Document Type : Research Paper


1 Department of Industrial Engineering, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Payame Noor University, Tehran, Iran.


The development of cell sites as part of the infrastructure of telecommunication technology is playing a unique role in emerging businesses at present. Natural disasters and crises can disrupt communication equipment and create severe challenges in service provisions, especially health and security, by damaging sites. This might lead to traffic congestion in certain network sections, causing chaos and social crises and increasing the commissioning and equipping costs of backup sites for operators. This study developed an integrated location–coverage–allocation model to improve sustainability through maximum coverage, enhanced flexibility, and minimized overhead expense by determining the position of backup sites and mitigating environmental pollution resulting from the establishment of sites. The stochastic robust optimization model was employed to control the effect of nonparametric uncertainty, while acceptable solutions were generated using the Lagrangian relaxation to address complicated model constraints.


Main Subjects

  1. Pokorny, J., Seda, P., Seda, M., & Hosek, J. (2021). Modeling optimal location distribution for deployment of flying base stations as on-demand connectivity enablers in real-world scenarios. Sensors, 21(16), 5580.
  2. Amin-Tahmasbi, H., Raheb, M., & Jafariyeh, S. (2018). A green optimization model in closed-loop supply chain with the aim of increasing profit and reducing environmental problems, with regard to product guaranty period. Journal of operational research in its applications (applied mathematics)-lahijan azad university, 15(3), 27-44. (In Persian).
  3. Bahrami, M., & Seif Barghi, M. (2013). Modeling the problem of locating facilities with regard to the reliability. The second national industrial and systems engineering conference, Isfahan, Iran. Civilica. (In Persian).
  4. Ashabniad, M., & Mosafa, N. (2016). Duties and mutual powers of the affected government and international organizations in natural disaster relief. Public law studies quarterly, 47(3). (In Persian).
  5. Ahmadi Choukolaei, H., Jahangoshai Rezaee, M., Ghasemi, P., & Saberi, M. (2021). Efficient crisis management by selection and analysis of relief centers in disaster integrating GIS and multicriteria decision methods: a case study of Tehran. Mathematical problems in engineering, 2021. (In Persian). DOI: 1155/2021/5944828
  6. Hosseini, S. Z., Rezaei, A., & Ebrahimi Dehkordi, A. (2016). Identifying and measuring urban resilience capacities with emphasis on earthquake (case study: chamestan city). 3rd International conference on new horizons in civil engineering, architecture and urban planning. (In Persian).
  7. Guan, H., Li, J., Cao, S., & Yu, Y. (2016). Use of mobile LiDAR in road information inventory: a review. International journal of image and data fusion, 7(3), 219-242
  8. Mehrabi, N. (2014). The applied function of ICT Tools in crisis management. Journal of the army department of paramedicine, Islamic republic of Iran, 9(1), 48-53. (In Persian).
  9. Yazadi, A. (2012). Presenting a three-objective mathematical model for the problem of maximum coverage of the allocation location set in the three-tier supply chain, Doctoral dissertation, master thesis in industrial engineering, Al-zahra university, October. (In Persian).
  10. Church, R., & ReVelle, C. (1974). The maximal covering location problem. Papers of the regional science association, 30(1), 101-118.
  11. Hajian, S., Afshar Kazemi, M. A., Seyed Hosseini, S. M., Tolouei Ashlaghi, A. (2018). Developing a multi-objective model for locating-routing-inventory problem in a multi-period and multi-product green closed-loop supply chain network for perishable products. Quarterly journal of industrial management, 11(1), 83-110. (In Persian). DOI: 22059/imj.2019.275295.1007558
  12. Akbarpour, H., Akbarpour, M., Alinejad, N., Bozorgi Amiri, A. (2011). Pages providing a model for strengthening and covering mobile towers in order to survive in natural disasters. Scientific quarterly of industrial management studies, 17(53), 159-184. (In Persian).
  13. Tayal, A., Solanki, A., & Singh, S. P. (2020). Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable cities and society, 62, 102383.
  14. Qamar, F., Hindia, M. H. D., Dimyati, K., Noordin, K. A., & Amiri, I. S. (2019). Interference management issues for the future 5G network: a review. Telecommunication systems71(4), 627-643.
