Hybrid optimization of urban transport networks through linear programming and genetic algorithms for the reduction of operating costs
DOI:
https://doi.org/10.55204/trc.v6i1.e685Keywords:
Transmission network optimization, linear programming, genetic algorithms, cost minimization, hybrid modelsAbstract
Efficiency in transport networks is essential to reduce operating costs and improve service. This study presents a hybrid approach that combines linear programming and genetic algorithms to optimize transport networks with the aim of minimizing costs. A mathematical model was developed that integrates both techniques, applying it to a representative case of an urban network with multiple nodes and routes. Linear programming was used to obtain a feasible initial solution with basic costs, while the genetic algorithm refined this solution by exploring a wider space and adapting to the nonlinear complexity of the problem. The results showed a significant 12.5% reduction in operating costs compared to traditional methods. This hybrid approach combines the precision and speed of linear programming with the flexibility and adaptability of genetic algorithms, allowing complex and dynamic problems to be tackled. In addition, it contributes to improving the quality of service, adapting to changing conditions and offering a robust tool for urban transport planners and operators.Downloads
References
Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network Flows: Theory, Algorithms, and Applications. Prentice Hall.
Benites Yarasca, A. W. (2023). Optimización del transporte urbano en Lima mediante la aplicación de los algoritmos genéticos de Tabú y Colonia de Hormigas. Repositorio UPN. Recuperado de https://repositorio.upn.edu.pe/bitstream/handle/11537/37668/Benites%20Yarasca%20Alfonso%20William.pdf?sequence=1
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR), 35(3), 268-308.
Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd edition). Springer.
Cormen, T. H., Leiserson, C.E., Rivest, R.L., & Stein, C. (2009). Introduction to Algorithms (3rd edition). MIT Press.
Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.
Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.
Gendreau, M., Hertz, A., & Laporte, G. (1994). A tabu search heuristic for the vehicle routing problem. Management Science, 40(10), 1276-1290.
Genovard Abad, M. (2016). Optimización de rutas de transporte público con algoritmos genéticos. Universidad Abierta de Cataluña. Recuperado de https://openaccess.uoc.edu/bitstream/10609/59085/7/mgenovardTFM1216memoria.pdf
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
Hertz, A., & Taillard, É. (2002). Heuristic methods for vehicle routing problems. In The Vehicle Routing Problem, SIAM.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-72.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University.
Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408-416.
Lawler, E.L., & Wood, D.E. (1966). Branch-and-bound methods: A survey. Operations Research, 14(4), 699-719.
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs (3rd edition). Springer.
Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research, 31(12), 1985-2002.
Quijano-Crisóstomo, I. A., Seck-Tuoh-Mora, J. C., Medina-Marín, J., Hernández-Romero, N., & Anaya-Fuentes, G. E. (2024). Modelo de optimización basado en algoritmos genéticos para el diseño de nuevas rutas de transporte escolar en una Universidad Pública del Estado de Hidalgo. Pädi Boletín Científico de Ciencias Básicas e Ingeniería del ICBI, 12 (Especial 3). Recuperado de https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/13420
Reeves, C. R. (1995). A genetic algorithm for flowshop sequencing. Computers & Operations Research, 22(1), 5-13.
Reinelt, G. (1994). The Traveling Salesman: Computational Solutions for TSP Applications. Springer.
Silva, V. D., Finamore, A., & Henriques, R. (2022). Sobre el papel de la optimización multiobjetivo en el problema de diseño de redes de transporte. Recuperado de https://arxiv.org/abs/2201.11616
Terrero Huerta, A., Hernández Hernández, J., & Hernández Hernández, M. (2024). La programación lineal se aplicaba a la optimización de rutas de transporte. Conocimiento Interconectado, 9(18), 55-64. Recuperado de https://is.uv.mx/index.php/IS/article/download/2889/4713/13690
Toth, P., & Vigo, D. (2014). Vehicle Routing: Problems, Methods, and Applications. SIAM.
Zhang, R., & Wang, Y. (2010). Hybrid genetic algorithm for vehicle routing problem with time windows. Expert Systems with Applications, 37(1), 430-436.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nancy Elizabeth Chariguamán Maurisaca
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors retain the moral and patrimonial rights of their works. They only give to the magazine Tesla Revista Científica the right to the first publication of this. Since Tesla Revista Científica is an open access publication, readers can fully or partially reproduce its content as long as they properly credit the corresponding authors and the journal itself. Tesla Revista Científica undertakes not to make commercial use of the texts it receives and/or publishes.
Our journal is governed by the international policies SHERPA/RoMEO: Green journal: They allow the self-archiving of both the pre-print (draft of a paper) and the post-print (the version corrected and reviewed by peers) and even the final version ( layout as it will be published in the journal).
See also "Copyright and licences".
