Skip to main content
SpringerLink
Log in

Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study

  • S.I. : OR in Neuroscience II
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran–Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ahmadi, A., & Bazargan-Hejazi, S. (2018). 2017 Kermanshah earthquake; lessons learned. Journal of Injury and Violence Research,10(1), 1.

    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.

    Article  Google Scholar 

  • Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics,11(2), 101–121.

    Article  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.

    Article  Google Scholar 

  • Cheraghi, S., & Hosseini-Motlagh, S. M. (2017). Optimal blood transportation in disaster relief considering facility disruption and route reliability under uncertainty. International Journal of Transportation Engineering,4(3), 225–254.

    Google Scholar 

  • Christopher, M. (2016). Logistics and supply chain management. London: Pearson.

    Google Scholar 

  • Cozzolino, A. (2012). Humanitarian logistics and supply chain management. In Humanitarian logistics, Springer, Berlin, pp. 5–16.

    Chapter  Google Scholar 

  • Ebrahimi, M., & Rahmani, D. (2019). A five-dimensional approach to sustainability for prioritizing energy production systems using a revised GRA method: A case study. Renewable Energy,135, 345–354.

    Article  Google Scholar 

  • Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Amin, S. H. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence,71, 236–250.

    Article  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.

    Article  Google Scholar 

  • Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2729-3.

    Article  Google Scholar 

  • Geospatial Information Authority of Japan. (2017). https://www.gsi.go.jp/ENGLISH/.

  • Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling,55, 583–599.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2019.07.001.

    Article  Google Scholar 

  • Iranian seismological center. (2017). http://irsc.ut.ac.ir/.

  • 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.

    Article  Google Scholar 

  • Jacobs, F. R., Chase, R. B., & Lummus, R. R. (2014). Operations and supply chain management (pp. 533–535). New York, NY: McGraw-Hill.

    Google Scholar 

  • Karasözen, B., Küçükseyhan, T., & Uzunca, M. (2017). Structure preserving integration and model order reduction of skew-gradient reaction–diffusion systems. Annals of Operations Research,258(1), 79–106.

    Article  Google Scholar 

  • Khalilpourazari, S., & Khalilpourazary, S. (2017). A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process. Engineering Optimization,49(5), 878–895.

    Article  Google Scholar 

  • Khalilpourazari, S., & Khalilpourazary, S. (2018a). Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm. Neural Computing and Applications,29, 1321–1336.

    Article  Google Scholar 

  • Khalilpourazari, S., & Khalilpourazary, S. (2018b). SCWOA: An efficient hybrid algorithm for parameter optimization of multi-pass milling process. Journal of Industrial and Production Engineering,35(3), 135–147.

    Article  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-y.

    Article  Google Scholar 

  • Khalilpourazari, S., Mirzazadeh, A., Weber, G. W., & Pasandideh, S. H. R. (2019a). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization. https://doi.org/10.1080/02331934.2019.1630625.

    Article  Google Scholar 

  • Khalilpourazari, S., Naderi, B., & Khalilpourazary, S. (2019b). Multi-objective stochastic fractal search: A powerful algorithm for solving complex multi-objective optimization problems. Soft Computing. https://doi.org/10.1007/s00500-019-04080-6.

    Article  Google Scholar 

  • Khalilpourazari, S., & Pasandideh, S. H. R. (2018). Multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO. Operational Research. https://doi.org/10.1007/s12351-018-0397-y.

    Article  Google Scholar 

  • Khalilpourazari, S., & Pasandideh, S. H. R. (2019). Modeling and optimization of multi-item multi-constrained EOQ model for growing items. Knowledge-Based Systems,164, 150–162.

    Article  Google Scholar 

  • Khalilpourazari, S., Pasandideh, S. H. R., & Ghodratnama, A. (2018). Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale Optimization and Water Cycle Algorithms. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3492-3.

    Article  Google Scholar 

  • Khalilpourazari, S., Pasandideh, S. H. R., & Niaki, S. T. A. (2019c). Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders. Soft Computing. https://doi.org/10.1007/s00500-018-03718-1.

    Article  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 

  • Mizutani, E., & Dreyfus, S. (2017). Totally model-free actor-critic recurrent neural-network reinforcement learning in non-Markovian domains. Annals of Operations Research,258(1), 107–131.

    Article  Google Scholar 

  • Mohammadi, M., & Khalilpourazari, S. (2017). Minimizing makespan in a single machine scheduling problem with deteriorating jobs and learning effects. In Proceedings of the 6th international conference on software and computer applications, ACM, pp. 310–315.

