How to cite this paper
Villalba, A & Rotta, E. (2022). Clustering and heuristics algorithm for the vehicle routing problem with time windows.International Journal of Industrial Engineering Computations , 13(2), 165-184.
Refrences
Aarts, E. & Lenstra, J. K. (Ed). (2003). Local Search in Combinatorial Optimization. Princeton. Oxford: Princeton University Press. ISBN 9780691115221
Abbatecola, L., Fanti, M. P., Pedroncelli, G., & Ukovich, W. (2018). A New Cluster-Based Approach for the Vehicle Routing Problem with Time Windows. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Automation Science and Engineering (CASE), 2018 IEEE 14th International Conference On, 744–749. doi: 10.1109/COASE.2018.8560419
Anaya, J. (2015). El transporte de mercancías: Enfoque logístico de la distribución. España: ESIC.
Ankerst, M., Breunig, M. M., Kriegel, H.-P. & Sander, J. (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD Record, 28(2), 49–60. doi: 10.1145/304181.304187
Applegate, D., Bixby, R., Chvátal, V. & Cook, W. (2007). The Traveling Salesman Problem: A Computational Study. New Jersey: Princeton University Press.
Ayu, K. G., Septivani, N., Xu, S., Kwan, S., & Ani. (2015). Optimum clustering and routing model using CVRP cluster-first, route-second in a 3PL provider. 2015 International Conference on Industrial Engineering and Operations Management (IEOM). doi: 10.1109/ieom.2015.7093800
Bai, R., Chen, X., Chen, Z. L., Cui, T., Gong, S., He, W., Jiang, X., Jin, H., Jin, J., Kendall, G., Li, J., Lu, Z., Ren, J., Weng, P., Xue, N. & Zhang, H. (2021). Analytics and Machine Learning in Vehicle Routing Research. arXiv:2102.10012.
Barbarosoglu, G., & Ozgur, D. (1999). A tabu search algorithm for the vehicle routing problem. Computers & Operations Research, 26(3), 255–270. doi: 10.1016/s0305-0548(98)00047-1
Bent, R. & Van Hentenryck, P. (2004). A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows. Transportation Science, 38(4), 515–530. doi: 10.1287/trsc.1030.0049
Berger, J., Salois, M., & Begin, R. (1998). A hybrid genetic algorithm for the vehicle routing problem with time windows. In Conference of the Canadian society for computational studies of intelligence. Lecture Notes in Computer Science, 114–127. Springer Verlag. doi: 10.1007/3-540-64575-6_44
Best Urban Freight Solutions. (2006). Quantification of Urban Freight Transport Effects I.
Bodin, L. D. (1975). A taxonomic structure for vehicle routing and scheduling problems. Computers & Urban Society, 1(1), 11–29.
Bogotá Mayor’s Office (2019). Decreto 840 de 2019 por medio del cual se establecen las condiciones y restricciones para el tránsito de los vehículos de transporte de carga en el Distrito Capital y se dictan otras disposiciones. Bogotá. Retrieved from http://www.andi.com.co/Uploads/Decreto%20840%202019.pdf
Bogotá Mayor’s Office (2020). Decreto 047 de 2020 Por medio del cual se toman medidas transitorias y preventivas en materia de tránsito en las vías púbicas en el Distrito Capital y se dictan otras disposiciones. Bogotá. Retrieved from http://www.andi.com.co/Uploads/Decreto%20047%20de%202020.pdf
Bouhamed, O., Ghazzai, H., Besbes, H. & Massoud, Y. (2019). Q-learning based routing scheduling for a multi-task autonomous agent. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), 634-637. doi: 10.1109/MWSCAS.2019.8885080
Bowersox, D. J., & Calantone, R. J. (1998). Executive Insights: Global Logistics. Journal of International Marketing, 6(4), 83–93. doi: 10.1177/1069031x9800600410
Braekers, K., Ramaekers, K. & Nieuwenhuyse, I. V. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300-313. doi: 10.1016/j.cie.2015.12.007
Bräysy, O. (2003). A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows. INFORMS Journal on Computing, 15(4), 347-368.
Bräysy, O., & Gendreau, M. (2005). Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms. Transportation Science, 39(1), 104–118. doi: 10.1287/trsc.1030.0056
Buhrkal, K., Larsen, A. & Ropke, S. (2012). The waste collection vehicle routing problem with time windows in a city logistics context. Procedia - Social and Behavioral Sciences, 39, 241-254. doi: 10.1016/j.sbspro.2012.03.105
Caric, T. & Gold, H. (Ed.). (2008). Vehicle Routing Problem. Austria. Vienna: In-Teh is Croatian branch of I-Tech Education and Publishing KG. ISBN 978-953-7619-09-1
Chen, D. & Yang, Z. (2017). Multiple depots vehicle routing problem in the context of total urban traffic equilibrium. Journal of Advanced Transportation, 2017, 1–14. doi: 10.1155/2017/8524960
Cömert, S., Yazgan, H., Sertvuran, İ., & Şengül, H. (2017). A new approach for solution of vehicle routing problem with hard time window: an application in a supermarket chain. Sadhana, 42(12), 2067–2080. doi: 10.1007/s12046-017-0754-1
Cordeau, J. F.; Desaulniers, G.; Desrosiers, J.; Solomon, M. M. & Soumis, F. (2000). The VRP with Time Windows. Les Cahiers du GERAD.