  15. Pedersen, S., Aschemann-Witzel, J., & Thøgersen, J. (2018). Consumers' evaluation of imported organic food products: The role of geographical distance. Appetite, 130, 134-145.
  16. Ghayvat, H., Awais, M., Gope, P., Pandya, S., & Majumdar, S. (2021). Recognizing suspect and predicting the spread of contagion based on mobile phone location data (counteract): a system of identifying covid-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence. Sustainable cities and society, 69, 102798. DOI: 1016/j.scs.2021.102798
  17. Hamidieh, A., & Arshadikhamseh, A. (2021). The flexible possibilistic-robust mathematical programming approach for the resilient supply chain network: an operational plan. Journal of advanced manufacturing systems, 20(3), 473-498.
  18. Report of statistical indicators of communication and information technology sector. (2020). Vice president of strategy and market development, office of planning, budgeting and program control. (In Persian). Retrieved from
  19. Barendse, A. (2004). Innovative regulatory and policy initiatives at increasing ICT connectivity in South Africa. Telematics and informatics, 21(1), 49-66.
  20. Balakrishnan, A., Magnanti, T. L., & Wong, R. T. (1995). A decomposition algorithm for local access telecommunications network expansion planning. Operations research, 43(1), 58-76.
  22. Buys, P., Dasgupta, S., Thomas, T. S., & Wheeler, D. (2009). Determinants of a digital divide in sub-saharan Africa: a spatial econometric analysis of cell phone coverage. World development, 37(9), 1494-1505.
  23. Vinu, P. V., Sherimon, P. C., & Krishnan, R. (2011). Towards pervasive mobile learning–the vision of 21st century. Procedia-social and behavioral sciences, 15, 3067-3073.
  24. Wilson, M. W. (2012). Location-based services, conspicuous mobility, and the location-aware future. Geoforum, 43(6), 1266-1275.
  25. Chen, D., & Chen, R. (2013). Optimal algorithms for the α-neighbor p-center problem. European journal of operational research, 225(1), 36-43.
  26. Lemamou, E. A., Chamberland, S., & Galinier, P. (2013). A reliable model for global planning of mobile networks. Computers and operations research, 40(10), 2270-2282.
  27. Khalafi, S., & Tavakoli Moghaddam, R. (2013). A new approach to locating mobile telecommunication stations with two-level service and possible demand for full subscriber coverage. 2nd national conference on industrial and systems engineering. Civilica. (In Persian).
  28. Peppanen, J., Reno, M. J., Thakkar, M., Grijalva, S., & Harley, R. G. (2015). Leveraging AMI data for distribution system model calibration and situational awareness. IEEE transactions on smart grid, 6(4), 2050-2059.
  29. Lee, Y. L., Chuah, T. C., Loo, J., & Vinel, A. (2014). Recent advances in radio resource management for heterogeneous LTE/LTE-A networks. IEEE Communications surveys and tutorials, 16(4), 2142-2180.
  30. Lee, G. (2015). 3D coverage location modeling of Wi-Fi access point placement in indoor environment. Computers, environment and urban systems, 54, 326-335.
  31. Tohidinasab, P., Syed Boyer, S., & Taybi Iraqi, M. (2015). Presenting a model for locating telecommunication antennas taking into account the capacity constraints and uncertain demand of subscribers. The second international conference on new research achievements in mechanics, industry and aerospace. Civilica. (In Persian).
  32. Asghar, M. Z., Nieminen, P., Hämäläinen, S., Ristaniemi, T., Imran, M. A., & Hämäläinen, T. (2017). Towards proactive context-aware self-healing for 5G networks. Computer networks, 128, 5-13.
  33. Akpakwu, G. A., Silva, B. J., Hancke, G. P., & Abu-Mahfouz, A. M. (2017). A survey on 5G networks for the Internet of Things: Communication technologies and challenges. IEEE access, 6, 3619-3647.
  34. Akhtar, S. (2009). Evolution of technologies, standards, and deployment of 2G-5G networks. In Encyclopedia of multimedia technology and networking, second edition (pp. 522-532). IGI Global.
