A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief

Document Type : Research Note

Authors

1 Industrial Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry, UK CV1 5DL, Coventry University

3 Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran

Abstract

Natural disasters such as earthquakes pose a constant threat to human communities. A key measure in planning and preparedness against disasters is to determine the optimal location of temporary emergency stations (TESs) and allocate them to the affected areas. Decisions in the preparedness phase ensure the optimal performance of TESs and minimize potential delays in the course of rescue operations. During crises, TESs may have a significant role in minimizing human causalities. In this research, a robust simulation-optimization approach is proposed to ensure appropriate planning in the preparedness phase. We develop a mathematical model for simultaneous and hierarchical location-allocation of the injured to the available medical facilities under disaster conditions. Given the inherent unpredictability of natural disasters, the uncertainty of the data should inevitably be taken into account. We thus employ a robust optimization technique to tackle the uncertainty in the number of the injured and utilize simulation to create the first seven days of the crisis and determine the optimal capacity of the medical facilities. The findings indicate that by eliminating the unnecessary transfer of mildly-injured victims to high-level medical facilities, the model brings about a 15 percent reduction in treatment costs.

Keywords

References

References
[1] Van Wassenhove, L. N. “Humanitarian aid logistics: supply chain management in high gear”, Journal of the operational research society., 57(5), pp. 475-489 (2006).
[2] Guha-Sapir, D., Hoyois, P., and Below, R. “Annual Disaster Statistical Review 2014”, The Numbers and Trends. CRED, Brussels., (2015).
 
[3] Özdamar, L. and Ertem, M.A. “Models, solutions and enabling technologies in humanitarian logistics”, Eur. J. Oper. Res., 244 (1), pp. 55–65 (2015).
 
[4] Nayeri, S., Asadi-Gangraj, E., and Emami, S. “Goal programming-based post-disaster decision making for allocation and scheduling the rescue units in natural disaster with time-window”, International Journal of Industrial Engineering & Production Research., 29(1), pp. 65-78 (2018).
[5] Cao, C., Li, C., Yang, Q., et al. “A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters”, Journal of Cleaner Production., 174, pp. 1422-1435 (2018).
 
[6] Every, D., & Richardson, J. “A framework for disaster resilience education with homeless communities”, Disaster Prevention and Management: An International Journal., (2018).
 
[7] Akgün, I. and Gümüşbuğa, F., Tansel, B. “Risk based facility location by using fault tree analysis in disaster management”, Omega., 52, pp. 168–179 (2015).
 
[8] Memari, P., Tavakkoli-Moghaddam, R., Partovi, M., et al. “Fuzzy dynamic location-allocation problem with temporary multi-medical centers in disaster management”, IFAC-Papers on Line., 51(11), pp.1554-1560 (2018).
 
[9] Rebeeh, Y. A., Pokharel, S., Abdella, G. M., et al. “Disaster management in industrial areas: Perspectives, challenges and future research. Journal of Industrial Engineering and Management”., 12(1), pp. 133-153 (2019).
 
[10] Eshghi, K. and Larson, RC. “Disasters: lessons from the past 105 years”. Disaster Prevention and Management., 17 (1), pp. 62– 82 (2008).
 
[11] Nabipoor Afruzi, E., Aghaie, A., and Najafi, A. “Robust optimization for the resource-constrained multi-project scheduling problem with uncertain activity durations”, Scientia Iranica., 27(1), pp. 361-376 (2020).
 
[12] Ahmadi-Javid, A., Seyedi, P., and Syam, S.S. “A survey of healthcare facility location”, Computers & Operations Research., 79, pp. 223-263 (2017).
 
[13] Ahmadzadeh, F., Mohammadi, N., and Babaie, M. “Evaluation the Emergency Response Program of Emergency Operations Command Center of the Alborz University of Medical Sciences in Response to Kermanshah Earthquake in November 2017”, Health in Emergencies and Disasters, 4(3), pp. 135-146 (2019).
 
[14] Laporte, G., Nickel, S., and Saldanha-da-Gama, F. “Introduction to location science”. In Location science, Springer., Cham., pp. 1-21 (2019).
 
