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An enhanced procedure for managing blood supply chain under disruptions and uncertainties

  • S.I.: Applications of OR in Disaster Relief Operations, Part II
  • Published:
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Abstract

As a service-oriented supply chain, blood supply chain management deals with a human-to-human network involved with a number of direct challenges such as irregularity in both blood supply and demand as well as multiplicity of blood products, their respective lifetimes and perishability. Besides, facing risks originating from natural disasters or man-made incidents such as labor strikes, economic problems, and so on, and from uncertainty embedded in the input data is also a real highlight in designing and managing a blood supply chain network. This paper addresses an enhanced perspective incorporating a two-phase preemptive policy by which the disruption risk is diminished through a hybrid technique using the fuzzy analytic hierarchy process and grey rational analysis for determining supplementary blood facilities, to cooperate in production process and decrease interruptions. Furthermore, a p-robust formulation is presented to control the network reliability under disruption scenarios at minimum cost. To protect the network against the uncertainty, a consolidated approach based on a recently developed fuzzy measure is extended. We also examine the validity and practicality of the proposed model and its solution perspective along with the reliability of the network by a real case of Iran. It is worth nothing that the problems in the case of disasters in which the impact of the two risk streams will be felt most keenly, could benefit from the proposed procedure advantages since it develops a proactive and controlling approach which can be extensively applicable in disastrous situation.

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References

  • Abdulwahab, U., & Wahab, M. I. M. (2014). Approximate dynamic programming modeling for a typical blood platelet bank. Computers & Industrial Engineering,78, 259–270.

    Google Scholar 

  • Alfonso, E., Augusto, V., & Xie, X. (2015). Mathematical programming models for annual and weekly bloodmobile collection planning. IEEE Transactions on Automation Science and Engineering,12(1), 96–105.

    Google Scholar 

  • Arvan, M., Tavakkoli-Moghaddam, R., & Abdollahi, M. (2015). Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management,3(1), 57–68.

    Google Scholar 

  • Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research,227(1), 199–215.

    Google Scholar 

  • Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research,217(1), 1–16.

    Google Scholar 

  • Ben-Tal, A., Golany, B., Nemirovski, A., & Vial, J. P. (2005). Retailer-supplier flexible commitments contracts: A robust optimization approach. Manufacturing and Service Operations Management,7(3), 248–271.

    Google Scholar 

  • Blake, J., Heddle, N., Hardy, M., & Barty, R. (2009). Simplified platelet ordering using shortage and outdate targets. International Journal of Health Management and Information,1(2), 145–166.

    Google Scholar 

  • Cetin, E., & Sarul, L. S. (2009). A blood bank location model: A multiobjective approach. European Journal of Pure and Applied Mathematics,2(1), 112–124.

    Google Scholar 

  • Cheraghi, S., Hosseini-Motlagh, S. M., & Ghatreh Samani, M. (2017). Integrated planning for blood platelet production: A robust optimization approach. Journal of Industrial and Systems Engineering,10, 55–80.

    Google Scholar 

  • Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics,187, 27–41.

    Google Scholar 

  • Drackley, A., Newbold, K. B., Paez, A., & Heddle, N. (2012). Forecasting Ontario’s blood supply and demand. Transfusion,52(2), 366–374.

    Google Scholar 

  • Duan, Q., & Liao, T. W. (2014). Optimization of blood supply chain with shortened shelf lives and ABO compatibility. International Journal of Production Economics,153, 113–129.

    Google Scholar 

  • Elalouf, A., Hovav, S., Tsadikovich, D., & Yedidsion, L. (2015). Minimizing operational costs by restructuring the blood sample collection chain. Operations Research for Health Care,7, 81–93.

    Google Scholar 

  • Ensafian, H., & Yaghoubi, S. (2017). Robust optimization model for integrated procurement, production and distribution in platelet supply chain. Transportation Research Part E: Logistics and Transportation Review,103, 32–55.

    Google Scholar 

  • Fabozzi, F. J., Huang, D., & Zhou, G. (2010). Robust portfolios: Contributions from operations research and finance. Annals of Operations Research,176(1), 191–220.

    Google Scholar 

  • 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.

    Google Scholar 

  • Ghandforoush, P., & Sen, T. K. (2010). A DSS to manage platelet production supply chain for regional blood centers. Decision Support Systems,50(1), 32–42.

    Google Scholar 

  • Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers and Operations Research,54, 129–141.

    Google Scholar 

  • Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. (2010). Vendor managed inventory for environments with stochastic product usage. European Journal of Operational Research,202(3), 686–695.

    Google Scholar 

  • Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers and Operations Research,89, 206–212.

    Google Scholar 

  • Inuiguchi, M., & Ramık, 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.

    Google Scholar 

  • Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review,70, 225–244.

    Google Scholar 

  • Jokar, A., & 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,4(3), 101–105.

    Google Scholar 

  • Kamp, C., Heiden, M., Henseler, O., & Seitz, R. (2010). Management of blood supplies during an influenza pandemic. Transfusion,50(1), 231–239.

    Google Scholar 

  • Kamyabniya, A., Lotfi, M. M., Naderpour, M., & Yih, Y. (2017). Robust platelet logistics planning in disaster relief operations under uncertainty: A coordinated approach. Information Systems Frontiers, 1–24. https://doi.org/10.1007/s10796-017-9788-5.

