.doi.org/10.1016/j.ejor.2012.09.016spa
dc.relation.referencesFieguth, P. (2017). Dynamic systems. In P. Fieguth (Ed.), An Introduction to Complex Systems: Society, Ecology, and Nonlinear Dynamics (pp. 41–65). https://doi.org/10.1007/978-3-319-44606-6spa
dc.relation.referencesFigliozzi, M. A. (2010). An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows. Transportation Research Part C: Emerging Technologies, 18(5), 668–679. https://doi.org/10.1016/j.trc.2009.08.005 Figliozzi, M. A. (2012). The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics. Transportation Research Part E: Logistics and Transportation Review, 48(3), 616–636. https://doi.org/10.1016/j.tre.2011.11.006spa
dc.relation.referencesFIPA. (2015). Foundation For Intelligent Physical Agents. Retrieved from Standar status specifications. website: http://fipa.org/repository/standardspecs.htmlspa
dc.relation.referencesFirdauusiyah, N., Taniguchi, E., & Qureshi, A. G. (2018). Multi-agent simulation using adaptive dynamic programing for evaluating urban consolidation centers. In E. Taniguchi & R. G. Thompson (Eds.), City Logistics 2 (pp. 211–228). https://doi.org/10.1002/9781119425526.ch13spa
dc.relation.referencesFriedrich, H., Tavasszy, L., & Davydenkob, I. (2014). Distribution Structures. In Lóránt Tavasszy & G. de Jong (Eds.), Modelling Freight Transport (pp. 65–87). https://doi.org/10.1016/B978-0-12-410400-6.00004-5spa
dc.relation.referencesFrindik, R., & Prudon, M. (2017). Collaborative City Logistics in hyperconnected delivery networks. 4th International Physical Internet Conference, (August), 1–6.spa
dc.relation.referencesGabriel, T., Phuong, C., Nguyen, K., Toulouse, M., Nguyen, P. K., & Crainic, T. G. (2015). Synchronized Multi-Trip Multi-Traffic Pickup & Delivery in City Logistics. Retrieved from https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2015-05.pdfspa
dc.relation.referencesGao, S., Wang, Y., Cheng, J., Inazumi, Y., & Tang, Z. (2016). Ant colony optimization with clustering for solving the dynamic location routing problem. Applied Mathematics and Computation, 285, 149–173. https://doi.org/10.1016/j.amc.2016.03.035spa
dc.relation.referencesGattuso, D., & Cassone, G. C. (2011). A statistical analysis for micro-simulation of UDC operativity. Procedia Engineering, 21, 114–124. https://doi.org/10.1016/j.proeng.2011.11.1994 Gattuso, D., Cassone, G. C., & Pellicanò, D. S. (2014). A micro-simulation model for performance evaluation of a logistics platform. Transportation Research Procedia, 3(July), 574–583. https://doi.org/10.1016/j.trpro.2014.10.036spa
dc.relation.referencesGeorge, A., & Rajakumar, B. R. (2013). Fuzzy Aided Ant Colony Optimization Algorithm to Solve Optimization Problem. In A. Abraham & S. Thampi (Eds.), Intelligent Informatics. Advances in Intelligent Systems and Computing (pp. 207–215). Springer Berlin Heidelberg.spa
dc.relation.referencesGhannadpour, S. F., Noori, S., Tavakkoli-Moghaddam, R., & Ghoseiri, K. (2014). A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application. Applied Soft Computing, 14, 504–527. https://doi.org/10.1016/j.asoc.2013.08.015spa
dc.relation.referencesGhiani, G., Laporte, G., & Musmano, R. (2004). Introduction to Logistics Systems Planning and Control (Wiley, Ed.). https://doi.org/10.1002/0470014040 Ghiani, G., Manni, E., Quaranta, A., & Triki, C. (2009). Anticipatory algorithms for same-day courier dispatching. Transportation Research Part E: Logistics and Transportation Review, 45(1), 96–106. https://doi.org/10.1016/j.tre.2008.08.003spa
dc.relation.referencesGómez-Marín, C. G., Arango-Serna, M. D., & Serna-Urán, C. A. (2018). Agent-based microsimulation conceptual model for urban freight distribution. Transportation Research Procedia, 33, 155–162. https://doi.org/10.1016/j.trpro.2018.10.088spa
dc.relation.referencesGómez-Marín, C. G., Arango-Serna, M. D., Serna-Urán, C. A., & Zapata-Cortés, J. A. (2018). Microsimulation as an Optimization Tool for Urban Goods Distribution: A Review. IET Seminar Digest Joint Conference for Urban Mobility in the Smart City, MOVICI-MOYCOT, 122–129. https://doi.org/10.1049/ic.2018.0020spa
dc.relation.referencesGómez-Marín, C. G., Zapata-Cortés, J. A., Arango-Serna, M. D., & Serna-Urán, C. A. (2019). An Urban Supply Chain Distribution Model. Research in Computing Science, 148(4), 9–18.spa
dc.relation.referencesGoncalves, G., Djadane, M., & Hsu, T. (2007). Handling Imprecision in Vehicle Routing Problems: Fuzzy Logic Approach. IFAC Proceedings Volumes, 40(18), 451–456. https://doi.org/10.3182/20070927-4-RO-3905.00075spa
dc.relation.referencesGoulias, K. G., Pendyala, R. M., & Bhat, C. R. (2013). Total design data needs for the new generation large-scale activity microsimulation models. In Transport Survey Methods: Best Practice for Decision Making (pp. 21–46). https://doi.org/10.1108/9781781902882-002spa
