Modelos de localización de cámaras de vigilancia en una red de transporte público masivo

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Nathaly Solano-Pinzón
David Pinzón-Marroquín
William Javier Guerrero http://orcid.org/0000-0002-9807-6593

Keywords

Localización, optimización combinatoria, seguridad, teoría de grafos, redes, problema de cubrimiento de vértices.

Resumen

Este artículo estudia el problema de localización de cámaras de vigilancia aplicado a una red de transporte público masivo. Se considera una red de estaciones conectadas entre sí, mediante rutas predeterminadas de buses. El problema estudiado consiste en escoger las estaciones que deben ser vigiladas mediante cámaras con el fin de optimizar simultáneamente dos objetivos: El valor esperado del número de crímenes detectados por las cámaras, y la calidad de las imágenes captadas por el sistema de vigilancia completo. Se formulan dos modelos de optimización basados en programación entera para este problema considerando múltiples períodos, restricciones de presupuesto y restricciones de conectividad donde se busca garantizar que al menos se cuente con una cámara de vigilancia por cada pareja de estaciones conectadas directamente. Se realiza una comparación  del desempeño de los modelos matemáticos propuestos usando un optimizador comercial en un conjunto de instancias aleatorio con 20 hasta 200 estaciones. Los resultados computacionales permiten concluir sobre la capacidad de los modelos matemáticos para encontrar soluciones óptimas y los recursos computacionales requeridos. 

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Referencias

[1] M. S. Daskin, Network and discrete location: models, algorithms, and applications. John Wiley & Sons, 2011

[2] A. T. Murray, K. Kim, J. W. Davis, R. Machiraju, and R. Parent, “Coverage optimization to support security monitoring,” Computers, Environment and Urban Systems, vol. 31, no. 2, pp. 133 – 147, 2007.

[3] H. D. Sherali, J. Desai, and H. Rakha, “A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times,” Transportation Research Part B: Methodological, vol. 40, no. 10, pp. 857 – 871, 2006.

[4] A. Danczyk and H. X. Liu, “A mixed-integer linear program for optimizing sensor locations along freeway corridors,” Transportation Research Part B: Methodological, vol. 45, no. 1, pp. 208 – 217, 2011.

[5] P. Dell’Olmo, N. Ricciardi, and A. Sgalambro, “A multiperiod maximal covering location model for the optimal location of intersection safety cameras on an urban traffic network,” Procedia - Social and Behavioral Sciences, vol. 108, pp. 106 – 117, 2014.

[6] H. Park and A. Haghani, “Optimal number and location of bluetooth sensors considering stochastic travel time prediction,” Transportation Research Part C: Emerging Technologies, vol. 55, pp. 203 – 216, 2015,

[7] M. Iqbal, M. Naeem, A. Anpalagan, N. Qadri, and M. Imran, “Multi-objective optimization in sensor networks: Optimization classification, applications and solution approaches,” Computer Networks, vol. 99, pp. 134 – 161, 2016.

[8] R. Bar-Yehuda, G. Flysher, J. Mestre, and D. Rawitz, “Approximation of partial capacitated vertex cover,” SIAM Journal on Discrete Mathematics, vol. 24, no. 4, pp. 1441–1469, 2010.

[9] R. Bar-Yehuda and S. Even, “A linear-time approximation algorithm for the weighted vertex cover problem,” Journal of Algorithms, vol. 2, no. 2, pp. 198 – 203, 1981.

[10] S. Guha, R. Hassin, S. Khuller, and E. Or, “Capacitated vertex covering,” Journal of Algorithms, vol. 48, no. 1, pp. 257 – 270, 2003.

[11] R. Z. Farahani, E. Miandoabchi, W. Szeto, and H. Rashidi, “A review of urban transportation network design problems,” European Journal of Operational Research, vol. 229, no. 2, pp. 281 – 302, 2013.

[12] F. Pan and R. Nagi, “Multi-echelon supply chain network design in agile manufacturing,” Omega, vol. 41, no. 6, pp. 969 – 983, 2013.

[13] I. Contreras and E. Fernández, “General network design: A unified view of combined location and network design problems,” European Journal of Operational Research, vol. 219, no. 3, pp. 680 – 697, 2012.

[14] W. Guerrero, N. Velasco, C. Prodhon, and C. Amaya, “On the generalized elementary shortest path problem: A heuristic approach,” Electronic Notes in Discrete Mathematics, vol. 41, pp. 503 – 510, 2013.

[15] D. S. Johnson, J. K. Lenstra, and A. H. G. R. Kan, “The complexity of the network design problem,” Networks, vol. 8, no. 4, pp. 279–285, 1978.

[16] M. Leitner, “Layered graph models and exact algorithms for the generalized hop-constrained minimum spanning tree problem,” Computers & Operations Research, vol. 65, pp. 1 – 18, 2016.

[17] J. Rubaszewski, A. Yalaoui, and L. Amodeo, Solving Unidirectional Flow Path Design Problems Using Metaheuristics. Cham: Springer International Publishing, 2016, pp. 25–56.

[18] B. Rashid and M. H. Rehmani, “Applications of wireless sensor networks for urban areas: A survey,” Journal of Network and Computer Applications, vol. 60, pp. 192 – 219, 2016.

[19] G. Han, H. Xu, T. Q. Duong, J. Jiang, and T. Hara, “Localization algorithms of wireless sensor networks: a survey,” Telecommunication Systems, vol. 52, no. 4, pp. 2419–2436, 2013.

[20] M. Younis and K. Akkaya, “Strategies and techniques for node placement in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 6, no. 4, pp. 621– 655, 2008.

[21] V. K. Shetty, M. Sudit, and R. Nagi, “Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles,” Computers & Operations Research, vol. 35, no. 6, pp. 1813 – 1828, 2008.

[22] I. Bekmezci, O. K. Sahingoz, and Åžamil Temel, “Flying ad-hoc networks (fanets): A survey,” Ad Hoc Networks, vol. 11, no. 3, pp. 1254 – 1270, 2013.

[23] K. Deb, Multi-objective Optimization. Boston, MA: Springer US, 2014, pp. 403–449.

[24] R. T. Marler and J. S. Arora, “The weighted sum method for multi-objective optimization: new insights,” Structural and Multidisciplinary Optimization, vol. 41, no. 6, pp. 853–862, 2010.