Ruteo de vehículos multi-objetivo en dos niveles para logística urbana inteligente
DOI:
https://doi.org/10.32480/rscp.2018-23-1.123-138Palabras clave:
Problema de Ruteo de Vehículos, Logística Multinivel, Movimiento de Bienes Urbanos, Optimización Multi-Objetivo, Algoritmo Evolutivo Multi-ObjetivoResumen
Las actuales tendencias de urbanización y economías crecientes conllevan aumentos en la congestión de tráfico, por lo que los gobiernos municipales se ven obligados a recurrir a nuevas estrategias para enfrentar tales desafíos. La distribución multi-nivel es una estrategia ya conocida y empleada por empresas, donde la formulación clásica del Problema de Ruteo de Vehículos en Dos Niveles (2E-VRP, por sus siglas en inglés) refleja la perspectiva de un único proveedor, sin considerar las decisiones de ruteo de terceros. La falta de coordinación entre proveedores ejecutando sus planificaciones individuales y, consecuentemente, la falta de un enfoque holístico al tráfico urbano puede causar más problemas. En este artículo se presenta una formulación multi-objetivo del 2E-VRP multi-proveedor con flotas heterogéneas de vehículos desde la perspectiva del gobierno municipal en el contexto del Movimiento de Bienes Urbanos. Adicionalmente, se presenta un Algoritmo Evolutivo Multi-Objetivo para la resolución del problema propuesto con validaciones experimentales de la calidad de soluciones obtenidas
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26. Han S, Tabata Y. A Hybrid Genetic Algorithm for the Vehicle-Routing Problem with Controlling Lethal Gene. Asia Pacific Management Review. 2002;7(3):405-425.
27. Zitzler E, Thiele L. Multi-Objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation.1999;257-271.
2. Laporte G. The Vehicle Routing Problem: An Overview of Exact and Approximate Algorithms. European Journal of Operational Research. 1992;59(3):345-358.
3. Crainic TG, Ricciardi N, Storchi G. Advanced Freight Transportation Systems for Congested Urban Areas: Transportation Research Part C. Emerging Technologies. 2004;12(2):119-137.
4. González-Feliu J. Models and Methods for the City Logistics: the Two-Echelon Capacitated Vehicle Routing Problem. Disertación Ph.D., Politecnico di Torino; 2008.
5. González-Feliu J. Vehicle Routing in Multi-echelon Distribution Systems with Cross-docking: A Systematic Lexical-metanarrative Analysis. Computer and Information Science. 2013;6(3):28-37.
6. Cuda R, Guastaroba G, Speranza MG. A Survey on Two-Echelon Routing Problems. Computers & Operations Research. 2015;55:185-199.
7. Hemmelmayr VC, Cordeau JF, Crainic TG. An Adaptive Large Neighborhood Search Heuristic for Two-Echelon Vehicle Routing Problems Arising in City Logistics. Computers & Operations Research. 2012;39(12):3215-3228.
8. Hernández-Pérez H, Salazar-González JJ. The Multi-commodity Pickup-and-Delivery Traveling Salesman Problem. Networks. 2014;63(1):46-59.
9. Coello Coello CA, Lamoun GB, Van Veldhuizen DA. Evolutionary Algorithms for Solving Multi-Objective Problems; 2007.
10. Crainic TG, Mancini S, Perboli G, Tadei R. Clustering-based Heuristics for the Two- Echelon Vehicle Routing Problem. Montreal, Canada: Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation; 2008.
11. Crainic TG, Mancini S, Perboli G, Tadei R. Multi-start Heuristics for the Two-Echelon Vehicle Routing Problem. En European Conference on Evolutionary Computation in Combinatorial Optimization. Springer; 2011. p. 179-190.
12. Crainic TG, Mancini S, Perboli G, Tadei R. GRASP with Path Relinking for the Two- Echelon Vehicle Routing Problem. En Advances in Metaheuristics. Springer; 2013, p. 113-125. 13.
13. Crainic TG, Ricciardi N, Storchi G. Models for Evaluating and Planning City Logistics Systems. Transportation Science. 2009;43(4):432-454.
14. Perboli G, Tadei R. New Families of Valid Inequalities for the Two-Echelon Vehicle Routing Problem. Electronic Notes in Discrete Mathematics. 2010;36:639-646.
15. Perboli G, Tadei R, Vigo D. The Two-Echelon Capacitated Vehicle Routing Problem: Models and Math-based Heuristics. Transportation Science. 2011;46(3):364-380.
16. Jepsen M, Spoorendonk S, Ropke S. A Branch-and-Cut Algorithm for the Symmetric Two-Echelon Capacitated Vehicle Routing Problem. Transportation Science.2013;47(1):23-37.
17. Baldacci R, Mingozzi A, Roberti R, Calvo RW. An Exact Algorithm for the Two- Echelon Capacitated Vehicle Routing Problem. Operations Research. 2013;61(2):298- 314.
18. González-Feliu J, Perboli G, Tadei R, Vigo D. The Two-Echelon Capacitated Vehicle Routing Problem; 2008.
19. Russo F, Comi A. City Characteristics and Urban Goods Movements: A Way to Environmental Transportation System in a Sustainable City. Procedia-Social and Behavioral Sciences. 2012;39: 61-73.
20. Silva AB, Ribeiro A. An Integrated Planning for Cities to Promote Sustainable Mobility. En Proceedings of European Transport Conference. Citeseer, 2009.
21. Van Vliet O, Brouwer AS, Kuramochi T, van Den Broek M, Faaij A. Energy Use, Cost, and CO 2 Emissions of Electric Cars. Journal of Power Sources. 2011;196(4):2298-2310.
22. Barán B, Scharer M. A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows. En Applied Informatics. 2003; p. 97-102.
23. Andrade CE, Miyazawa FK, Resende MG. Evolutionary Algorithm for the K- interconnected Multi-depot Multi-traveling Salesmen Problem. En Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation; 2013. p. 463-470.
24. Gee SG, Arokiasami WA, Jiang J, Tan KC. Decomposition-based multi-objective evolutionary algorithm for vehicle routing problema with stochastic demands. Soft Computing. 2016;20(9):3443-3453.
25. Deb K, Pratap A, AgarwalS, Meyarivan TAMT. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation.2002;6(2):182-197.
26. Han S, Tabata Y. A Hybrid Genetic Algorithm for the Vehicle-Routing Problem with Controlling Lethal Gene. Asia Pacific Management Review. 2002;7(3):405-425.
27. Zitzler E, Thiele L. Multi-Objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation.1999;257-271.
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13.10.2018
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Ruteo de vehículos multi-objetivo en dos niveles para logística urbana inteligente. Rev. Soc. cient. Py. [Internet]. 2018 Oct. 13 [cited 2025 Nov. 5];23(1):123-38. Available from: http://www.sociedadcientifica.org.py/ojs/index.php/rscpy/article/view/40