dc.relation.referencesGrzybowska, H., & Barceló, J. (2012). Decision support system for real-time urban freight management. Procedia - Social and Behavioral Sciences, 39, 712–725. https://doi.org/10.1016/j.sbspro.2012.03.142 Grzybwska, H. (2012). Combination of vehicle routing models and dynamic traffic simulation for city logistics applications (PhD Thesis) (Universidad Politécnica de Cataluña). Retrieved from http://upcommons.upc.edu/handle/10803/125067spa
dc.relation.referencesGuedria, M., Malhene, N., & Deschamps, J.-C. (2016). Urban Freight Transport: From Optimized Routes to Robust Routes. Transportation Research Procedia, 12(June 2015), 413–424. https://doi.org/10.1016/j.trpro.2016.02.076 Guerlain, C., Cortina, S., & Renault, S. (2016). Towards a Collaborative Geographical Information System to Support Collective Decision Making for Urban Logistics Initiative. Transportation Research Procedia, 12, 634–643. https://doi.org/10.1016/j.trpro.2016.02.017spa
dc.relation.referencesGuerrero, W. J., Prodhon, C., Velasco, N., & Amaya, C. A. (2013). Hybrid heuristic for the inventory location-routing problem with deterministic demand. International Journal of Production Economics, 146, 359–370.spa
dc.relation.referencesHackney, J., & Marchal, F. (2011). A coupled multi-agent microsimulation of social interactions and transportation behavior. Transportation Research Part A, 45, 296–309. https://doi.org/10.1016/j.tra.2011.01.009spa
dc.relation.referencesHolguín-Veras, J., & Sánchez-Díaz, I. (2016). Freight Demand Management and the Potential of Receiver-Led Consolidation programs. Transportation Research Part A: Policy and Practice, 84, 109–130. https://doi.org/10.1016/j.tra.2015.06.013spa
dc.relation.referencesHunt, J. D., & Stefan, K. J. (2007). Tour-based microsimulation of urban commercial movements. Transportation Research Part B: Methodological, 41(9), 981–1013. https://doi.org/10.1016/j.trb.2007.04.009spa
dc.relation.referencesIchoua, S., Gendreau, M., & Potvin, J.-Y. (2007). Planned Route Optimization For Real-Time Vehicle Routing. In V. Zeimpekis, C. . Tarantilis, G. . Giaglis, & I. Minis (Eds.), Dynamic Fleet Management; Concepts, Systems, Algorithms and Case Studies (Vol. 38, pp. 1–18). https://doi.org/10.1007/978-0-387-71722-7_1spa
dc.relation.referencesIshfaq, R., & Sox, C. R. (2012). Design of intermodal logistics networks with hub delays. European Journal of Operational Research, 220(3), 629–641. https://doi.org/10.1016/j.ejor.2012.03.010spa
dc.relation.referencesJaller, M., Wang, X. (Cara), & Holguín-Veras, J. (2015). Large urban freight traffic generators: Opportunities for city logistics initiatives. The Journal of Transport and Land Use, 8(1), 51. https://doi.org/10.5198/jtlu.2015.406spa
dc.relation.referencesJamshidi, A., Jamshidi, F., Ait-Kadi, D., & Ramudhin, A. (2018). A review of priority criteria and decision-making method applied in selection of sustainable city logistics initiatives and collaboration partners. International Journal of Production Research, 57(15–16), 5175–5193. https://doi.org/10.1080/00207543.2018.1540892spa
dc.relation.referencesJemal, H., Kechaou, Z., & Ayed, M. Ben. (2019). Multi-agent based intuitionistic fuzzy logic healthcare decision support system. Journal of Intelligent & Fuzzy System, 37(2), 2697–2712. https://doi.org/10.3233/JIFS-182926spa
dc.relation.referencesJoubert, J. W. (2012). Analyzing Commercial Through-Traffic. Procedia - Social and Behavioral Sciences, 39, 184–194. https://doi.org/10.1016/j.sbspro.2012.03.100spa
dc.relation.referencesKalia, A. K., & Singh, M. P. (2015). Muon: designing multiagent communication protocols from interaction scenarios. Autonomous Agents and Multi-Agent Systems, 29(4), 621–657.spa
dc.relation.referencesKao, C. (2017). Dynamic systems. In Network Data Envelopment Analysis (Vol. 240, pp. 409–431). https://doi.org/10.1016/j.ejor.2014.02.039spa
dc.relation.referencesKarakikes, I., Mitropoulos, L., & Savrasovs, M. (2018). Evaluating smart urban freight solutions using microsimulation.pdf. In I. Kabashkin, O. Prentkovskis, & I. Yatskiv (Eds.), Reliability and Statistics in Transportation and Communication (pp. 551–560). https://doi.org/10.1007/978-3-319-74454-4spa
dc.relation.referencesKayikci, Y. (2010). A conceptual model for intermodal freight logistics centre location decisions. Procedia - Social and Behavioral Sciences, 2(3), 6297–6311. https://doi.org/10.1016/j.sbspro.2010.04.039spa
dc.relation.referencesKhouadjia, M. R., Sarasola, B., Alba, E., Jourdan, L., & Talbi, E. G. (2012). A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests. Applied Soft Computing Journal, 12(4), 1426–1439. https://doi.org/10.1016/j.asoc.2011.10.023 Khouadjia, M. R., Sarasola, B., Alba, E., Talbi, E., & Jourdan, L. (2013). Metaheuristics for Dynamic Vehicle Routing. In E. et al Alba (Ed.), Metaheuristics for Dynamic Optimization (pp. 265–289). Berlin Heidelberg: Springer-Verlag.spa