Cordeau, J. F., Laporte, G., Savelsbergh, M. W. P. & Vigo, D. (2007). Chapter 6: vehicle routing. In: Barnhart. C. y Laporte. G. Handbooks in Operations Research and Management Science: Transportation, 14, 367-428. Amsterdam: North-Holland. Elsevier.
Croes, G. (1958). A Method for Solving Traveling-Salesman Problems. Operations Research, 6(6), 791-812. doi: 10.2307/167074
Czech, Z. J., & Czarnas, P. (2002). Parallel simulated annealing for the vehicle routing problem with time windows. Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, Canary Islands, Spain. doi: 10.1109/empdp.2002.994313
Dantzig, G. & Ramser, J. (1959). The truck dispatching problem. Management Science, 6(1), 80-91. doi: 10.1287/mnsc.6.1.80
Dondo, R., & Cerdá, J. (2007). A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. European Journal of Operational Research, 176(3), 1478–1507. doi: 10.1016/j.ejor.2004.07.077
Elshaer, R. & Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering 106242, 140. doi: 1016/j.cie.2019.106242
Englert, M., Röglin, H. & Vöcking, B. (2006). Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP. Electronic Colloquium on Computational Complexity. Report No. 92, 190–264. ISSN 1433-8092.
Ester, M., Kriegel, H. P., Sander, J. & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD’96), AAAI Press, 226–231.
Fachini, R. F., & Armentano, V. A. (2020). Logic-based Benders decomposition for the heterogeneous fixed fleet vehicle routing problem with time windows. Computers & Industrial Engineering, 148. doi: 10.1016/j.cie.2020.106641
Fernández, I. (2008). Modelización de la distribución urbana de mercancías (undergraduate thesis). Universitat Politécnica de Catalunya, Cataluña, España.
Furian, N., O’Sullivan, M., Walker, C. & Çela, E. (2021). A machine learning-based branch and price algorithm for a sampled vehicle routing problem. OR Spectrum. doi: 10.1007/s00291-020-00615-8
Galba, T., Balkić, Z. & Martinović, G. (2013). Public Transportation BigData Clustering. International journal of electrical and computer engineering systems, 4(1), 21-26.
Gendreau, M., Hertz, A., & Laporte, G. (1994). A Tabu Search Heuristic for the Vehicle Routing Problem. Management Science, 40(10), 1276–1290. doi: 10.1287/mnsc.40.10.1276
Gendreau, M., Potvin, J. Y., Bräumlaysy, O., Hasle, G. & Løkketangen, A. (2008). Metaheuristics for the Vehicle Routing Problem and Its Extensions: A Categorized Bibliography. In B. Golden; S. Raghavan y E. Wasil (eds), The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces, 43. Boston: Springer. doi: 10.1007/978-0-387-77778-8_7
Ghoseiri, K., & Ghannadpour, S. F. (2010). A hybrid genetic algorithm for multi-depot homogenous locomotive assignment with time windows. Applied Soft Computing Journal, 10(1), 53–65. doi: 10.1016/j.asoc.2009.06.004
Glover, F., Taillard, E., & de Werra, D. (1993). A user’s guide to tabu search. Annals of Operations Research, 41(1), 1–28. doi: 10.1007/bf02078647
Groër, C., Golden, B., & Wasil, E. (2010). A library of local search heuristics for the vehicle routing problem. Mathematical Programming Computation, 2(2), 79–101. doi: 10.1007/s12532-010-0013-5
Guerequeta, R. & Vallecillo, A. (2000). Técnicas de diseño de algoritmos. Málaga, España: Servicio de publicaciones de la Universidad de Málaga. ISBN 84-7496-666-3
Gupta, R., Singh, B. & Pandey, D. (2010). Multi-Objective Fuzzy Vehicle Routing Problem: A Case Study. Int. J. Contemp. Math. Sciences, 5(29), 1439–1454.
Gutiérrez-Rubiano, D. F., Hincapié-Montes J. A. & León-Villalba, A. F. (2019). Collaborative distribution: strategies to generate efficiencies in urban distribution - Results of two pilot tests in the city of Bogotá. DYNA, 86(210), 42-51. doi: 10.15446/dyna.v86n210.78931
Hair, J. F., Black, W., Babin, B. & Anderson, R. (2014). Multivariate Data Analysis. Pearson.
