A Location-Inventory Model for Casualty Response Planning in Crisis Situations

Document Type : Original Article


1 Assocaite Professor, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

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


When it comes to providing aid to the victims of natural and unnatural disasters, the main goal of a relief chain is to provide the items needed by the victims such as water and food, medicine, shelter and other necessities to reduce the number of deaths caused by reduce the occurrence of disasters as much as possible; therefore, designing, developing and implementing a relief chain can play an important role in finding a suitable answer. The most obvious differences in dealing with the relief supply chain are the unpredictability of demand in terms of time, place, type, scale and volume. Other reasons such chains are the sudden occurrence of a large amount of demand and a very short opportunity to provide a large amount of goods, lack of resources including goods, relief forces, appropriate technology, transportation capacity, the need to provide timely and sufficient supplies after the accident, and the risks in the relief environment. In the present research, a mathematical model for the location-inventory problem for planning response to casualties is presented; also, due to the NP-hard nature of the problem considered, meta-heuristic algorithms were used to solve it.


Main Subjects

  1. Afshar Najafi, B., & Ghorbani, S. (2020). modeling and solving the problem of location of cross-warehouses and vehicle scheduling in the multi-product supply chain with the possibility of discrete harvesting and delivery. The Journal of Industrial Management Perspective, 11(2), 2-8. (In Persian).
  2. Ahmadzadeh, E. and Vahdani, B., 2017. A location-inventory-pricing model in a closed loop supply chain network with correlated demands and shortages under a periodic review system. Computers & chemical engineering, 101, pp.148-166.
  3. Amiri, A., & Fattahi, A. (2015). Provide a fuzzy multi-objective logistics model for distributing relief items and evacuating the injured in times of crisis, 62(2), 63-76 (In Persian).
  4. Alizadeh, M., Amiri, M., Mustafee, N., & Sumohon, M. (2019). A robust stochastic Casualty Collection Points location problem. European Journal of Operational Research, 279(3), 965-983.
  5. Balcik, B., & Beamon, B.M. (2008). Facility location in humanitarian relief. International Journal of logistics, 11(2), 101-121.
  6. Balcik, B., B.M. Beamon, & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems, 12(2), 51-63.
  7. Behnamian, J., & Pourmoradkhani, M. (2015). Inventory modeling to deal with natural disasters. Two Quarterly Journal of Crisis Management, 7, 63-77. (In Persian)
  8. Brotcorne, L., Laporte, G., & Semet, F. (2003). Ambulance location and relocation models. European journal of operational research, 147(3), 451-463.
  9. Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498.
  10. Caunhye, A.M., Li, M., & Nie, X. (2015). A location-allocation model for casualty response planning during catastrophic radiological incidents. Socio-Economic Planning Sciences, 50, 32-44.
  11. Caunhye, A.M., X. Nie, & Pokharel S., (2012). Optimization models in emergency logistics: A literature review. Socio-economic planning sciences,. 46(1), 4-13.
  12. Craggs, R. (2012). Towards a political geography of hotels: Southern Rhodesia, 1958–1962. Political Geography, 31(4), 215-224.
  13. Foukardi, R., & Talavari, (2021).Optimizing Cash Flow in the Drug Supply Chain: A Risk-Approach Approach. The Journal of Industrial Management Perspective, 11(1), 117-145. (In Persian).
  14. Hale, T. & Moberg, C.R. (2005). Improving supply chain disaster preparedness. International Journal of Physical Distribution & Logistics Management, 35(3), 195-207.
  15. Liu, Y., N. Cui, & Zhang J., (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation research part E: logistics and transportation review, 128, 1-16.
  16. Liu, J.,Jiang,D.,Guo,L.,Nan,J.,Cao,W.,Wang,P., (2020). Emergency material location-allocation planning using a risk-based integration methodology for river chemical spills. Environmental Science and Pollution Research,27(15), 1-14.
  17. Loree, N. & Aros-Vera, F. (2018). Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics. Transportation Research Part E: Logistics and Transportation Review, 116, 1-24.
  18. Mohri, S.S. & Haghshenas, H. (020). An Ambulance Location Problem for Covering Inherently Rare and Random Road Crashes. Computers & Industrial Engineering, 151(1), 3-10.
  19. Mohagheghi, V., Mousavi, S.M. and Vahdani, B., (2015). A new optimization model for project portfolio selection under interval-valued fuzzy environment. Arabian Journal for Science and Engineering, 40(11), pp.3351-3361.
  20. Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2017). Analyzing project cash flow by a new interval type-2 fuzzy model with an application to construction industry. Neural Computing and Applications, 28(11), 3393-3411.
  21. Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Computers & Industrial Engineering, 148, 106734.
  22. Mousavi, S.M., Antuchevičienė, J., Zavadskas, E.K., Vahdani, B. & Hashemi, H., (2019). A new decision model for cross-docking center location in logistics networks under interval-valued intuitionistic fuzzy uncertainty. Transport, 34(1), 30-40.