dc.relation.referencesKickhöfer, B., & Nagel, K. (2016). Towards high-resolution first-best air pollution tolls: An evaluation of regulatory policies and a discussion on long-term user reactions. Networks and Spatial Economics, 16(1), 175–198. https://doi.org/10.1007/s11067-013-9204-8spa
dc.relation.referencesKilby, P., Prosser, P., & Shaw, P. (1998). Dynamic VRPs: A Study of Scenarios. In Report APES-06-1998.spa
dc.relation.referencesKim, G., Ong, Y. S., Cheong, T., & Tan, P. S. (2016). Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion. IEEE Transactions on Intelligent Transportation Systems, 17(8), 2367–2380. https://doi.org/10.1109/TITS.2016.2521779spa
dc.relation.referencesKim, G., Ong, Y. S., Heng, C. K., Tan, P. S., & Zhang, N. A. (2015). City Vehicle Routing Problem (City VRP): A Review. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1654–1666. https://doi.org/10.1109/TITS.2015.2395536spa
dc.relation.referencesKim, H., & Park, D. (2017). Empirical comparison of tour- and trip-based truck travel demand models. KSCE Journal of Civil Engineering, 00(0000), 1–11. https://doi.org/10.1007/s12205-017-0868-3spa
dc.relation.referencesKoç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2016). The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B: Methodological, 84, 81–102. https://doi.org/10.1016/j.trb.2015.12.010spa
dc.relation.referencesKuo, R. J., Wibowo, B. S., & Zulvia, F. E. (2016). Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time. Applied Mathematical Modelling, 40(23–24), 9990–10001. https://doi.org/10.1016/j.apm.2016.06.025spa
dc.relation.referencesLagorio, A., Pinto, R., & Golini, R. (2016). Research in urban logistics: a systematic literature review. International Journal of Physical Distribution and Logistics Management, 46(10), 908–931. https://doi.org/10.1108/IJPDLM-01-2016-0008spa
dc.relation.referencesLarsen, A., Madsen, O. B. G., & Solomon, M. M. (2007). Classifications of dynamic vehicle routing systems. In V. Zeimpekis, C. . Tarantilis, G. . Giaglis, & I. Minis (Eds.), Dynamic Fleet Management; Concepts, Systems, Algorithms & Case Studies (pp. 19–40). US: Springer.spa
dc.relation.referencesLi, J. Q., Mirchandani, P. B., & Borenstein, D. (2009). A Lagrangian heuristic for the real-time vehicle rescheduling problem. Transportation Research Part E: Logistics and Transportation Review, 45(3), 419–433. https://doi.org/10.1016/j.tre.2008.09.002spa
dc.relation.referencesLi, Y.-T., Huang, B., & Lee, D.-H. (2011). Multimodal, multicriteria dynamic route choice: a GIS-microscopic traffic simulation approach. Annals of GIS, 17(3), 173–187. https://doi.org/10.1080/19475683.2011.602026spa
dc.relation.referencesLiedtke, G., Matteis, T., & Wisetjindawat, W. (2015). Impacts of urban logistics measures on multiple actors and decision layers - a case study. Transportation Research Record: Journal of the Transportation Research Board, (2478), 57–65. https://doi.org/10.3141/2478-07spa
dc.relation.referencesLin, C., Choy, K. L., Ho, G. T. S., Lam, H. Y., Pang, G. K. H., & Chin, K. S. (2014). A decision support system for optimizing dynamic courier routing operations. Expert Systems with Applications, 41(15), 6917–6933. https://doi.org/10.1016/j.eswa.2014.04.036spa
dc.relation.referencesLindawati, van S. J., Goh, M., & de Souza, R. (2014). Collaboration in urban logistics: motivations and barriers. International Journal of Urban Sciences, 18(2), 278–290.spa
dc.relation.referencesLindholm, M., & Behrends, S. (2012). Challenges in urban freight transport planning – a review in the Baltic Sea Region. Journal of Transport Geography, 22, 129–136. https://doi.org/10.1016/j.jtrangeo.2012.01.001spa
dc.relation.referencesLiu, C. S., Kou, G., & Huang, F. H. (2016). Vehicle coordinated strategy for vehicle routing problem with fuzzy demands. Mathematical Problems in Engineering, 2016, 9071394. https://doi.org/10.1155/2016/9071394spa
dc.relation.referencesLiu, R., Xie, X., Augusto, V., & Rodriguez, C. (2013). Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. European Journal of Operational Research, 230, 475–486. https://doi.org/10.1016/j.ejor.2013.04.044spa
dc.relation.referencesLu, C. C., Ying, K. C., & Chen, H. J. (2016). Real-time relief distribution in the aftermath of disasters - A rolling horizon approach. Transportation Research Part E: Logistics and Transportation Review, 93, 1–20. https://doi.org/10.1016/j.tre.2016.05.002spa
dc.relation.referencesMahmoudi, M., & Zhou, X. (2016). Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations. Transportation Research Part B: Methodological, 89, 19–42. https://doi.org/10.1016/j.trb.2016.03.009spa