Hiquebran, D. T., Alfa, A. S., Shapiro, J. A., & Gittoes, D. H. (1993). A revised simulated annealing and cluster-first route-second algorithm applied to the vehicle routing problem. Engineering Optimization, 22(2), 77–107. doi: 10.1080/03052159308941327
Homberger, J. (2000). Verteilt-parallele Metaheuristiken zur Tourenplanung. Gaber, Wiesbaden
Homberger, J., & Gehring, H. (1999). Two evolutionary metaheuristics for the vehicle routing problem with time windows. INFOR: Information Systems and Operational Research, 37(3), 297–318. doi: 10.1080/03155986.1999.11732386
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. doi: 10.1109/mcse.2007.55
Kalakanti, A. K., Verma, S., Paul, T., & Yoshida, T. (2019). RL SolVeR Pro: Reinforcement Learning for Solving Vehicle Routing Problem. 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), 94–99. doi: 10.1109/aidas47888.2019.8970890
Kang, H. Y. & Lee, A. (2018). An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window. Symmetry, 10(11), 650. doi:10.3390/sym10110650
Kao, Y., & Chen, M. (2013). Solving the CVRP Problem Using a Hybrid PSO Approach. Computational Intelligence, 59–67. doi: 10.1007/978-3-642-35638-4_5
Karagül, K. & Gungor, I. (2014). A case study of heterogeneous fleet vehicle routing problem: Touristic distribution application in Alanya. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 4(2), 67–76. doi: 10.11121/ijocta.01.2014.00185
Khodabandeh, E., Bai, L., Heragu, S. S., Evans, G. W., Elrod, T. & Shirkness, M. (2016). Modelling and solution of a large-scale vehicle routing problem at GE appliances & lighting. International Journal of Production Research, 55(4), 1100–1116. doi: 10.1080/00207543.2016.1220685.
Kim, B.-I., Kim, S., & Sahoo, S. (2006). Waste collection vehicle routing problem with time windows. Computers & Operations Research, 33(12), 3624–3642. doi: 10.1016/j.cor.2005.02.045
Kizilateş, G., & Nuriyeva, F. (2013). On the Nearest Neighbor Algorithms for the Traveling Salesman Problem. Advances in Computational Science. Engineering and Information Technology, 111–118. doi: 10.1007/978-3-319-00951-3_11
Küçükoğlu, İ., & Öztürk, N. (2015). An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Computers & Industrial Engineering, 86, 60–68.
Ladner, R. (1975). On the structure of polynomial time reducibility. Journal of the Association for Computing Machinery, 22(1), 155-171. doi: 10.1145/321864.321877
Laporte, G. (2009). Fifty Years of Vehicle Routing. Transportation Science, 43(4), 408-416. doi: 10.1287/trsc.1090.0301
Lide, D. R. (Ed.). (2003). Handbook of Chemistry and Physics. (2003). United States: CRC Press. ISBN 0-8493-0481-4
Likas, A., Vlassis, N. & Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. doi: 10.1016/s0031-3203(02)00060-2
Lin, B., Ghaddar, B. & Nathwani, J. (2020). Deep reinforcement learning for the electricvehicle routing problem with time windows. arXiv:2010.02068v2
López, E. R., & de Jesús, J. (2015). A hybrid column generation and clustering approach to the school bus routing problem with time windows. Ingeniería (0121-750X), 20(1), 111–127. doi: 10.14483/udistrital.jour.reving.2015.1.a07
Lüer, A., Benavente, M., Bustos, J., & Venegas, B. (2009). El problema de rutas de vehículos: Extensiones y métodos de resolución estado del arte. Workshop Internacional EIG 2009 - Actas 3er Encuentro Informatica y Gestion, 558.
MacQueen, J. B. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9thPython in Science Conference, 445, 56 –61. doi: 10.25080/Majora-92bf1922-00a
Mester, D. (1999). A parallel dichotomy algorithm for vehicle routing problem with time windows, Working paper, Minerva Optimization Center, Technion, Israel.
Miller, C., Tucker, A. & Zemlin, R. (1960). Integer Programming Formulation of Traveling Salesman Problems. Journal of the ACM (JACM), 7(4), 326–329. doi: 10.1145/321043.321046
Moen, O. (2016). The Five-step Model – Procurement to Increase Transport Efficiency for an Urban Distribution of Goods. Transportation Research Procedia, 12, 861–873. doi: 10.1016/j.trpro.2016.02.039
Mohri, M., Rostamizadeh, A. & Talwalkar, A. (2018). Foundations of Machine Learning. Cambridge, MA: MIT Press. Retrieved from https://cs.nyu.edu/~mohri/mlbook/
Moon, I., Lee, J.-H. & Seong, J. (2012). Vehicle routing problem with time windows considering overtime and outsourcing vehicles. Expert Systems with Applications, 39(18), 13202-13213. doi: 10.1016/j.eswa.2012.05.081
Nazari, M., Oroojlooy, A., Snyder, L. V., Takáč, M. (2018). Reinforcement Learning for Solving the Vehicle Routing Problem. arXiv:1802.04240v2.
Oliphant, T. E. (2006). Guide to NumPy. MDTDR.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Pemberthy, J. (2012). Implementación de un algoritmo metaheurístico para la solución de un problema de programación de transporte terrestre internacional (master thesis). National University of Colombia, Medellín, Colombia.
Pisinger, D., & Ropke, S. (2010). Large Neighborhood Search. International Series in Operations Research & Management Science, 399–419. doi: 10.1007/978-1-4419-1665-5_13
Poullet, J. (2020). Leveraging machine learning to solve the vehicle routing problem with time windows (master thesis). Massachusetts Institute of Technology, Cambridge, United States.