  35. Santhi, K. R., Srivastava, V. k., SenthilKumaran, G., & Butare, A. (2003). Goals of true broad band's wireless next wave (4G-5G). 2003 IEEE 58th vehicular technology conference (pp. 2317-2321). IEEE.
  36. Goodarzian, F., Bahrami, F., & Shishebori, D. (2022). A new location-allocation-problem for mobile telecommunication rigs model under crises and natural disasters: a real case study. Journal of ambient intelligence and humanized computing, 13(5), 2565-2583.
  37. Janevski, T. (2009, January). 5G mobile phone concept. 2009 6th IEEE consumer communications and networking conference (pp. 1-2). IEEE.
  38. Soleimani, M., Khalilzadeh, M., Bahari, A., & Heidary, A. (2021). NSGA-II algorithm for hub location-allocation problem considering hub disruption and backup hub allocation. World journal of engineering. Advance online publication.
  39. Khalili-Damghani, K., Tavana, M., & Ghasemi, P. (2022). A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of operations research, 309(1), 103-141.
  40. Dinu, S., & Ciucur, V. (2016, August). Location-allocation models and new solution methodologies in telecommunication networks. IOP conference series: materials science and engineering, 145(8).
  41. Comprehensive Health Guidelines and Instructions for Radiation Workers. (2012). Medical University of Tehran. (In Persian). Retrieved from
  42. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
  43. Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers and operations research37(4), 668-683. DOI: 1016/j.cor.2009.06.017
  44. Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64, 385-397.
  45. Canzar, S. (2008). Lagrangian relaxation - solving np-hard problems in computational biology via combinatorial optimization (Master of Thesis, University of Lorraine). Retrieved from
  46. Tavakoli Moqaddam, R., Hosseini, S. M., & Amouzad Khalili, H. (2018). Modeling and solving the problem of supply chain construction based on order in conditions of production capacity limitation using lagrangian relaxation algorithm. Journal of industrial engineering research in production systems, 7(14), 143-157. (In Persian).
  47. Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using lagrangian relaxation. Transportation research part E: logistics and transportation review, 134, 101764.
  48. Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers and industrial engineering, 122, 95-105.
  49. Diabat, A., & Richard, J. P. P. (2015). An integrated supply chain problem: a nested lagrangian relaxation approach. Annals of operations research, 229(1), 303-323.
  50. Pakravan, P., & Behnamian, J. (2019). Undesirable facility location under uncertainty. Modeling and algorithm, 10(1), 1-23. DOI: 22108/jpom.2018.102029.1017
  51. Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International journal of production economics, 183, 700-709. DOI: 1016/j.ijpe.2015.11.007
  52. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation research part e: logistics and transportation review, 48(6), 1152-1168.
  53. Abou Kasm, O., & Diabat, A. (2019). The quay crane scheduling problem with non-crossing and safety clearance constraints: an exact solution approach. Computers and operations research, 107, 189–199. DOI: 1016/j.cor.2019.03.014
  54. Mahmoudi Rad, A., Niroumand, S., Sanei, M., & Sajedinejad, A. (2016). Lagrange release method for transportation problem with fixed cost stages. New research in mathematics (basic sciences of Islamic azad university), 3(10), 19-30. (In Persian).
  55. Kim, B. S., Park, H., Kim, K. H., Godfrey, D., & Kim, K. I. (2017). A survey on real-time communications in wireless sensor networks. Wireless communications and mobile computing, 2017, 1864847.
  56. Demiane, F., Sharafeddine, S., & Farhat, O. (2020). An optimized UAV trajectory planning for localization in disaster scenarios. Computer networks, 179, 107378.
  57. Monakhov, Y. M., Monakhov, M. Y., & Telny, A. V. (2019). Improving the accuracy of local positioning for mobile network nodes using satellite navigation systems. 2019 Ural symposium on biomedical engineering, radioelectronics and information technology (USBEREIT) (pp. 424-427). IEEE.
  58. Preece, G., Shaw, D., & Hayashi, H. (2015). Application of the viable system model to analyse communications structures: a case study of disaster response in Japan. European journal of operational research243(1), 312-322.