Document Type : Research Paper


1 Department of Management, College of Human Science, Saveh Branch, Islamic Azad University, Saveh, Iran.

2 Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.


The natural disasters of the last few decades clearly reveal that natural disasters impose high financial and human costs on governments and communities. Concerns in this regard are growing day by day. Making the right decisions and taking appropriate and timely measures in each phase of the crisis management cycle will reduce potential damage at the time of the disaster and reduce the vulnerability of society. Therefore, in this research, a mathematical model of crisis logistics planning considering the problem of primary and secondary crisis in disaster relief is introduced, which is the innovation of this research. In the primary crisis, the goal is to provide services and relief goods to crisis areas, and in the second stage, the secondary crisis that occurs after the primary crisis seeks to provide relief to crisis centers and transfer the injured to relief centers.  Therefore, this research proposes a mathematical fuzzy ideal programming model in two primary and secondary crises. In the primary crisis, the goal is to provide services and relief goods to crisis-stricken areas. The secondary crisis, which occurs after the primary crisis, aims to support crisis-stricken centers and move injured people to relief bases in the second step. According to the proposed model, Bertsimas-Sim’s fuzzy programming that formulation proposed by Bertsimas and Sim [1] and robust approach we initially used. The Epsilon constraint method was used to solve the low-dimensional model. Multi-objective meta-heuristic algorithms have been designed to handle the computational complexity of large-scale real-time problems. Multiple comparisons and analyses have been proposed to assess the performance of the model and problem-solving capabilities. The results indicate that the proposed approach can be applied and implemented to develop a real-world humanitarian logistics network.