Prag, K., Woolway, M. & Jacobs, B. (2019). Optimising the Vehicle Routing Problem with Time Windows under Standardised Metrics. 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), Soft Computing & Machine Intelligence (ISCMI), 2019 6th International Conference On, 111–115. doi: 10.1109/ISCMI47871.2019.9004294
Qi, M., Lin, W.-H., Li, N., & Miao, L. (2012). A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows. Transportation Research Part E, 48(1), 248–257. doi: 10.1016/j.tre.2011.07.001
Rahman, A. (2012). The traveling Salesman problem. 1-15. Retrieved from http://cs.indstate.edu/~zeeshan/aman.pdf
Rochat, Y., & Taillard, É. D. (1995). Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics, 1(1), 147–167. doi: 10.1007/bf02430370
Rousseau, L.M.; Gendreau, M. & Peasant. G. (2002). Using constraint-based operators to solve the vehicle routing problem with time windows. Journal of Heuristics, 8, 43–58. doi: 10.1023/A:1013661617536
Shaw, P. (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. Lecture Notes in Computer Science, 417–431. doi: 10.1007/3-540-49481-2_30
Shin, K. & Han, S. (2011). A centroid-based heuristic algorithm for the capacitated vehicle routing problem. Computing and Informatics, 30, 721–732.
Sinnott, R. W. (1984). Virtues of the Haversine. Sky and Telescope, 68(2).
Smet, L. & Thomas, C. (2016). Local Search for the Vehicle Routing Problem (master thesis). Université Catholique de Louvain. Bélgica.
Solano, E., Montoya-Torres, J. & Guerrero-Rueda, W. (2019). A decision support system for technician routing with time windows. A case study of a Colombian public utility company. Academia Revista Latinoamericana de Administración, 23(2), 138-158. doi: 10.1108/ARLA-04-2017-0101
Solomon, M. M. (1986). On the worst-case performance of some heuristics for the vehicle routing and scheduling problem with time window constraints. Networks, 16, 161–174.
Solomon, M. M. (2005). VRPTW benchmark problems. CBA. Retrieved from http://web.cba.neu.edu/~msolomon/problems.htm
Son, H., Kim, G. & Shin, H. (2018). Case study: vehicle routing problem with time windows for a shop in fisheries wholesale market. International Journal of Management and Applied Science, 4(4), 44-47. ISSN 2394-7926.
Stehling, T. M. & de Souza, S. R. (2017). A Comparison of Crossover Operators Applied to the Vehicle Routing Problem with Time Window. 2017 Brazilian Conference on Intelligent Systems (BRACIS), 300–305. doi: 10.1109/BRACIS.2017.47
Sutton, R. S. & Barto, A. G. (2018). Reinforcement learning: an introduction. Cambridge, MA: MIT Press.
Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J.-Y. (1997). A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transportation Science, 31(2), 170–186. doi: 10.1287/trsc.31.2.170
Tamagawa, D., Taniguchi, E., & Yamada, T. (2010). Evaluating city logistics measures using a multi-agent model. Procedia - Social and Behavioral Sciences, 2(3), 6002–6012. doi: 10.1016/j.sbspro.2010.04.014
Tan, K. C., Lee, L. H., Zhu, Q. L., & Ou, K. (2001). Heuristic methods for vehicle routing problem with time windows. Artificial Intelligence in Engineering, 15(3), 281–295. doi:10.1016/s0954-1810(01)00005-x
Taner, F., Galić, A. & Carić, T. (2012). Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms. Promet – Traffic&Transportation, 24(4), 343-351.
Tirkolaee, E., Abbasian, P., Soltani, M. & Ghaffarian, S. (2019). Developing an applied algorithm for multi-trip vehicle routing problem with time windows in urban waste collection: A case study. Waste Management & Research, 37(1), 4–13. doi: 10.1177/0734242X18807001
Toro, E., Escobar A. & Granada, M. (2016). Literature review of vehicle routing problem in the green transportation context. Revista Luna Azul, 42, 362-387. doi: 10.17151/luaz.2016.42.21
Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics and Intelligent Laboratory Systems, 120, 92–96. doi: 10.1016/j.chemolab.2012.11.006
Van Rossum, G. (1993). An Introduction to Python for UNIX/C Programmers. Proceedings of the NLUUG najaarsconferentie. Dutch UNIX users group, 1-8.
Verhoeven, M. G. A., Aarts, E. H. L., & Swinkels, P. C. J. (1995). A parallel 2-opt algorithm for the Traveling Salesman Problem. Future Generation Computer Systems, 11(2), 175–182.
Wy, J., Kim, B. I. & Kim, S. (2013). The rollon–rolloff waste collection vehicle routing problem with time windows. European Journal of Operational Research, 224(3), 466–476. doi: 10.1016/j.ejor.2012.09.001
Yepes, V. (2002). Optimización heurística económica aplicada a las redes de transporte del tipo VRPTW (doctoral thesis). Universidad Politécnica de Valencia, Valencia, España.