[15] Hu, S. L., Han, C. F., and Meng, L. P. “Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief”, Computers & Industrial Engineering., 111, pp. 39-49 (2017).
 
[16] Alem, D., Clark, A., and Moreno, A. “Stochastic network models for logistics planning in disaster relief”, European Journal of Operational Research., 255(1), pp. 187-206 (2018).
 
[17] Beikia, H., Seyedhosseini, S. M., Ghezavati, V.R., et al. “Multi-objective Optimization of Multi-Vehicle Relief Logistics Considering Satisfaction Levels under Uncertainty”, International Journal of Engineering, IJE TRANSACTIONS B: Applications., 33(5), pp. 814-824 (2020).
 
[18] Ghasemi, P., Khalili-Damghani, K., Hafezalkotob, A., et al. “Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)”, Socio-Economic Planning Sciences., (2019).
 
[19] Li, X., Ramshani, M., and Huang, Y. “Cooperative maximal covering models for humanitarian relief chain management”, Computers & industrial engineering., 119, pp. 301-308 (2018).
 
[20] Khorsi, M., Chaharsooghi, S.K., Bozorgi-Amiri, A., et al. “A Multi-Objective Multi-Period Model for Humanitarian Relief Logistics with Split Delivery and Multiple Uses of Vehicles”, J SYST SCI SYST ENG., 29(3), pp. 360-378 (2020).
 
[21] Safaei, A. S., Farsad, S., and Paydar, M. M. “Emergency logistics planning under supply risk and demand uncertainty”, Operational Research., pp. 1-24 (2018).
 
[22] Boonmee, C., and Kasemset, Ch. “The Multi-Objective Fuzzy Mathematical Programming Model for Humanitarian Relief Logistics”. Industrial Engineering & Management Systems., 19 (1), pp. 197-210 (2020).
[23] Fazli-Khalaf, M., Khalilpourazari, S., and Mohammadi, M. “Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design”, Ann. Oper. Res., 283, pp. 1079–1109 (2019).
 
[24] Naghipour, M. and Bashiri, M. “Designing a bi-objective stochastic blood supply chain network in a disaster”, 15th Iran International Industrial Engineering Conference (IIIEC), IEEE., pp. 23-24 (2019).
 
[25] Li, X., Zhao, Z., Zhu, X., et al. “Covering models and optimization techniques for emergency response facility location and planning: a review”, Mathematical Methods of Operations Research., 74 (3), pp. 281–310 (2011).
 
[26] Liu, Y., Cui, N., and Zhang, J.  “Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service”, Transportation research part E: logistics and transportation review., 128, pp. 1-16 (2019).
 
[27] Kumar, V., Ramamritham, K., and Jana, A. “Resource allocation for handling emergencies considering dynamic variations and urban spaces: firefighting in Mumbai”, Proceedings of the tenth international conference on information and communication technologies and development., pp. 1-16 (2019).
 
[28] Memari, P., Tavakkoli-Moghaddam, R., Navazi, F., et al. “Air and ground ambulance location-allocation-routing problem for designing a temporary emergency management system after a disaster”, Institution of Mechanical Engineers, Proc IMechE Part H: J Engineering in Medicine., pp. 1–17_ IMechE (2020).
 
[29] Baharmand, H., Comesa, T., and Lauras, M. “Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters”, Transportation Research Part E., 127, pp. 86–110 (2019).
 
[30] Chen, T., Wu, Sh., Yang, J., et al. “Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters”, Complexity., 7501891, pp. 1-15 (2020).
 
[31] Verma, A. and Gaukler, G.M. “Pre-positioning disaster response facilities at safe locations: an evaluation of deterministic and stochastic modeling approaches”, Computer. Oper. Res., 62, pp. 197-209 (2015).
 
[32] Soyster, A.L. “Convex programming with set-inclusive constraints and applications to inexact linear programming”, Operations Research., 21 (5), pp. 1154–1157 (1973).
 
[33] Ben-Tal, A., Do Chung, B., Mandala, S. R., et al. “Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains”, Transportation research part B: methodological., 45 (8), pp. 1177-1189 (2011).
 