dc.relation.referencesMahmoudifard, S. M., Shabanpour, R., Golshani, N., Mohammadian, K., & Mohammadian, A. (2018). Supplier Evaluation Model in Freight Activity Microsimulation Estimator. Journal of Transportation Research Record, 2672(9), 70–80. https://doi.org/10.1177/0361198118777084spa
dc.relation.referencesMancini, S., González-Feliu, J., & Crainic, T. G. (2014). Planning y Optimization Methods for Advanced Urban Logistics Systems at Tactical Level. In Sustainable Urban Logistics: Concepts, Methods and Information Systems. EcoProduction. Environmental Issuesin Logistics and Manufacturing (pp. 145–164). Springer Berlin Heidelberg.spa
dc.relation.referencesMarcucci, E., Gatta, V., Marciani, M., & Cossu, P. (2017). Measuring the effects of an urban freight policy package defined via a collaborative governance model. Research in Transportation Economics, 65, 3–9. https://doi.org/10.1016/j.retrec.2017.09.001spa
dc.relation.referencesMavrovouniotis, M., & Yang, S. (2015). Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Information Sciences, 294, 456–477. https://doi.org/10.1016/j.ins.2014.10.002spa
dc.relation.referencesMelo, S., Macedo, J., & Baptista, P. (2017). Guiding cities to pursue a smart mobility paradigm: An example from vehicle routing guidance and its traffic and operational effects. Research in Transportation Economics, 65, 24–33. https://doi.org/10.1016/j.retrec.2017.09.007spa
dc.relation.referencesMiller, E. J. (2019). Agent-based activity travel microsimulation what’s next? In H. Briassoulis, D. Kavroudakis, & N. Soulakellis (Eds.), The Practice of Spatial Analysis (pp. 119–150). Springer International Publishing.spa
dc.relation.referencesMirabi, M., Ghomi, S. M. T. F., & Jolai, F. (2010). Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem. Robotics and Computer-Integrated Manufacturing, 26(6), 564–569. https://doi.org/http://dx.doi.org/10.1016/j.rcim.2010.06.023spa
dc.relation.referencesMöller, D. P. F. (2014). Computacional foundation in transportation systems modeling. In Introduction to Transportation Analysis, Modeling and Simulation (pp. 195–228). https://doi.org/10.1007/978-1-4471-5637-6spa
dc.relation.referencesMontemanni, R., Gambardella, L., Rizzoli, A., & Donati, A. (2005). A new algorithm for a dynamic vehicle routing problem based on ant colony system. Journal of Combinatorial Optimization, 10, 327–343.spa
dc.relation.referencesMontoya-Torres, J. R., Muñoz-Villamizar, A. F., & Vega-Mejía, C. A. (2016). On the impact of collaborative strategies for goods delivery in city logistics. Production Planning & Control: The Management of Operations, 27(6), 443–455.spa
dc.relation.referencesMorganti, E., & Dablanc, L. (2014). Recent innovation in last mile deliveries. In Non-technological Innovations for Sustainable Transport (pp. 27–45). Springer International Publishing.spa
dc.relation.referencesMorganti, E., & Gonzalez-Feliu, J. (2015). City logistics for perishable products. The case of the Parma’s Food Hub. Case Studies on Transport Policy, 3(2), 120–128. https://doi.org/10.1016/j.cstp.2014.08.003spa
dc.relation.referencesMuñuzuri, J., Cortés, P., Onieva, L., & Guadix, J. (2009). Modeling freight delivery flows: Missing link of urban transport analysis. Journal of Urban Planning and Development, 135(3), 91–99.spa
dc.relation.referencesNguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254–264. https://doi.org/10.1016/J.COR.2017.07.004spa
dc.relation.referencesNinikas, G., & Minis, I. (2018). Load transfer operations for a dynamic vehicle routing problem with mixed backhauls. Journal on Vehicle Routing Algorithms, 1(1), 47–68. https://doi.org/10.1007/s41604-017-0005-yspa
dc.relation.referencesNourinejad, M., Wenneman, A., Habib, K. N., & Roorda, M. J. (2014). Truck parking in urban areas: Application of choice modelling within traffic microsimulation. Transportation Research Part A: Policy and Practice, 64, 54–64. https://doi.org/10.1016/j.tra.2014.03.006spa
dc.relation.referencesNovaes, A. G. N., Bez, E. T., Burin, P. J., & Aragão, D. P. (2015). Dynamic milk-run OEM operations in over-congested traffic conditions. Computers and Industrial Engineering, 88, 326–340. https://doi.org/10.1016/j.cie.2015.07.010spa
dc.relation.referencesNuzzolo, A., & Comi, A. (2014). Urban freight demand forecasting: A mixed quantity/delivery/vehicle-based model. Transportation Research Part E: Logistics and Transportation Review, 65, 84–98. https://doi.org/10.1016/j.tre.2013.12.014spa
dc.relation.referencesO’Donoghue, C. (2014). Introduction. In C. O’Donoghue (Ed.), Handbook of Microsimulation Modelling (pp. 1–21). https://doi.org/10.1108/S0573-855520140000293001spa
