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Methodological Model for the Solution of Periodic Customer Scheduling in Routing Problems

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Trends in Sustainable Smart Cities and Territories (SSCT 2023)

Abstract

Transportation logistics represents a challenge in the efficient management of supply chains of products and services in urban centers that require effective solutions to properly manage operations, one of these points is linked to the creation of routes based on the demand of users considering geographical conditions, size, and capacity of the fleet, points that have the opportunity to be integrated into the Vehicle Routes theme. This initiative recommends a methodological model to solve the periodic scheduling of consumers associated with the PVRP, where the service is defined by the frequency of visits to the nodes; to be implemented in an information technology (IT) industry for logistics management in the city of Manizales—SIGMA Ingeniería—to validate the development of a logistics platform based on step-by-step consumer scheduling.

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References

  1. Arvianto, A., Sopha, B.M., Asih, A.M.S., Imron, M.A.: City logistics challenges and innovative solutions in developed and developing economies: a systematic literature review. Int. J. Eng. Bus. Manag. 13 (2021). https://doi.org/10.1177/18479790211039723

  2. Zhou, W., Song, T., He, F., Liu, X.: Multiobjective vehicle routing problem with route balance based on genetic algorithm. Discrete Dyn. Nat. Soc. (2013). https://doi.org/10.1155/2013/325686

  3. Vieira, B.S., Ribeiro, G.M., Bahiense, L.: Metaheuristics with variable diversity control and neighborhood search for the Heterogeneous site-dependent multi-depot multi-trip periodic vehicle routing problem. Comput. Oper. Res. 153, 106189 (2023). https://doi.org/10.1016/J.COR.2023.106189

  4. Scheinberg, K., et al.: MOS-SIAM Series on Optimization (2014). Accessed 01 Mar 2023. www.ibm.com

  5. Mindur, L., Mindur, M.: Intermodal transport on selected continents. Stud. Syst. Decis. Control 400, 125–195 (2022). https://doi.org/10.1007/978-3-030-87120

    Article  Google Scholar 

  6. Francis, P., Smilowitz, K., Tzur, M.: The period vehicle routing problem with service choice. 40(4), 439–454 (2006). https://doi.org/10.1287/TRSC.1050.0140

  7. Beltrami, E.J., Bodin, L.D.: Networks and vehicle routing for municipal waste collection. Networks 4(1), 65–94 (1974). https://doi.org/10.1002/NET.3230040106

  8. Christofides, N., Beasley, J.E.: The period routing problem. Networks 14(2), 237–256 (1984). https://doi.org/10.1002/NET.3230140205

    Article  MATH  Google Scholar 

  9. Gaudioso, M., Paletta, G.: A heuristic for the periodic vehicle routing problem. Transp. Sci. 26(2), 86–92 (1992). https://doi.org/10.1287/TRSC.26.2.86

    Article  MATH  Google Scholar 

  10. Cordeau, et al.: VRP-REP: the vehicle routing problem repository (1997). www.vrp-rep.org/references/item/cordeau-et-al-1997.html. Accessed 01 Mar 2023

  11. Chao, I.-M., Golden, B.L., Wasil, E.: An improved heuristic for the period vehicle routing problem. Networks 26(1), 25–44 (1995). https://doi.org/10.1002/NET.3230260104

  12. Hildebrandt, F.D., Thomas, B.W., Ulmer, M.W.: Opportunities for reinforcement learning in stochastic dynamic vehicle routing. Comput. Oper. Res. 150, 106071 (2023). https://doi.org/10.1016/J.COR.2022.106071

  13. Ulmer, M.W.: Dynamic pricing and routing for same-day delivery. 54(4), 1016–1033 (2020). https://doi.org/10.1287/TRSC.2019.0958

  14. Bagloee, S.A., Tavana, M., Asadi, M., Oliver, T.: Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J. Modern Transp. 24(4), 284–303 (2016). https://doi.org/10.1007/S40534-016-0117-3/FIGURES/5

    Article  Google Scholar 

  15. Ikotun, A.M., Ezugwu, A.E., Abualigah, L., Abuhaija, B., Heming, J.: K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. (N Y) 622, 178–210 (2023). https://doi.org/10.1016/J.INS.2022.11.139

    Article  Google Scholar 

  16. Ay, M., Özbakir, L., Kulluk, S., Gülmez, B., Öztürk, G., Özer, S.: FC-K means: Fixed-centered K-means algorithm. Expert Syst. Appl. 211, 118656 (2023). https://doi.org/10.1016/J.ESWA.2022.118656

  17. Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding

    Google Scholar 

  18. Mannor, S., et al.: K-means clustering. In: Encyclopedia of Machine Learning, pp. 563–564 (2011). https://doi.org/10.1007/978-0-387-30164-8425

  19. Nagy, M., Negru, D.: Using clustering software for exploring spatial and temporal patterns in non-communicable diseases. Euro. Sci. J. ESJ 10(33), 1857–7881 (2014). https://doi.org/10.19044/ESJ.2014.V10N33P

  20. Byun, J.W., Kamra, A., Bertino, E., Li, N.: Efficient k-anonymization using clustering techniques. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4443, pp. 188–200. LNCS (2007). https://doi.org/10.1007/978-3-540-71703-418

  21. Rani, S., Kholidah, K.N., Huda, S.N.: A development of travel itinerary planning application using traveling salesman problem and k-means clustering approach. In: ACM International Conference Proceeding Series, pp. 327–331 (2018). https://doi.org/10.1145/3185089.3185142

  22. Fuentes, G.E.A., Gress, E.S.H., Mora, J.C.S.T., Marín, J.M.: Solución al Problema de Secuenciación de Trabajos mediante el Problema del Agente Viajero. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial 13(4), 430–437 (2016). https://doi.org/10.1016/J.RIAI.2016.07.003

  23. el Krari, M., Ahiod, B., el Benani, Y.B.: A pre-processing reduction method for the generalized travelling salesman problem. Oper. Res. 21(4), 2543–2591 (2021). https://doi.org/10.1007/S12351-019-00533-W/TABLES/10

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Correspondence to Restrepo Franco Alejandra María .

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Alejandra María, R.F. et al. (2023). Methodological Model for the Solution of Periodic Customer Scheduling in Routing Problems. In: Castillo Ossa, L.F., Isaza, G., Cardona, Ó., Castrillón, O.D., Corchado Rodriguez, J.M., De la Prieta Pintado, F. (eds) Trends in Sustainable Smart Cities and Territories . SSCT 2023. Lecture Notes in Networks and Systems, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-031-36957-5_18

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