Optimal Blood Transportation in Disaster Relief Considering Facility Disruption and Route Reliability under Uncertainty

Document Type : Research Paper


1 MSc. Student, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran


The blood supply chain as a part of healthcare systems play a substantial role in health improvement within societies, and blood supply for daily needs and especially in disasters is the challenges requiring more attention. This paper presents a fuzzy-stochastic mixed integer linear programming model to design blood supply chain network for disaster relief. To deal with the uncertainty in model parameters, a fuzzy programming approach is considered, and the combination of the expected value and the chance constrained programming is applied to solve the proposed model. Besides, a real case study in Iran is implemented to illustrate the applicability of the present model. The results implies that an appropriate adjustment in the capacity and coverage radius of blood facilities, the decrease in the disruption probability of facilities and transportation routes as well as referral rate can be applied as strategies to improve the supply chain costs. supply chain costs.


-Arvan, M., Tavakkoli-Moghaddam, R. and Abdollahi, M. (2015) “Designing a bi-objective, multi-product supply chain network for blood supply”, Uncertain Supply Chain Management, Vol. 3, No. 1, pp. 57–68.
-Beliën, J. and Forcé, H. (2012) “Supply chain management of blood products: A literature review”, European Journal of Operational Research, Vol. 217, No. 1, pp. 1–16.
-Birge, J.R. and Louveaux, F. (2011) “Introduction to stochastic programming”, Germany: Springer Science & Business Media.
-Chakravarty, A. K. (2014) “Humanitarian relief chain: Rapid response under uncertainty”, International Journal of Production Economics, Vol. 151, pp. 146–157.
-Cheraghi, S., Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., (2017) “Integrated planning for blood platelet production: a robust optimization approach”, Journal of Industrial and Systems Engineering, 10 (SI: Healthcare).
-Chester, D. K. (1995) “World disasters report 1994”, International Federation of Red Cross and Red Crescent Societies: Third World Planning Review, Vol.17, No. 3, pp. 357.
-Fahimnia, B, Jabbarzadeh, A., Ghavamifar, A. and Bell, M. (2015) “Supply chain design for efficient and effective blood supply in disasters”, International Journal of Production Economics.
-Huang, M., Smilowitz, K. and Balcik, B. (2012) “Models for relief routing: equity, efficiency and efficacy”, Transportation Research Part E: Logistics and Transportation Review, Vol. 48, No. 1, pp. 2-18.
-Ignatius, J., Hosseini-Motlagh, S. M., Sepehri, M. M., Behzadian, M. and Mustafa, A. (2010) “Hybrid models in decision making under uncertainty: the case of training provider evaluation”, Journal of Intelligent & Fuzzy Systems, Vol. 21, pp. 147-162.
-Jokar, A. and Hosseini-Motlagh, S. M. (2015) “Impact of capacity of mobile units on blood supply chain performance: Results from a robust analysis”, International Journal of Hospital Research, Vol. 4, No. 3, pp. 109–114.
-Kabak, Ö. and Ülengin, F. (2011) “Possibilistic linear-programming approach for supply chain networking decisions”, European Journal of Operational Research, Vol. 209, No. 3, pp. 253–264.
-Liu, B. and Liu, Y. K. (2002) “Expected value of fuzzy variable and fuzzy expected value models”, IEEE Transactions on fuzzy systems, Vol. 10, No. 4, pp. 445-450.
-Majidi, S., Hosseini-Motlagh, S. M., Yaghoubi, S., and Jokar, A. (2017) “Fuzzy green vehicle routing with simultaneous pickup- delivery and time window”,RAIRO,-Operations research. DOI:https://doi.org/10.1051/ro/2017007.
-Nadizadeh, A. and Hosseini Nasab, H. (2014) “Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm”, European Journal of Operational Research, Vol. 238, pp. 458–470.
-Nagurney, A. and Masoumi, A. H. (2012) “Supply chain network operations management of a blood banking system with cost and risk minimization”, Computational Management Science, Vol. 9, No. 2,
pp. 205-231.
-Najafi, M., Eshghi, K. and Dullaert, W. (2013) “A multi-objective robust optimization model for logistics planning in the earthquake response phase”, Transportation Research Part E: Logistics an Transportation Review, Vol. 49, No. 1, pp. 217–249.
-Noyan, N. (2012) “Risk-averse two-stage stochastic programming with an application to disaster management”, Computers & Operations Research, Vol. 39, No. 3, pp. 541–559.
-Osorio, A. F., Brailsford, S. C. and Smith, H. K. (2015) “A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making”, International Journal of Production Research, Vol.53, No. 24, pp. 1-22.
-Pierskalla, W. P. and Roach, C. D. (1972) “Optimal issuing policies for perishable inventory”, Management Science, Vol. 18, No. 11, pp. 603–614.
-Riahi, N., Hosseini-Motlagh, S. M. and Teimourpour, B. A. (2013) “Three-phase hybrid times series modelling framework for improved hospital inventory demand forecast”, International Journal of Hospital Research, Vol. 2, No. 3, pp. 133–142.
-Rytila, J. S. and Spens, K. M. (2006) “Using simulation to increase efficiency in blood supply chains”, Management Research News, Vol. 29, No. 12, pp. 801–819.
-┼×ahin, G., Sural, H. and Meral, S. (2007) “Locational analysis for regionalization of Turkish Red Crescent blood services”, Computers & Operations Research, Vol. 34, No. 3, pp. 692–704.
-Sha,Y. and Huang, J. (2012) “The Multi-period Location-allocation Problem of Engineering Emergency Blood Supply Systems”, Systems Engineering Procedia, Vol. 5, pp. 21 – 28.
-Sheu, J. B. (2007) “Challenges of emergency logistics management”, Transportation Research Part E: Logistics and Transportation Review, Vol. 43, pp. 655–659.
-Tofighi, S., Torabi, S. A. and Mansouri, S. A. (2016) “Humanitarian logistics network design under mixed uncertainty”, European Journal of Operational Research, Vol. 250, No. 1, pp. 239–250.
-Torabi, S. A. and Hassini, E. (2008) “An interactive possibilistic programming approach for multiple objective supply chain master planning”, Fuzzy Sets and Systems, Vol. 159, No. 2, pp. 193–214.
-Zadeh, L. (1978) “Fuzzy sets as a basis for a theory of possibility”, Fuzzy Sets and Systems, Vol.1, pp. 3- 28.
-Zahiri, B., Torabi, S. A., Mousazadeh, M. and Mansouri, S. A. (2015) “Blood collection management: Methodology and application”, Applied Mathematical Modelling, Vol. 39, No.s 23-24, pp. 7680–7696.
-Zhu, H. and Zhang, J. (2009) “A credibility-based fuzzy mathematical programming model for APP problem” In: Artificial Intelligence and Computational Intelligence, AICI'09. International Conference on. IEEE, pp. 455-459