dc.relation.referencesOrozco, J. A. (2011). A Microscopic Traffic Simulation Based Decision Support System for Real-Time Fleet Management (PhD Thesis). Universidad Politécnica de Cataluña.spa
dc.relation.referencesOsicka, O., Guajardo, M., & van Oost, T. (2019). Cooperative game-theoretic features of cost sharing in location-routing. International Transactions in Operational Research, 27, 2157–2183. https://doi.org/10.1111/itor.12698spa
dc.relation.referencesPalacio-León, O., & Adarme-Jaimes, W. (2014). Coordinación de inventarios: Un caso de estudio para la logística de ciudad. DYNA, 81(186), 295–303.spa
dc.relation.referencesPerboli, G., & Rosano, M. (2018). A decision support system for optimizing the last-mile by mixing traditional and green logistics. Lecture Notes in Business Information Processing, 262, 28–46. https://doi.org/10.1007/978-3-319-73758-4_3spa
dc.relation.referencesPerboli, G., Rosano, M., Saint-Guillain, M., & Rizzo, P. (2018). Simulation–optimisation framework for City Logistics: an application on multimodal last-mile delivery. IET Intelligent Transport Systems, 12(4), 262–269. https://doi.org/10.1049/iet-its.2017.0357spa
dc.relation.referencesPillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1–11. https://doi.org/10.1016/j.ejor.2012.08.015spa
dc.relation.referencesPillac, V., Guéret, C., & Medaglia, A. L. (2012). An event-driven optimization framework for dynamic vehicle routing. Decision Support Systems, 54(1), 414–423. https://doi.org/10.1016/j.dss.2012.06.007spa
dc.relation.referencesPira, M. Le, Marcucci, E., Gatta, V., & Ignaccolo, M. (2017). Towards a decision-support procedure to foster stakeholder involvement and acceptability of urban freight transport policies. 9, 54. https://doi.org/10.1007/s12544-017-0268-2spa
dc.relation.referencesPonce-Cueto, E., Carrasco-Gallego, R., & García-García, R. (2009). Propuesta de una guía de selección del modelo de distribución en el sistema logístico del canal HORECA. Dirección y Organización, 37, 67–75. Retrieved from http://www.revistadyo.com/index.php/dyo/article/view/40spa
dc.relation.referencesPsaraftis, H. N. (1995). Dynamic vehicle routing: Status and prospects. Annals of Operations Research, 61(1), 143–164.spa
dc.relation.referencesPsaraftis, H. N., Wen, M., & Kontovas, C. A. (2016). Dynamic Vehicle Routing Problems: Three Decades and Counting. Networks, 67(1), 3–31. https://doi.org/10.1002/netspa
dc.relation.referencesPureza, V., & Laporte, G. (2008). Waiting and Buffering Strategies for the Dynamic Pickup and Delivery Problem with Time Windows. INFOR: Information Systems and Operational Research, 46(3), 165–175. https://doi.org/10.3138/infor.46.3.165spa
dc.relation.referencesQuak, H., & De Koster, M. B. . (2009). Delivering goods in urban areas: how to deal with urban policy resctrictions and the enviroment.pdf. Transportation Science, 43(2), 211–227.spa
dc.relation.referencesQureshi, A. G., Taniguchi, E., & Yamada, T. (2009). An exact solution approach for vehicle routing and scheduling problems with soft time windows. Transportation Research Part E: Logistics and Transportation Review, 45(6), 960–977. https://doi.org/10.1016/j.tre.2009.04.007spa
dc.relation.referencesRamstedt, L., Törnquist, K., & Davidsson, P. (2013). Movement of people and goods. In E. Bruce & M. Ruth (Eds.), Simulating Social Complexity. A Handbook (pp. 651–665). https://doi.org/10.1007/978-3-540-93813-2spa
dc.relation.referencesRanaiefar, F., & Amelia, R. (2011). Freight-Transportation Externalities. In R. Z. Farahani, S. Rezapour, & L. Kardar (Eds.), Logistics Operations and Management (pp. 333–358). https://doi.org/10.1016/B978-0-12-385202-1.00016-5spa
dc.relation.referencesReis, V., & Macário, R. (2019). Freight transport modeling and simulation. In V. Reis & R. Macário (Eds.), Intermodal Freight Transportation (pp. 131–160). https://doi.org/10.1016/b978-0-12-814464-0.00006-2spa
dc.relation.referencesRichiardi, M. (2013). The missing link: AB models and dynamic microsimulation. In S. Leitner & F. Wall (Eds.), Artificial Economics and Self Organization. Springer, Lecture Notes in Economics and Mathematical Systems (Vol. 669).spa
dc.relation.referencesRichiardi, M., & Richardson, R. (2017). JAS-mine: A new platform for microsimulation and agent-based modelling. International Journal of Microsimulation, 10(1), 106–134.spa
dc.relation.referencesRichiardi, M., & Richardson, R. E. (2016). Agent-based computational demography and microsimulation using JAS-mine. In A. Grow & J. Van Bavel (Eds.), Agent-Based Modelling in Population Studies. Concepts, Methods and Applications (Vol. 41, pp. 75–112). https://doi.org/10.1007/978-3-319-32283-4spa
dc.relation.referencesRieck, J., Ehrenberg, C., & Zimmermann, J. (2014). Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery. European Journal of Operational Research, 236(3), 863–878. https://doi.org/10.1016/j.ejor.2013.12.021spa
