Volume List  / Volume 13 (1)



DOI: 10.7708/ijtte2023.13(1).02

13 / 1 / 17-27 Pages


Branka Dimitrijević - University of Belgrade, Faculty of Traffic and Transport Engineering, Serbia, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Branislava Ratković - University of Belgrade, Faculty of Traffic and Transport Engineering, Serbia, Vojvode Stepe 305, 11000 Belgrade, Serbia -

Vladimir Simić - University of Belgrade, Faculty of Traffic and Transport Engineering, Serbia, Vojvode Stepe 305, 11000 Belgrade, Serbia -


The number and locations of facilities represent the most important decisions when modeling service networks. The facility location problem in the context of service networks is predetermined by the investment costs and/or achieving a certain standard of satisfying users’ demand. Systems designed in this way are based on the idea that they will function in regular exploitation conditions, without any interference. However, various adverse events caused by intent, unintentional human activities, technological disasters or natural disasters can lead to a partial or complete cessation of the service networks. For the first time, this paper highlights the importance of the impact assessment of disruption events on the service networks where the r-interdiction median location model is presented as a potential solution approach in a case when these events occur. Also, an extensive overview of the state-of-the-art literature is provided. Finally, a numerical example of the determination of the most vulnerable points of service networks is given to illustrate the effects of potential disruptions, as well as appropriate preventive actions that eliminate or at least mitigate those situations.

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Azad, N.; Saharidis, G. K. D.; Davoudpour, H.; et al. 2013. Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach, Annals of Operations Research 210: 125–163.


Berman, O.; Krass, D.; Menezes, M. B. C. 2007. Facility Reliability Issues in Network p-Median Problems: Strategic Centralization and Co-location Effects, Operations Research 55(2): 332–350.


Bhuiyan, T. H.; Medal, H. R.; Harun, S. 2020. A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption, European Journal of Operational Research 285(2): 670-694.


Church, R. L.; Scaparra, M. P. 2007. Protecting Critical Assets: The r-Interdiction Median Problem with Fortification, Georaphical Analysis 39(2): 129–146.


Church, R. L.; Scaparra, M. P.; Middleton, R. S. 2004. Identifying critical infrastructure: the median and covering facility interdiction problems, Annals of the Association of American Geographers 94: 491–502.


Forghani, A.; Dehghanian, F.; Salari, M.; Ghiami, Y. 2020. A bi-level model and solution methods for partial interdiction problem on capacitated hierarchical facilities, Computers & Operations Research 114: 104831.


Hien, L. T. K.; Sim, M.; Xu, H. 2020. Mitigating interdiction risk with fortification, Operations Research 68(2): 348–362.


Laporte, G.; Nickel, S.; Saldanha da Gama, F. 2015. Location Science, Springer, Switzerland.


Lei, T. L. 2019. Evaluating the vulnerability of time-sensitive transportation networks: A hub center interdiction problem, Sustainability 11(17): 4614.


Li, Q.; Li, M.; Gong, Z.; Tian, Y.; Zhang, R. 2022. Locating and protecting interdependent facilities to hedge against multiple non-cooperative limited choice attackers, Reliability Engineering & System Safety 223: 108440.


Li, S.; Chen, H.; Wang, M.; Heidari, A. A.; Mirjalili, S. 2020. Slime mould algorithm: A new method for stochastic optimization, Future Generation Computer Systems 111: 300-323.


Maleki, H. R.; Khanduzi, R. 2015. Modeling r-interdiction median problem with fortification in a fuzzy environment. In 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 1-5.


Snyder, L. V.; Daskin, M. S. 2005. Reliability models for facility location: the expected failure cost case, Transportation Science 39: 400–416.


Starita, S.; Scaparra, M. P; O’Hanley, J. R. 2017. A dynamic model for road protection against flooding, Journal of the Operational Research Society 68: 74–88.


Tu, J.; Chen, H.; Wang, M.; Gandomi, A. H. 2021. The colony predation algorithm, Journal of Bionic Engineering 18(3): 674-710.


Wang, G. G. 2018. Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems, Memetic Computing 10(2): 151-164.


Wang, G. G.; Deb, S.; Coelho, L. D. S. 2015. Elephant herding optimization. In Proceedings of the 3rd international symposium on computational and business intelligence (ISCBI), 07-09 December, Bali, Indonesia, IEEE (2015), 1-5.


Wang, G. G.; Deb, S.; Coelho, L. D. S. 2018. Earthworm optimisation algorithm: A bio-inspired metaheuristic algorithm for global optimisation problems, International Journal of Bio-Inspired Computation 12(1): 1-22.


Yang, Y.; Chen, H.; Heidari, A. A.; Gandomi, A. H. 2021. Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Systems with Applications 177: 114864.


Zhang, K.; Li, X.; Jin, M. 2021. Efficient Solution Methods for a General r-Interdiction Median Problem with Fortification, INFORMS Journal on Computing 34(2): 1272–1290.


Zhu, Y.; Zheng, Z.; Zhang, X.; Cai, K. 2013. The r-interdiction median problem with probabilistic protection and its solution algorithm, Computers & Operations Research 40(1): 451–462.

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