23.Nabavi, S.M., Vahdani, B., Nadjafi, B.A. & Adibi, M.A. (2022). Synchronizing victim evacuation and debris removal: A data-driven robust prediction approach. European Journal of Operational Research, 300(2), 689-712.

  1. Nel, E.-M., du Preez, J.A., & Herbst, B.M. (2009). A pseudo-skeletonization algorithm for static handwritten scripts. International Journal of Document Analysis and Recognition (IJDAR), 12(1), 47-62.
  2. Niakan, F., Vahdani, B., & Mohammadi, M. (2015). A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach. Engineering Optimization, 47(12), 1670-1688.
  3. Rawls, C.G. & Turnquist, M.A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: Methodological, 44(4), 521-534.
  4. Saedinia, R., Vahdani, B., Etebari, F., & Nadjafi, B. A. (2019). Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer. Transportation Research Part E: Logistics and Transportation Review, 123, 121-141.
  5. Salimi, F. & Vahdani, B. (2018). Designing a bio-fuel network considering links reliability and risk-pooling effect in bio-refineries.Reliability Engineering & System Safety, 174, 96-107.
  6. Liu, Y., N. Cui, & Zhang J. (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation research part E: logistics and transportation review, 128, 1-16.
  7. Tajani, K., Mohtashami, A., Amiri, M., & Ehtesham, R. (2021). Presenting a robust optimization model to design a comprehensive blood supply chain under conditions of supply and demand uncertainty. The Journal of Industrial Management Perspective, 11(1), 81-116 (In Persian).
  8. Van Wassenhove, L.N. (2006). Humanitarian aid logistics: Supply chain management in high gear. Journal of the Operational research Society, 57(5), 475-489.
  9. Vahdani, B., Soltani, M., Yazdani, M., & Mousavi, S. M. (2017). A three level joint location-inventory problem with correlated demand, shortages and periodic review system: Robust meta-heuristics. Computers & Industrial Engineering, 109, 113-129.
  10. Vahdani, B., Mansour, F., Soltani, M. & Veysmoradi, D. (2019). Bi-objective optimization for integrating quay crane and internal truck assignment with challenges of trucks sharing. Knowledge-Based Systems, 163, 675-692.
  11. Vahdani, B. (2019). Assignment and scheduling trucks in cross-docking system with energy consumption consideration and trucks queuing. Journal of Cleaner Production, 213, 21-41.
  12. Vahdani, B., & Ahmadzadeh, E. (2019). Designing a realistic ICT closed loop supply chain network with integrated decisions under uncertain demand and lead time. Knowledge-Based Systems, 179, 34-54.
  13. Vahdani, B., & Naderi-Beni, M. (2014). A mathematical programming model for recycling network design under uncertainty: an interval-stochastic robust optimization model. The International Journal of Advanced Manufacturing Technology, 73(5), 1057-1071.
  14. Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M. & Razmi, J. (2012). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing, 23(3), 759-774.
  15. Vahdani, B. & Zandieh, M. (2010). Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method. International Journal of Production Research, 48(18), 5307-5326.
  16. Vahdani, B., Veysmoradi, D., Mousavi, S.M. & Amiri, M. (2022). Planning for relief distribution, victim evacuation, redistricting and service sharing under uncertainty. Socio-Economic Planning Sciences, 80, 101158.
  17. Vaziri, S., Etebari, F. and Vahdani, B., (2019). Development and optimization of a horizontal carrier collaboration vehicle routing model with multi-commodity request allocation. Journal of Cleaner Production, 224, 492-505.
  18. Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., & Mousavi, S. M. (2018). Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty.Scientia Iranica, 25(6), 3635-3653.
  19. Zahedi, A., M. Kargari, & Kashan, A.H. (2020). Multi-objective decision-macking model for distribution planning of goods and routing of vehicles in emergency, International Journal of Disaster Risk Reduction, 48(1), 2-10.