dc.relation.referencesRitzinger, U., Puchinger, J., & Hartl, R. F. (2016). A survey on dynamic and stochastic vehicle routing problems. International Journal of Production Research, 54(1), 1–17. https://doi.org/10.1080/00207543.2015.1043403spa
dc.relation.referencesRoorda, M. J., Cavalcante, R., McCabe, S., & Kwan, H. (2010). A conceptual framework for agent-based modelling of logistics services. Transportation Research Part E: Logistics and Transportation Review, 46(1), 18–31. https://doi.org/10.1016/j.tre.2009.06.002spa
dc.relation.referencesRushton, A., Croucher, P., & Baker, P. (2011). The Handbook of Logistics and Distribution Management: Understanding the Supply Chain (4th ed.). https://doi.org/10.1016/j.tre.2007.02.001spa
dc.relation.referencesRusso, F., & Comi, A. (2011). A model system for the ex-ante assessment of city logistics measures. Research in Transportation Economics, 31(1), 81–87. https://doi.org/10.1016/j.retrec.2010.11.011spa
dc.relation.referencesSadeghi, J., Mousavi, S. M., & Niaki, S. T. A. (2014). Optimizing an inventory model with fuzzy demand, backordering, and discount using a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 40, 7318–7335. https://doi.org/10.1016/j.apm.2016.03.013spa
dc.relation.referencesSadjady, H. (2011). Physical Flows. In R. Z. Farahani, S. Rezapour, & L. Kardar (Eds.), Logistics Operations and Management (pp. 13–42). https://doi.org/10.1016/B978-0-12-385202-1.00002-5spa
dc.relation.referencesSamimi, A., Mohammadian, A., Kawamura, K., & Pourabdollahi, Z. (2014). An activity-based freight mode choice microsimulation model. Transportation Letters, 6(3), 142–151. https://doi.org/10.1179/1942787514Y.0000000021spa
dc.relation.referencesSayama, H. (2015). Introduction to the Modeling and Analysis of Complex Systems. New York, NY: Open SUNY Textbooks.spa
dc.relation.referencesScherr, Y. O., Neumann Saavedra, B. A., Hewitt, M., & Mattfeld, D. C. (2019). Service network design with mixed autonomous fleets. Transportation Research Part E: Logistics and Transportation Review, 124(February), 40–55. https://doi.org/10.1016/j.tre.2019.02.001spa
dc.relation.referencesSchröder, S., & Liedtke, G. T. (2017). Towards an integrated multi-agent urban transport model of passenger and freight. Research in Transportation Economics, 64, 3–12. https://doi.org/10.1016/j.retrec.2016.12.001spa
dc.relation.referencesSchyns, M. (2015). An ant colony system for responsive dynamic vehicle routing. European Journal of Operational Research, 245(3), 704–718. https://doi.org/10.1016/j.ejor.2015.04.009spa
dc.relation.referencesSerna-Urán, C. A. (2016). Modelo multi-agente para problemas de recogida y entrega de mercancías con ventanas de tiempo usando un algoritmo memético con relajaciones difusas (Tesis de doctorado). Universidad Nacional de Colombia.spa
dc.relation.referencesSerna-Urán, C. A., Arango-Serna, M. D., Zapata-Cortés, J. A., & Gómez-Marín, C. G. (2018). An agent-based memetic algorithm for solving three-level freight distribution problems. In J. L. Sánchez-Cervantes, G. Alor-Hernández, M. del P. Salas-Zárate, J. L. García-Alcaraz, & L. Rodríguez-Mazahua (Eds.), Exploring Intelligent Decision support Systems (pp. 111–131). https://doi.org/10.1007/978-3-319-74002-7_6spa
dc.relation.referencesSeyedhosseini, S. M., Makui, A., Shahanaghi, K., & Torkestani, S. S. (2016). Models, solution, methods and their applicability of dynamic location problems (DLPs) (a gap analysis for further research). Journal of Industrial Engineering International, 12(3), 311–341. https://doi.org/10.1007/s40092-016-0150-1spa
dc.relation.referencesShaabani, H., & Kamalabadi, I. N. (2016). An efficient population-based simulated annealing algorithm for the multi-product multi-retailer perishable inventory routing problem. Computers and Industrial Engineering, 99, 189–201. https://doi.org/10.1016/j.cie.2016.07.022spa
dc.relation.referencesSifa, Z., Jiandong, C., Xiaomin, L., & Keqiang, L. (2011). Urban pickup and delivery problem considering time-dependent fuzzy velocity. Computers and Industrial Engineering, 60(4), 821–829. https://doi.org/10.1016/j.cie.2011.01.020spa
dc.relation.referencesSimic, D., & Simic, S. (2011). A Review : Approach of Fuzzy Models. In R. et al Bruduk (Ed.), Computer Recognition Systems (pp. 717–726). Springer Berlin Heidelberg.spa
dc.relation.referencesSolomon, M. M. (1987). Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research, 35(2), 254–265. https://doi.org/10.1287/opre.35.2.254spa
dc.relation.referencesSopha, B. M., Asih, A., Nurdiansyah, H., & Maulida, R. (2018). Decision Support System for an Urban Distribution Center Using Agent‐based Modeling: A Case Study of Yogyakarta Special Region Province, Indonesia. In E. Taniguchi & R. G. Thompson (Eds.), City Logistics 2: Modeling and Planning Initiatives. (pp. 179–196). https://doi.org/10.1002/9781119425526spa