[34] Alinaghian, M., Hejazi, S. R., Bajoul, N., et al. “A Novel Robust Model for Health Care Facilities Location-Allocation Considering Pre-Disaster and Post Disaster Characteristics”, Scientia Iranica., (2021).
[35] Mamashli, Z., Nayeri, S., Tavakkoli-Moghaddam, R., et al. “Designing a sustainable–resilient disaster waste management system under hybrid uncertainty: A case study”, Engineering Applications of Artificial Intelligence., 106, pp. 104459 (2021).
 
[36] Yu, W. “Pre-disaster location and storage model for emergency commodities considering both randomness and uncertainty”, Safety Science., 141, pp. 105330 (2021).
 
[37] Tirkolaee, E. B., Aydın, N. S., Ranjbar-Bourani, M., et al. “A robust bi-objective mathematical model for disaster rescue units’ allocation and scheduling with learning effect”, Computers & Industrial Engineering., 149, pp. 106790 (2020).
 
[38] Du, J., Ji, Y., Qu, D., et al. “Three-Stage Mixed Integer Robust Optimization Model Applied to Humanitarian Emergency Logistics by Considering Secondary Disasters”, IEEE Access., 8, pp. 223255-223270 (2021).
 
[39] Sun, H., Wang, Y., Zhang, J., et al. “A robust optimization model for location-transportation problem of disaster casualties with triage and uncertainty”, Expert Systems with Applications., 175, pp. 114867 (2021).
 
[40] Ramezanian, R. and Ghorbani, M. “Stochastic optimization for the carrier selection problem in humanitarian relief”, Scientia Iranica., (2021).
 
[41] Eshghi, A., Tavakkoli-Moghaddam, R., Ebrahimnejad, S., et al. “multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty”, Scientia Iranica., (2021).
 
[42] Sotoudeh-Anvari, A., Sadjadi, S. J., Hadji Molana, S. M., et al. “A stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake”, Scientia Iranica., 26(3), pp. 1842-1864 (2019).
 
[43] Velasquez, G. A., Mayorga, M. E., and Özaltın, O. Y. “Prepositioning disaster relief supplies using robust optimization”, IISE Transactions., 52(10), pp. 1122-1140 (2020).
 
[44] Makui, A., Ashouri, F., and Barzinpoor, F. “Assignment of Injuries and Medical Supplies in Urban Crisis Management”, Journal of Applied Research on Industrial Engineering, J. Appl. Res. Ind. Eng., 6 (3), pp. 232–250 (2019).
 
[45] Sepehri, Z., Arabzad, S.M., and Sajadi, S.M. “Analysing the performance of emergency department by simulation: The case of Sirjan Hospital”, International Journal of Services and Operations Management., 20(3), pp. 289- 301 (2015).
 
[46] Sajadi, S.M., Ghasemi., Sh., and Vahdani, H. “Simulation optimisation for nurse scheduling in a hospital emergency department (case study: Shahid Beheshti Hospital)”, Int. J. Industrial and Systems Engineering., 23(4), pp. 405- 419 (2016).
 
[47] Salehi, F., Mahootchi, M., and Moattar Husseini, S.M. “Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran”, Ann Oper Res., 283, pp. 679–703 (2019).
[48] Gul, M., Fuat Guneri, A., and Gunal, M. M. “Emergency department network under disaster conditions: The case of possible major Istanbul earthquake”, Journal of the Operational Research Society., 71(5), pp. 733-747 (2020).
 
[49] Kamali, A, Sajadi, S.M., and Jolai, F. “Location of medical emergency bases with the help of a combination of optimization and simulation methods (Case study: Isfahan urban emergency stations)”, Journal of Health Information Management., 15 (2), pp. 61- 67 (2018).
 
[50] Karatas, M. and Yakıcı, E. “An analysis of p-median location problem: Effects of backup service level and demand assignment policy”, European Journal of Operational Research., 272 (1), pp. 207-18 (2018).
[51] Bertsimas, D. and Sim, M. “The price of robustness”, Operations research., 52(1), pp. 35-53 (2004).
 
Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 01 August 2022

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