    Google Scholar 

  • Khalilpourazari, S., & Khamseh, A. A. (2017). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: A comprehensive study with real world application. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2588.

    Article  Google Scholar 

  • Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and operations management,14(1), 53–68.

    Google Scholar 

  • Kohneh, J. N., Teymoury, E., & Pishvaee, M. S. (2016). Blood products supply chain design considering disaster circumstances (Case study: Earthquake disaster in Tehran). Journal of Industrial and Systems Engineering,9, 51–72.

    Google Scholar 

  • Lee, D. H., & Dong, M. (2008). A heuristic approach to logistics network design for end-of-lease computer products recovery. Transportation Research Part E: Logistics and Transportation Review,44(3), 455–474.

    Google Scholar 

  • Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy Sets and Systems,94(2), 227–237.

    Google Scholar 

  • Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems,10(4), 445–450.

    Google Scholar 

  • Lowalekar, H., & Ravichandran, N. (2010). Model for blood collections management. Transfusion,50(12), 2778–2784.

    Google Scholar 

  • Mousazadeh, M., Torabi, S. A., Pishvaee, M. S., & Abolhassani, F. (2018). Health service network design: A robust possibilistic approach. International Transactions in Operational Research,25(1), 337–373.

    Google Scholar 

  • Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science,9(2), 205–231.

    Google Scholar 

  • Osorio, A. F., Brailsford, S. C., & 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,53(24), 7191–7212.

    Google Scholar 

  • Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological,45(8), 1190–1211.

    Google Scholar 

  • Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling,35(2), 637–649.

    Google Scholar 

  • Puranam, K., Novak, D. C., Lucas, M. T., & Fung, M. (2017). Managing blood inventory with multiple independent sources of supply. European Journal of Operational Research,259(2), 500–511.

    Google Scholar 

  • Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation Research Part E: Logistics and Transportation Review,104, 69–82.

    Google Scholar 

  • Şahinyazan, F. G., Kara, B. Y., & Taner, M. R. (2015). Selective vehicle routing for a mobile blood donation system. European Journal of Operational Research,245(1), 22–34.

    Google Scholar 

  • Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2017). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 1–25. https://doi.org/10.1007/s10479-017-2533-0.

  • Samani, M. R. G., 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.

    Google Scholar 

  • Samvedi, A., Jain, V., & Chan, F. T. (2012). An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. International Journal of Production Research,50(12), 3211–3221.

    Google Scholar 

  • Seifried, E., Klueter, H., Weidmann, C., Staudenmaier, T., Schrezenmeier, H., Henschler, R., et al. (2011). How much blood is needed? Vox Sanguinis,100(1), 10–21.

    Google Scholar 

  • Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia,5, 21–28.

    Google Scholar 

  • Shishebori, D., & Babadi, A. Y. (2015). Robust and reliable medical services network design under uncertain environment and system disruptions. Transportation Research Part E: Logistics and Transportation Review,77, 268–288.

    Google Scholar 

  • Shishebori, D., Snyder, L. V., & Jabalameli, M. S. (2014). A reliable budget-constrained FL/ND problem with unreliable facilities. Networks and Spatial Economics,14(3–4), 549–580.

    Google Scholar 

  • Snyder, L. V., & Daskin, M. S. (2006). Stochastic p-robust location problems. IIE Transactions,38(11), 971–985.

    Google Scholar 

  • Snyder, L. V., Scaparra, M. P., Daskin, M. S., & Church, R. L. (2006). Planning for disruptions in supply chain networks. Tutorials in operations research,2, 234–257.

    Google Scholar 

  • Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications,9(1), 33–45.

    Google Scholar 

  • Xu, J., & Zhou, X. (2013). Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation. Information Sciences,238, 75–95.

    Google Scholar 

  • Yahyaei, M., & Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: an integration of shelter and supply facility location. Annals of Operations Research, 1–20. https://doi.org/10.1007/s10479-018-2758-6.

  • Zahiri, B., & Pishvaee, M. S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research,55(7), 2013–2033.

    Google Scholar 

  • Zahiri, B., Torabi, S. A., Mousazadeh, M., & Mansouri, S. A. (2015). Blood collection management: Methodology and application. Applied Mathematical Modelling,39(23–24), 7680–7696.

    Google Scholar 

  • Zokaee, S., Jabbarzadeh, A., Fahimnia, B., & Sadjadi, S. J. (2017). Robust supply chain network design: An optimization model with real world application. Annals of Operations Research,257(1–2), 15–44.

    Google Scholar 

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Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, University Ave, Narmak, Tehran, 16846, Iran

    Mohammad Reza Ghatreh Samani & Seyyed-Mahdi Hosseini-Motlagh

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  1. Mohammad Reza Ghatreh Samani
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  2. Seyyed-Mahdi Hosseini-Motlagh
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Correspondence to Seyyed-Mahdi Hosseini-Motlagh.

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Samani, M.R.G., Hosseini-Motlagh, SM. An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Ann Oper Res 283, 1413–1462 (2019). https://doi.org/10.1007/s10479-018-2873-4

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  • DOI: https://doi.org/10.1007/s10479-018-2873-4

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