dc.relation.referencesTadić, S., Zečević, S., & Krstić, M. (2014). Ranking of logistics system scenarios for central business district. Promet-Traffic & Transportation, 26(2), 159–167. https://doi.org/10.7307/ptt.v26i2.1349spa
dc.relation.referencesTaniguchi, E. (2015). City Logistics for sustainable and liveable cities. In Green Logistics and Transportation (pp. 49–60). https://doi.org/10.1007/978-3-319-17181-4_4spa
dc.relation.referencesTaniguchi, E., Thompson, R. G., Yamada, T., & Van Duin, J. H. . (2001). City logistics: Network modelling and intelligent transport systems. Elsevier Science.spa
dc.relation.referencesTarimoradi, M., Zarandi, M. H. F., Zaman, H., & Turksan, I. B. (2015). Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art. Journal of Intelligent Manufacturing, 28, 1551–1579. https://doi.org/10.1007/s10845-015-1170-1spa
dc.relation.referencesTavasszy, Lorant, De Bok, M., Alimoradi, Z., & Rezaei, J. (2019). Logistics Decisions in Descriptive Freight Transportation Models: A Review. Journal of Supply Chain Management Science, 1(March), 74–86. https://doi.org/10.18757/jscms.2019.1992spa
dc.relation.referencesTeo, J. S.-E., Taniguchi, E., & Qureshi, A. G. (2015). Evaluation of Urban Distribution Centers Using Multiagent Modeling with Geographic Information Systems. Transportation Research Record: Journal of the Transportation Research Board, 2478(1), 35–47. https://doi.org/10.3141/2478-05spa
dc.relation.referencesTeodorović, D., & Janić, M. (2016). Freight Transportation and Logistics. In Transportation Engineering (pp. 569–634). https://doi.org/10.1016/b978-0-12-803818-5.00009-3spa
dc.relation.referencesThaller, C., Uwe, C., & Kampmann, R. (2016). System dynamics based, microscopic freight transport simulation for urban areas. A methodological approach. In U. Clausen, H. Friedrich, C. Thaller, & C. Geiger (Eds.), Commercial Transport. Lecture Notes in Logistics. (pp. 14–20). https://doi.org/10.1007/978-3-319-21266-1spa
dc.relation.referencesTomislavav, L., Matej, M., & Stane, B. (2018). Dynamic management of urban last-mile deliveries. In E. Taniguchi & R. G. Thompson (Eds.), City Logistics 2 (pp. 23–37). https://doi.org/10.1002/9781119425526.ch2spa
dc.relation.referencesToth, P., & Vigo, D. (2002). The Vehicle Routing Problem. Philadelphia, PA, USA: SIAM, 2002. Philadelphia, PA, USA: SIAM.spa
dc.relation.referencesTrillas, E., & Eciolaza, L. (2015). Fuzzy Logic: An Introductory Course for Engineering Students. Switzerland: Springer International Publishing.spa
dc.relation.referencesTsekeris, T., & Vogiatzoglou, K. (2011). Spatial agent-based modeling of household and firm location with endogenous transport costs. NETNOMICS: Economic Research and Electronic Networking, 12(2), 77–98. https://doi.org/10.1007/s11066-011-9060-yspa
dc.relation.referencesUrbano-Guerrero, L. C., Muñoz-Marín, L. S., & Osorio-Gómez, J. C. (2016). Selección multicriterio de aliado estratégico para la operación de carga terrestre. Estudios Gerenciales, 32(138), 35–43. https://doi.org/10.1016/j.estger.2015.09.002spa
dc.relation.referencesvan Lon, R. R. S., Ferrante, E., Turgut, A. E., Wenseleers, T., Berghe, G. Vanden, & Holvoet, T. (2016). Measures of dynamism and urgency in logistics. European Journal of Operational Research, 253(3), 614–624. https://doi.org/http://dx.doi.org/10.1016/j.ejor.2016.03.021spa
dc.relation.referencesVerlinden, T., Van de Voorde, E., & Dewulf, W. (2016). Ho.Re-Ca. Logistics and medieval structured cities: A market analysis and typology. In C. Uwe, H. Friedrich, C. Thaller, & C. Geiger (Eds.), Commercial Transport (pp. 369–384). https://doi.org/10.1007/978-3-319-21266-1spa
dc.relation.referencesVidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). Implicit depot assignments and rotations in vehicle routing heuristics. European Journal of Operational Research, 237(1), 15–28. https://doi.org/10.1016/j.ejor.2013.12.044spa
dc.relation.referencesVilleta, M., Lahera, T., Merino, S., Zato, J. G., Naranjo, J. E., & Jiménez, F. (2012). Modelo para la conducción eficiente y sostenible basado en lógica borrosa. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, 9(3), 259–266. https://doi.org/10.1016/j.riai.2012.05.009spa
dc.relation.referencesWang, Q., & Holguín-Veras, J. (2009). Investigation of attributes determining trip chaining behavior in hybrid microsimulation urban freight models. Transportation Research Record: Journal of the Transportation Research Board, 2066(5478), 1–8. https://doi.org/10.3141/2066-01spa
dc.relation.referencesWeidner, T., Knudson, B., Picado, R., & Hunt, J. D. (2010). Sensitivity testing with the Oregon statewide integrated model. Transportation Research Record: Journal of the Transportation Research Board, 2133, 109–122. https://doi.org/10.3141/2133-12spa