Yu, J. J. Q., Yu, W., & Gu, J. (2019). Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 1–12. doi: 10.1109/tits.2019.2909109
Zhao, J., Mao, M., Zhao, X., & Zou, J. (2020). A hybrid of deep reinforcement learning and local search for the vehicle routing problems. IEEE Transactions on Intelligent Transportation Systems, 1–11. doi:10.1109/tits.2020.3003163
Abbatecola, L., Fanti, M. P., Pedroncelli, G., & Ukovich, W. (2018). A New Cluster-Based Approach for the Vehicle Routing Problem with Time Windows. 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Automation Science and Engineering (CASE), 2018 IEEE 14th International Conference On, 744–749. doi: 10.1109/COASE.2018.8560419
Anaya, J. (2015). El transporte de mercancías: Enfoque logístico de la distribución. España: ESIC.
Ankerst, M., Breunig, M. M., Kriegel, H.-P. & Sander, J. (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD Record, 28(2), 49–60. doi: 10.1145/304181.304187
Applegate, D., Bixby, R., Chvátal, V. & Cook, W. (2007). The Traveling Salesman Problem: A Computational Study. New Jersey: Princeton University Press.
Ayu, K. G., Septivani, N., Xu, S., Kwan, S., & Ani. (2015). Optimum clustering and routing model using CVRP cluster-first, route-second in a 3PL provider. 2015 International Conference on Industrial Engineering and Operations Management (IEOM). doi: 10.1109/ieom.2015.7093800
Bai, R., Chen, X., Chen, Z. L., Cui, T., Gong, S., He, W., Jiang, X., Jin, H., Jin, J., Kendall, G., Li, J., Lu, Z., Ren, J., Weng, P., Xue, N. & Zhang, H. (2021). Analytics and Machine Learning in Vehicle Routing Research. arXiv:2102.10012.
Barbarosoglu, G., & Ozgur, D. (1999). A tabu search algorithm for the vehicle routing problem. Computers & Operations Research, 26(3), 255–270. doi: 10.1016/s0305-0548(98)00047-1
Bent, R. & Van Hentenryck, P. (2004). A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows. Transportation Science, 38(4), 515–530. doi: 10.1287/trsc.1030.0049
Berger, J., Salois, M., & Begin, R. (1998). A hybrid genetic algorithm for the vehicle routing problem with time windows. In Conference of the Canadian society for computational studies of intelligence. Lecture Notes in Computer Science, 114–127. Springer Verlag. doi: 10.1007/3-540-64575-6_44
Best Urban Freight Solutions. (2006). Quantification of Urban Freight Transport Effects I.
Bodin, L. D. (1975). A taxonomic structure for vehicle routing and scheduling problems. Computers & Urban Society, 1(1), 11–29.
Bogotá Mayor’s Office (2019). Decreto 840 de 2019 por medio del cual se establecen las condiciones y restricciones para el tránsito de los vehículos de transporte de carga en el Distrito Capital y se dictan otras disposiciones. Bogotá. Retrieved from http://www.andi.com.co/Uploads/Decreto%20840%202019.pdf
Bogotá Mayor’s Office (2020). Decreto 047 de 2020 Por medio del cual se toman medidas transitorias y preventivas en materia de tránsito en las vías púbicas en el Distrito Capital y se dictan otras disposiciones. Bogotá. Retrieved from http://www.andi.com.co/Uploads/Decreto%20047%20de%202020.pdf
Bouhamed, O., Ghazzai, H., Besbes, H. & Massoud, Y. (2019). Q-learning based routing scheduling for a multi-task autonomous agent. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), 634-637. doi: 10.1109/MWSCAS.2019.8885080
Bowersox, D. J., & Calantone, R. J. (1998). Executive Insights: Global Logistics. Journal of International Marketing, 6(4), 83–93. doi: 10.1177/1069031x9800600410
Braekers, K., Ramaekers, K. & Nieuwenhuyse, I. V. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300-313. doi: 10.1016/j.cie.2015.12.007
Bräysy, O. (2003). A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows. INFORMS Journal on Computing, 15(4), 347-368.
Bräysy, O., & Gendreau, M. (2005). Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms. Transportation Science, 39(1), 104–118. doi: 10.1287/trsc.1030.0056
Buhrkal, K., Larsen, A. & Ropke, S. (2012). The waste collection vehicle routing problem with time windows in a city logistics context. Procedia - Social and Behavioral Sciences, 39, 241-254. doi: 10.1016/j.sbspro.2012.03.105
Caric, T. & Gold, H. (Ed.). (2008). Vehicle Routing Problem. Austria. Vienna: In-Teh is Croatian branch of I-Tech Education and Publishing KG. ISBN 978-953-7619-09-1
Chen, D. & Yang, Z. (2017). Multiple depots vehicle routing problem in the context of total urban traffic equilibrium. Journal of Advanced Transportation, 2017, 1–14. doi: 10.1155/2017/8524960
Cömert, S., Yazgan, H., Sertvuran, İ., & Şengül, H. (2017). A new approach for solution of vehicle routing problem with hard time window: an application in a supermarket chain. Sadhana, 42(12), 2067–2080. doi: 10.1007/s12046-017-0754-1
Cordeau, J. F.; Desaulniers, G.; Desrosiers, J.; Solomon, M. M. & Soumis, F. (2000). The VRP with Time Windows. Les Cahiers du GERAD.