Main Subjects

[1]     Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35–53.
[2]     Cao, C., Li, C., Yang, Q., Liu, Y., & Qu, T. (2018). A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of cleaner production, 174, 1422–1435.
[3]     Safaei, A. S., Farsad, S., & Paydar, M. M. (2018). Robust bi-level optimization of relief logistics operations. Applied mathematical modelling, 56, 359–380.
[4]     Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International journal of disaster risk reduction, 27, 168–188. DOI:10.1016/j.ijdrr.2017.10.005
[5]     Jha, A., Acharya, D., & Tiwari, M. K. (2017). Humanitarian relief supply chain: a multi-objective model and solution. Sādhanā, 42, 1167–1174.
[6]     Goli, A., Bakhshi, M., & Babaee Tirkolaee, E. (2017). A review on main challenges of disaster relief supply chain to reduce casualties in case of natural disasters. Journal of applied research on industrial engineering, 4(2), 77–88.
[7]     Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2018). Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. European journal of operational research, 264(3), 978–993.
[8]     Chapman, A. G., & Mitchell, J. E. (2018). A fair division approach to humanitarian logistics inspired by conditional value-at-risk. Annals of operations research, 262, 133–151.
[9]     Yu, L., Zhang, C., Yang, H., & Miao, L. (2018). Novel methods for resource allocation in humanitarian logistics considering human suffering. Computers & industrial engineering, 119, 1–20.
[10]   Vahdani, B., Veysmoradi, D., Noori, F., & Mansour, F. (2018). Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International journal of disaster risk reduction, 27, 290–306.
[11]   Mohammadi, R., Ghomi, S. M. T. F., & Jolai, F. (2016). Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm. Applied mathematical modelling, 40(9–10), 5183–5199.
[12]   Zahiri, B., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2017). A novel multi-stage possibilistic stochastic programming approach (with an application in relief distribution planning). Information sciences, 385, 225–249.
[13]   Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran. Annals of operations research, 283, 679–703.
[14]   Khatami, M., Mahootchi, M., & Farahani, R. Z. (2015). Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation research part e: logistics and transportation review, 79, 1–21.
[15]   Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied mathematical modelling, 40(17–18), 7996–8016.
[16]   Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European journal of operational research, 255(1), 187–206.
[17]   Cavdur, F., Kose-Kucuk, M., & Sebatli, A. (2016). Allocation of temporary disaster response facilities under demand uncertainty: An earthquake case study. International journal of disaster risk reduction, 19, 159–166.
[18]   Xu, J., Yin, X., Chen, D., An, J., & Nie, G. (2016). Multi-criteria location model of earthquake evacuation shelters to aid in urban planning. International journal of disaster risk reduction, 20, 51–62.
[19]   Ouyed, O., & Allili, M. S. (2020). Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition. Expert systems with applications, 148, 113247. DOI:10.1016/j.eswa.2020.113247
[20]   Ling, H. F., Su, Z. L., Jiang, X. L., & Zheng, Y. J. (2021). Multi-objective optimization of integrated civilian-military scheduling of medical supplies for epidemic prevention and control [presentation]. Healthcare (switzerland) (Vol. 9, p. 126). DOI: 10.3390/healthcare9020126
[21]   Saatchi, H. M., Khamseh, A. A., & Tavakkoli-Moghaddam, R. (2021). Solving a new bi-objective model for relief logistics in a humanitarian supply chain using bi-objective meta-heuristic algorithms. Scientia iranica, 28(5 E), 2948–2971. DOI:10.24200/sci.2020.53823.3438
[22]   Makalesi, A., Hallak, J., & Miç, P. (2021). Multi criteria decision making approach to the evaluation of humanitarian relief warehouses integrating fuzzy logic: a case study in Syria. Avrupa bilim ve technolojy dergisi, 22(22), 71–80.
[23]   Abazari, S. R., Aghsami, A., & Rabbani, M. (2021). Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-economic planning sciences, 74, 100933. DOI:10.1016/j.seps.2020.100933
[24]   Momeni, B., Aghsami, A., & Rabbani, M. (2019). Designing humanitarian relief supply chains by considering the reliability of route, repair groups and monitoring route. Advances in industrial engineering, 53(4), 93–126.
[25]   Sarma, D., Das, A., Dutta, P., & Bera, U. K. (2020). A cost minimization resource allocation model for disaster relief operations with an information crowdsourcing-based MCDM approach. IEEE transactions on engineering management, 69(5), 2454–2474.
[26]   Dachyar, M., & Nilasari, T. (2020). The improvement of disaster relief distribution by accommodating internet of things (Iot) real-time data. International journal of advanced science and technology, 29(7), 3654–3664.
[27]   Cao, C., Liu, Y., Tang, O., & Gao, X. (2021). A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International journal of production economics, 235, 108081. DOI:10.1016/j.ijpe.2021.108081
[28]   Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location-transportation problem for disaster response. Computers and operations research, 41(1), 83–93. DOI:10.1016/j.cor.2013.08.001
[29]   Aghajani, M., & Torabi, S. A. (2020). A mixed procurement model for humanitarian relief chains. Journal of humanitarian logistics and supply chain management, 10(1), 45–74. DOI:10.1108/JHLSCM-10-2018-0067
[30]   Liang, B., & Han, S. (2019). Modeling and simulation of civil aviation disaster relief logistics within multiairport network [presentation]. 2019 6th international conference on frontiers of industrial engineering, icfie 2019 (pp. 48–55). DOI: 10.1109/ICFIE.2019.8907779
[31]   Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International journal of disaster risk reduction, 24, 485–498. DOI:10.1016/j.ijdrr.2017.01.017
[32]   Kahraman, C. (2016). Multiattribute warehouse location selection in humanitarian logistics using hesitant fuzzy AHP. International journal of the analytic hierarchy process, 8(2). DOI:10.13033/ijahp.v8i2.387
[33]   Barbarosolu, G., Özdamar, L., & Çevik, A. (2002). An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European journal of operational research, 140(1), 118–133. DOI:10.1016/S0377-2217(01)00222-3
[34]   Chen, A. Y., & Yu, T. Y. (2016). Network based temporary facility location for the emergency medical services considering the disaster induced demand and the transportation infrastructure in disaster response. Transportation research part B: methodological, 91, 408–423. DOI:10.1016/j.trb.2016.06.004
[35]   Chu, X., & Zhong, Q. Y. (2015). Post-earthquake allocation approach of medical rescue teams. Natural hazards, 79(3), 1809–1824. DOI:10.1007/s11069-015-1928-y
[36]   De Angelis, V., Mecoli, M., Nikoi, C., & Storchi, G. (2007). Multiperiod integrated routing and scheduling of World Food Programme cargo planes in Angola. Computers and operations research, 34(Spec.Issue), 1601–1615. DOI:10.1016/j.cor.2005.07.012
[37] 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:10.1016/j.ijpe.2015.11.007
[38] Fereiduni, M., & Shahanaghi, K. (2017). A robust optimization model for distribution and evacuation in the disaster response phase. Journal of industrial engineering international, 13(1), 117–141. DOI:10.1007/s40092-016-0173-7
[39] Zhao, M., & Liu, X. (2018). Development of decision support tool for optimizing urban emergency rescue facility locations to improve humanitarian logistics management. Safety science, 102, 110–117. DOI:10.1016/j.ssci.2017.10.007
[40] Papi, A., Pishvaee, M., & Jabbarzadeh, A. (2019). Robust optimal disaster relief logistics planning using a bi-objective robust scenario-based stochastic programming model and augmented epsilon constraint method. Disaster prevention and management knowledge (quarterly), 8(4), 349–364.
[41] Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The pre-positioned warehouse location selection for international humanitarian relief logistics. Asian journal of shipping and logistics, 34(4), 297–307.
[42] Inuiguchi, M., & Ramík, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems, 111(1), 3–28. DOI:10.1016/S0165-0114(98)00449-7