dc.relation.referencesWolpert, S., & Reuter, C. (2012). Status quo of city logistics in scientific literature: systematic review. Transportation Research Record, 2269(2269), 110–116. https://doi.org/10.3141/2269-13spa
dc.relation.referencesWooldridge, M. (2002). An Introduction to Multiagent Systems. https://doi.org/10.1145/1753171.1753181spa
dc.relation.referencesXu, J., Hancock, K., & Southworth, F. (2003). Simulation of regional freight movement with trade and transportation multinetworks. Transportation Research Record, 1854(1), 152–161. https://doi.org/10.3141/1854-17spa
dc.relation.referencesXu, Y., Wang, L., & Yang, Y. (2013). Dynamic vehicle routing using an improved variable neighborhood search algorithm. Journal of Applied Mathematics, 2013. https://doi.org/10.1155/2013/672078spa
dc.relation.referencesYang, Z., van Osta, J.-P., van Veen, B., van Krevelen, R., van Klaveren, R., Stam, A., … Emmerich, M. (2016). Dynamic vehicle routing with time windows in theory and practice. Natural Computing, 16, 119–134. https://doi.org/10.1007/s11047-016-9550-9spa
dc.relation.referencesZadeh, L. a. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-Xspa
dc.relation.referencesZapata-Cortés, J. A. (2016). Optimización de la distribución de mercancías utilizando un modelo genético multiobjetivo de inventario colaborativo de m proveedores con n clientes (Tesis de doctorado). Universidad Nacional de Colombia.spa
dc.relation.referencesZenezini, G., & De Marco, A. (2016). A review of methodologies to assess urban freight initiatives. IFAC-PapersOnLine, 49(12), 1359–1364. https://doi.org/10.1016/j.ifacol.2016.07.752spa
dc.relation.referencesZenezini, G., Gonzalez-Feliu, J., Mangano, G., & Palacios-Arguello, L. (2019). A Business Model Assessment and Evaluation Framework for City Logistics Collaborative Strategic Decision Support. In Collaborative Networks and Digital Transformation (Vol. 1, pp. 552–561). https://doi.org/10.1007/978-3-030-28464-0_48spa
dc.relation.referencesZhang, J., Lam, W. H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249(1), 144–154. https://doi.org/10.1016/j.ejor.2015.08.050spa
dc.relation.referencesZheng, H., Son, Y.-J., Chiu, Y.-C., Head, L., Feng, Y., Xi, H., … Hickman, M. (2013). A Primer for Agent-Based Simulation and Modeling in Transportation Applications. Tucson, Arizona: US Department of Transportation (DOT).spa
dc.relation.referencesZhu, L., Rousseau, L. M., Rei, W., & Li, B. (2014). Paired cooperative reoptimization strategy for the vehicle routing problem with stochastic demands. Computers and Operations Research, 50, 1–13. https://doi.org/10.1016/j.cor.2014.03.027spa
dc.relation.referencesZhu, Z., Xiao, J., He, S., Ji, Z., & Sun, Y. (2016). A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem. Information Sciences, 329, 73–89. https://doi.org/10.1016/j.ins.2015.09.006spa
dc.relation.referencesZufferey, N., Cho, B. Y., & Glardon, R. (2016). Dynamic multi-trip vehicle routing with unusual time-windows for the pick-up of blood samples and delivery of medical material. Proceedings of 5th the International Confemarrence on Operations Research and Enterprise Systems, (Icores), 366–372. https://doi.org/10.5220/0005733303660372spa
dc.relation.referencesZulvia, F. E., Kuo, R. J., & Hu, T. L. (2012). Solving CVRP with time window, fuzzy travel time and demand via a hybrid ant colony optimization and genetic algortihm. 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 10–15. https://doi.org/10.1109/CEC.2012.6252922spa
dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc650 - Gerencia y servicios auxiliares::658 - Gerencia generalspa
dc.subject.proposalurban goods distributioneng
dc.subject.proposaldistribución urbana de mercancíasspa
dc.subject.proposallogística urbanaspa
dc.subject.proposalcity logisticseng
dc.subject.proposalruteo dinámico de vehículosspa
dc.subject.proposaldynamic vehicle routing problemeng
dc.subject.proposalmulti-agent systemeng
dc.subject.proposalsistemas multi-agentespa
dc.subject.proposalinferencia difusaspa
dc.subject.proposalfuzzy inferenceeng
dc.titleModelo dinámico multivariable de la distribución urbana de mercancías utilizando microsimulación e inferencia difusaspa
dc.title.alternativeA multivariable dynamic model for the urban goods distribution using microsimulation and fuzzy inferencespa
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
75095607.2020.pdf
Tamaño:
3.89 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Doctorado en Ingeniería - Industria y Organizaciones
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Miniatura
Nombre:
license.txt
Tamaño:
3.8 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Doctorado en Ingeniería - Industria y Organizaciones