Cordeau, J. F., Laporte, G., Savelsbergh, M. W. P. & Vigo, D. (2007). Chapter 6: vehicle routing. In: Barnhart. C. y Laporte. G. Handbooks in Operations Research and Management Science: Transportation, 14, 367-428. Amsterdam: North-Holland. Elsevier.
Croes, G. (1958). A Method for Solving Traveling-Salesman Problems. Operations Research, 6(6), 791-812. doi: 10.2307/167074
Czech, Z. J., & Czarnas, P. (2002). Parallel simulated annealing for the vehicle routing problem with time windows. Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, Canary Islands, Spain. doi: 10.1109/empdp.2002.994313
Dantzig, G. & Ramser, J. (1959). The truck dispatching problem. Management Science, 6(1), 80-91. doi: 10.1287/mnsc.6.1.80
Dondo, R., & Cerdá, J. (2007). A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. European Journal of Operational Research, 176(3), 1478–1507. doi: 10.1016/j.ejor.2004.07.077
Elshaer, R. & Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering 106242, 140. doi: 1016/j.cie.2019.106242
Englert, M., Röglin, H. & Vöcking, B. (2006). Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP. Electronic Colloquium on Computational Complexity. Report No. 92, 190–264. ISSN 1433-8092.
Ester, M., Kriegel, H. P., Sander, J. & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD’96), AAAI Press, 226–231.
Fachini, R. F., & Armentano, V. A. (2020). Logic-based Benders decomposition for the heterogeneous fixed fleet vehicle routing problem with time windows. Computers & Industrial Engineering, 148. doi: 10.1016/j.cie.2020.106641
Fernández, I. (2008). Modelización de la distribución urbana de mercancías (undergraduate thesis). Universitat Politécnica de Catalunya, Cataluña, España.
Furian, N., O’Sullivan, M., Walker, C. & Çela, E. (2021). A machine learning-based branch and price algorithm for a sampled vehicle routing problem. OR Spectrum. doi: 10.1007/s00291-020-00615-8
Galba, T., Balkić, Z. & Martinović, G. (2013). Public Transportation BigData Clustering. International journal of electrical and computer engineering systems, 4(1), 21-26.
Gendreau, M., Hertz, A., & Laporte, G. (1994). A Tabu Search Heuristic for the Vehicle Routing Problem. Management Science, 40(10), 1276–1290. doi: 10.1287/mnsc.40.10.1276
Gendreau, M., Potvin, J. Y., Bräumlaysy, O., Hasle, G. & Løkketangen, A. (2008). Metaheuristics for the Vehicle Routing Problem and Its Extensions: A Categorized Bibliography. In B. Golden; S. Raghavan y E. Wasil (eds), The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces, 43. Boston: Springer. doi: 10.1007/978-0-387-77778-8_7
Ghoseiri, K., & Ghannadpour, S. F. (2010). A hybrid genetic algorithm for multi-depot homogenous locomotive assignment with time windows. Applied Soft Computing Journal, 10(1), 53–65. doi: 10.1016/j.asoc.2009.06.004
Glover, F., Taillard, E., & de Werra, D. (1993). A user’s guide to tabu search. Annals of Operations Research, 41(1), 1–28. doi: 10.1007/bf02078647
Groër, C., Golden, B., & Wasil, E. (2010). A library of local search heuristics for the vehicle routing problem. Mathematical Programming Computation, 2(2), 79–101. doi: 10.1007/s12532-010-0013-5
Guerequeta, R. & Vallecillo, A. (2000). Técnicas de diseño de algoritmos. Málaga, España: Servicio de publicaciones de la Universidad de Málaga. ISBN 84-7496-666-3
Gupta, R., Singh, B. & Pandey, D. (2010). Multi-Objective Fuzzy Vehicle Routing Problem: A Case Study. Int. J. Contemp. Math. Sciences, 5(29), 1439–1454.
Gutiérrez-Rubiano, D. F., Hincapié-Montes J. A. & León-Villalba, A. F. (2019). Collaborative distribution: strategies to generate efficiencies in urban distribution - Results of two pilot tests in the city of Bogotá. DYNA, 86(210), 42-51. doi: 10.15446/dyna.v86n210.78931
Hair, J. F., Black, W., Babin, B. & Anderson, R. (2014). Multivariate Data Analysis. Pearson.
