Assignment of injuries and medical supplies in urban crisis management

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

2 School of Industrial Engineering, Iran University of Science and Technology, Noor Branch, Iran.

10.22105/jarie.2019.187654.1092

Abstract

In this paper, we introduce a two stages model for allocation of injuries and medical supplies to medical centers. In the first stage a multi objective mathematical model allocates injured people from the affected neighborhood to medical centers. In the second stage a single objective linear model allocates medical supplies from the supply points to medical centers. The first stage’s objective is simultaneously minimizing the total relief time and costs and maximizing the level of matching the type of injury with the specialized field of the medical centers those injuries are sent. The second stage’s objective is to minimize the costs of allocating medical supplies to medical centers. An integrated model that combines the two previous models is presented and comparing the results with the two stages model. Proposed models are applied to one of the districts of Tehran to demonstrate their effectiveness. The case study includes two affected neighborhood and four medical centers and three supply points. ϵ-constraint method is used to produce the Pareto optimal solutions in a MOMP.

Keywords


Van Wassenhove, L. N. (2006). Humanitarian aid logistics: supply chain management in high gear. Journal of the operational research society57(5), 475-489.

[2] Altay, N., & Green III, W. G. (2006). OR/MS research in disaster operations management. European journal of operational research175(1), 475-493.

[3] Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European journal of operational research230(2), 201-211.

[4] Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: methodological44(4), 521-534.

[5] Görmez, N., Köksalan, M., & Salman, F. S. (2011). Locating disaster response facilities in Istanbul. Journal of the operational research society62(7), 1239-1252.

[6] Rawls, C. G., & Turnquist, M. A. (2011). Pre-positioning planning for emergency response with service quality constraints. OR spectrum33(3), 481-498.

[7] Murali, P., Ordóñez, F., & Dessouky, M. M. (2012). Facility location under demand uncertainty: Response to a large-scale bio-terror attack. Socio-economic planning sciences46(1), 78-87.

[8] Salman, F. S., & Gül, S. (2014). Deployment of field hospitals in mass casualty incidents. Computers & industrial engineering74, 37-51.

[9] Kılcı, F., Kara, B. Y., & Bozkaya, B. (2015). Locating temporary shelter areas after an earthquake: A case for Turkey. European journal of operational research243(1), 323-332.

[10] Rumbach, A., & Follingstad, G. (2019). Urban disasters beyond the city: Environmental risk in India’s fast-growing towns and villages. International journal of disaster risk reduction34, 94-107.

[11] Lindell, M. K. (2019). The routledge handbook of urban disaster resilience: integrating mitigation, preparedness, and recovery planning. Routledge.

[12] Tiernan, A., Drennan, L., Nalau, J., Onyango, E., Morrissey, L., & Mackey, B. (2019). A review of themes in disaster resilience literature and international practice since 2012. Policy design and practice2(1), 53-74.

[13] Chong, M., Lazo Lazo, J. G., Pereda, M. C., & Machuca De Pina, J. M. (2019). Goal programming optimization model under uncertainty and the critical areas characterization in humanitarian logistics management. Journal of humanitarian logistics and supply chain management9(1), 82-107.

[14] Kumar, V., Ramamritham, K., & Jana, A. (2019, January). 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 (p. 16). ACM.

[15] Liu, Y., Cui, N., & Zhang, J. (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation research part E: logistics and transportation review128, 1-16.

[16] Sheu, J. B. (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation research part E: logistics and transportation Review43(6), 687-709.

[17] Li, X., Ramshani, M., & Huang, Y. (2018). Cooperative maximal covering models for humanitarian relief chain management. Computers & industrial engineering119, 301-308.

[18] Zhang, S., Guo, H., Zhu, K., Yu, S., & Li, J. (2017). Multistage assignment optimization for emergency rescue teams in the disaster chain. Knowledge-based systems137, 123-137.

[19] Sebatli, A., Cavdur, F., & Kose-Kucuk, M. (2017). Determination of relief supplies demands and allocation of temporary disaster response facilities. Transportation research procedia22, 245-254.

[20] Celik, E., Aydin, N., & Gumus, A. T. (2016). A stochastic location and allocation model for critical items to response large-scale emergencies: A case of Turkey. An international journal of optimization and control: theories & applications (IJOCTA)7(1), 1-15.

[21] Lutter, P., Degel, D., Büsing, C., Koster, A. M., & Werners, B. (2017). Improved handling of uncertainty and robustness in set covering problems. European journal of operational research263(1), 35-49.

[22] Marín, A., Martínez-Merino, L. I., Rodríguez-Chía, A. M., & Saldanha-da-Gama, F. (2018). Multi-period stochastic covering location problems: Modeling framework and solution approach. European journal of operational research268(2), 432-449.

[23] Sheu, J. B., & Pan, C. (2015). Relief supply collaboration for emergency logistics responses to large-scale disasters. Transportmetrica A: transport science11(3), 210-242.

[24] Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review69, 160-179.

[25] Wen, M., Qin, Z., & Kang, R. (2014). The $$alpha $$-cost minimization model for capacitated facility location-allocation problem with uncertain demands. Fuzzy Optimization and Decision Making13(3), 345-356.

[26] Barzinpour, F., & Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. The International Journal of Advanced Manufacturing Technology70(5-8), 1291-1302.

[27] Safaei, A. S., Farsad, S., & Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research, 1-24.

[28] Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum35(4), 905-933.

[29] Vira, C., & Haimes, Y. Y. (1983). Multiobjective decision making: theory and methodology. In North Holland series in system science and engineering (No. 8). North-Holland.

[30] Cohon, J. L. (2004). Multiobjective programming and planning (Vol. 140). Courier Corporation.

[31] Ehrgott, M., & Ryan, D. M. (2002). Constructing robust crew schedules with bicriteria optimization. Journal of multi‐criteria decision analysis11(3), 139-150.

[32] Hamacher, H. W., Pedersen, C. R., & Ruzika, S. (2007). Finding representative systems for discrete bicriterion optimization problems. Operations research letters35(3), 336-344.

[33] Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation213(2), 455-465.

[34] Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization26(6), 369-395.

[35] Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation213(2), 455-465.