Hiquebran, D. T., Alfa, A. S., Shapiro, J. A., & Gittoes, D. H. (1993). A revised simulated annealing and cluster-first route-second algorithm applied to the vehicle routing problem. Engineering Optimization, 22(2), 77–107. doi: 10.1080/03052159308941327
Homberger, J. (2000). Verteilt-parallele Metaheuristiken zur Tourenplanung. Gaber, Wiesbaden
Homberger, J., & Gehring, H. (1999). Two evolutionary metaheuristics for the vehicle routing problem with time windows. INFOR: Information Systems and Operational Research, 37(3), 297–318. doi: 10.1080/03155986.1999.11732386
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. doi: 10.1109/mcse.2007.55
Kalakanti, A. K., Verma, S., Paul, T., & Yoshida, T. (2019). RL SolVeR Pro: Reinforcement Learning for Solving Vehicle Routing Problem. 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), 94–99. doi: 10.1109/aidas47888.2019.8970890
Kang, H. Y. & Lee, A. (2018). An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window. Symmetry, 10(11), 650. doi:10.3390/sym10110650
Kao, Y., & Chen, M. (2013). Solving the CVRP Problem Using a Hybrid PSO Approach. Computational Intelligence, 59–67. doi: 10.1007/978-3-642-35638-4_5
Karagül, K. & Gungor, I. (2014). A case study of heterogeneous fleet vehicle routing problem: Touristic distribution application in Alanya. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 4(2), 67–76. doi: 10.11121/ijocta.01.2014.00185
Khodabandeh, E., Bai, L., Heragu, S. S., Evans, G. W., Elrod, T. & Shirkness, M. (2016). Modelling and solution of a large-scale vehicle routing problem at GE appliances & lighting. International Journal of Production Research, 55(4), 1100–1116. doi: 10.1080/00207543.2016.1220685.
Kim, B.-I., Kim, S., & Sahoo, S. (2006). Waste collection vehicle routing problem with time windows. Computers & Operations Research, 33(12), 3624–3642. doi: 10.1016/j.cor.2005.02.045
Kizilateş, G., & Nuriyeva, F. (2013). On the Nearest Neighbor Algorithms for the Traveling Salesman Problem. Advances in Computational Science. Engineering and Information Technology, 111–118. doi: 10.1007/978-3-319-00951-3_11
Küçükoğlu, İ., & Öztürk, N. (2015). An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Computers & Industrial Engineering, 86, 60–68.
Ladner, R. (1975). On the structure of polynomial time reducibility. Journal of the Association for Computing Machinery, 22(1), 155-171. doi: 10.1145/321864.321877
Laporte, G. (2009). Fifty Years of Vehicle Routing. Transportation Science, 43(4), 408-416. doi: 10.1287/trsc.1090.0301
Lide, D. R. (Ed.). (2003). Handbook of Chemistry and Physics. (2003). United States: CRC Press. ISBN 0-8493-0481-4
Likas, A., Vlassis, N. & Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. doi: 10.1016/s0031-3203(02)00060-2
Lin, B., Ghaddar, B. & Nathwani, J. (2020). Deep reinforcement learning for the electricvehicle routing problem with time windows. arXiv:2010.02068v2
López, E. R., & de Jesús, J. (2015). A hybrid column generation and clustering approach to the school bus routing problem with time windows. Ingeniería (0121-750X), 20(1), 111–127. doi: 10.14483/udistrital.jour.reving.2015.1.a07
Lüer, A., Benavente, M., Bustos, J., & Venegas, B. (2009). El problema de rutas de vehículos: Extensiones y métodos de resolución estado del arte. Workshop Internacional EIG 2009 - Actas 3er Encuentro Informatica y Gestion, 558.
MacQueen, J. B. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9thPython in Science Conference, 445, 56 –61. doi: 10.25080/Majora-92bf1922-00a
Mester, D. (1999). A parallel dichotomy algorithm for vehicle routing problem with time windows, Working paper, Minerva Optimization Center, Technion, Israel.
Miller, C., Tucker, A. & Zemlin, R. (1960). Integer Programming Formulation of Traveling Salesman Problems. Journal of the ACM (JACM), 7(4), 326–329. doi: 10.1145/321043.321046
Moen, O. (2016). The Five-step Model – Procurement to Increase Transport Efficiency for an Urban Distribution of Goods. Transportation Research Procedia, 12, 861–873. doi: 10.1016/j.trpro.2016.02.039
Mohri, M., Rostamizadeh, A. & Talwalkar, A. (2018). Foundations of Machine Learning. Cambridge, MA: MIT Press. Retrieved from https://cs.nyu.edu/~mohri/mlbook/
Moon, I., Lee, J.-H. & Seong, J. (2012). Vehicle routing problem with time windows considering overtime and outsourcing vehicles. Expert Systems with Applications, 39(18), 13202-13213. doi: 10.1016/j.eswa.2012.05.081
Nazari, M., Oroojlooy, A., Snyder, L. V., Takáč, M. (2018). Reinforcement Learning for Solving the Vehicle Routing Problem. arXiv:1802.04240v2.
Oliphant, T. E. (2006). Guide to NumPy. MDTDR.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Pemberthy, J. (2012). Implementación de un algoritmo metaheurístico para la solución de un problema de programación de transporte terrestre internacional (master thesis). National University of Colombia, Medellín, Colombia.
Pisinger, D., & Ropke, S. (2010). Large Neighborhood Search. International Series in Operations Research & Management Science, 399–419. doi: 10.1007/978-1-4419-1665-5_13
Poullet, J. (2020). Leveraging machine learning to solve the vehicle routing problem with time windows (master thesis). Massachusetts Institute of Technology, Cambridge, United States.
Prag, K., Woolway, M. & Jacobs, B. (2019). Optimising the Vehicle Routing Problem with Time Windows under Standardised Metrics. 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), Soft Computing & Machine Intelligence (ISCMI), 2019 6th International Conference On, 111–115. doi: 10.1109/ISCMI47871.2019.9004294
Qi, M., Lin, W.-H., Li, N., & Miao, L. (2012). A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows. Transportation Research Part E, 48(1), 248–257. doi: 10.1016/j.tre.2011.07.001
Rahman, A. (2012). The traveling Salesman problem. 1-15. Retrieved from http://cs.indstate.edu/~zeeshan/aman.pdf
Rochat, Y., & Taillard, É. D. (1995). Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics, 1(1), 147–167. doi: 10.1007/bf02430370
Rousseau, L.M.; Gendreau, M. & Peasant. G. (2002). Using constraint-based operators to solve the vehicle routing problem with time windows. Journal of Heuristics, 8, 43–58. doi: 10.1023/A:1013661617536
Shaw, P. (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. Lecture Notes in Computer Science, 417–431. doi: 10.1007/3-540-49481-2_30
Shin, K. & Han, S. (2011). A centroid-based heuristic algorithm for the capacitated vehicle routing problem. Computing and Informatics, 30, 721–732.
Sinnott, R. W. (1984). Virtues of the Haversine. Sky and Telescope, 68(2).
Smet, L. & Thomas, C. (2016). Local Search for the Vehicle Routing Problem (master thesis). Université Catholique de Louvain. Bélgica.
Solano, E., Montoya-Torres, J. & Guerrero-Rueda, W. (2019). A decision support system for technician routing with time windows. A case study of a Colombian public utility company. Academia Revista Latinoamericana de Administración, 23(2), 138-158. doi: 10.1108/ARLA-04-2017-0101
Solomon, M. M. (1986). On the worst-case performance of some heuristics for the vehicle routing and scheduling problem with time window constraints. Networks, 16, 161–174.
Solomon, M. M. (2005). VRPTW benchmark problems. CBA. Retrieved from http://web.cba.neu.edu/~msolomon/problems.htm
Son, H., Kim, G. & Shin, H. (2018). Case study: vehicle routing problem with time windows for a shop in fisheries wholesale market. International Journal of Management and Applied Science, 4(4), 44-47. ISSN 2394-7926.
Stehling, T. M. & de Souza, S. R. (2017). A Comparison of Crossover Operators Applied to the Vehicle Routing Problem with Time Window. 2017 Brazilian Conference on Intelligent Systems (BRACIS), 300–305. doi: 10.1109/BRACIS.2017.47
Sutton, R. S. & Barto, A. G. (2018). Reinforcement learning: an introduction. Cambridge, MA: MIT Press.
Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J.-Y. (1997). A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transportation Science, 31(2), 170–186. doi: 10.1287/trsc.31.2.170
Tamagawa, D., Taniguchi, E., & Yamada, T. (2010). Evaluating city logistics measures using a multi-agent model. Procedia - Social and Behavioral Sciences, 2(3), 6002–6012. doi: 10.1016/j.sbspro.2010.04.014
Tan, K. C., Lee, L. H., Zhu, Q. L., & Ou, K. (2001). Heuristic methods for vehicle routing problem with time windows. Artificial Intelligence in Engineering, 15(3), 281–295. doi:10.1016/s0954-1810(01)00005-x
Taner, F., Galić, A. & Carić, T. (2012). Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms. Promet – Traffic&Transportation, 24(4), 343-351.
Tirkolaee, E., Abbasian, P., Soltani, M. & Ghaffarian, S. (2019). Developing an applied algorithm for multi-trip vehicle routing problem with time windows in urban waste collection: A case study. Waste Management & Research, 37(1), 4–13. doi: 10.1177/0734242X18807001
Toro, E., Escobar A. & Granada, M. (2016). Literature review of vehicle routing problem in the green transportation context. Revista Luna Azul, 42, 362-387. doi: 10.17151/luaz.2016.42.21
Tran, T. N., Drab, K., & Daszykowski, M. (2013). Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics and Intelligent Laboratory Systems, 120, 92–96. doi: 10.1016/j.chemolab.2012.11.006
Van Rossum, G. (1993). An Introduction to Python for UNIX/C Programmers. Proceedings of the NLUUG najaarsconferentie. Dutch UNIX users group, 1-8.
Verhoeven, M. G. A., Aarts, E. H. L., & Swinkels, P. C. J. (1995). A parallel 2-opt algorithm for the Traveling Salesman Problem. Future Generation Computer Systems, 11(2), 175–182.
Wy, J., Kim, B. I. & Kim, S. (2013). The rollon–rolloff waste collection vehicle routing problem with time windows. European Journal of Operational Research, 224(3), 466–476. doi: 10.1016/j.ejor.2012.09.001
Yepes, V. (2002). Optimización heurística económica aplicada a las redes de transporte del tipo VRPTW (doctoral thesis). Universidad Politécnica de Valencia, Valencia, España.
Yu, J. J. Q., Yu, W., & Gu, J. (2019). Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 1–12. doi: 10.1109/tits.2019.2909109
Zhao, J., Mao, M., Zhao, X., & Zou, J. (2020). A hybrid of deep reinforcement learning and local search for the vehicle routing problems. IEEE Transactions on Intelligent Transportation Systems, 1–11. doi:10.1109/tits.2020.3003163