  • Mohammadian-Behbahani, Z., Jabbarzadeh, A., & Pishvaee, M. S. (2019). A robust optimisation model for sustainable blood supply chain network design under uncertainty. International Journal of Industrial and Systems Engineering,31(4), 475–494.

    Article  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.

    Article  Google Scholar 

  • Or, I., & Pierskalla, W. P. (1979). A transportation location-allocation model for regional blood banking. AIIE Transactions,11(2), 86–95.

    Article  Google Scholar 

  • Pasandideh, S. H. R., & Khalilpourazari, S. (2018). Sine cosine crow search algorithm: a powerful hybrid meta heuristic for global optimization. arXiv preprint arXiv:1801.08485.

  • Pervin, M., Roy, S. K., & Weber, G. W. (2018). Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration. Annals of Operations Research,260(1–2), 437–460.

    Article  Google Scholar 

  • Pierskalla, W. P. (2005). Supply chain management of blood banks. In Operations research and health care, Springer, Boston, MA, pp. 103–145.

  • Rahmani, D. (2018). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2960-6.

    Article  Google Scholar 

  • Rahmani, D., Zandi, A., Behdad, S., & Entezaminia, A. (2019). A light robust model for aggregate production planning with consideration of environmental impacts of machines. Operational Research. https://doi.org/10.1007/s12351-019-00451-x.

    Article  Google Scholar 

  • Rahmani, D., Zandi, A., Peyghaleh, E., & Siamakmanesh, N. (2018). A robust model for a humanitarian relief network with backup covering under disruptions: A real world application. International Journal of Disaster Risk Reduction,28, 56–68.

    Article  Google Scholar 

  • Roy, S. K., Maity, G., Weber, G. W., & Alparslan Gök, S. Z. (2017). Conic scalarization approach to solve multi-choice multi-objective transportation problem with interval goal. Annals of Operations Research,253(1), 599–620.

    Article  Google Scholar 

  • Şahin, G., Süral, H., & Meral, S. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services. Computers & Operations Research,34(3), 692–704.

    Article  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.

    Article  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, 1. https://doi.org/10.1007/s10479-017-2533-0.

    Article  Google Scholar 

  • Samani, M. R. G., & Hosseini-Motlagh, S. M. (2018). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2873-4.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Şaylı, M., & Yılmaz, E. (2017). Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays. Annals of Operations Research,258(1), 159–185.

    Article  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.

    Article  Google Scholar 

  • Steuer, R. E. (1986). Multiple criteria optimization. In Theory, computation and applications, Willey, New York.

  • Tabatabaie, M., Ardalan, A., Abolghasemi, H., Naieni, K. H., Pourmalek, F., Ahmadi, B., et al. (2010). Estimating blood transfusion requirements in preparation for a major earthquake: The Tehran, Iran study. Prehospital and Disaster Medicine,25(3), 246–252.

    Article  Google Scholar 

  • Valinejad, F., & Rahmani, D. (2018). Sustainability risk management in the supply chain of telecommunication companies: A case study. Journal of Cleaner Production,203, 53–67.

    Article  Google Scholar 

  • Yerlikaya-Özkurt, F., Askan, A., & Weber, G. W. (2014). An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method. Engineering Optimization,46(12), 1651–1668.

    Article  Google Scholar 

  • Yerlikaya-Özkurt, F., Askan, A., & Weber, G. W. (2016). A hybrid computational method based on convex optimization for outlier problems: Application to earthquake ground motion prediction. Informatica,27(4), 893–910.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zahiri, B., Torabi, S. A., Mohammadi, M., & Aghabegloo, M. (2018). A multi-stage stochastic programming approach for blood supply chain planning. Computers & Industrial Engineering,122, 1–14.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical, Industrial and Aerospace Engineering (MIAE), Concordia University, Montreal, Canada

    Soheyl Khalilpourazari

  2. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

    Shima Soltanzadeh

  3. Faculty of Engineering Management, Poznan University of Technology, ul. Strzelecka, 11, 60-965, Poznan, Poland

    Gerhard-Wilhelm Weber

  4. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, West Bengal, 721102, India

    Sankar Kumar Roy

  5. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada

    Soheyl Khalilpourazari

  6. Institute of Applied Mathematics, Middle East Technical University, 06800, Ankara, Turkey

    Gerhard-Wilhelm Weber

Authors
  1. Soheyl Khalilpourazari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shima Soltanzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerhard-Wilhelm Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sankar Kumar Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soheyl Khalilpourazari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14.

Table 4 Geographical coordinates of donor groups (Khalilpourazari and Khamseh 2017)
Full size table
Table 5 Geographical coordinates of the facilities (IBTO and Google earth)
Full size table
Table 6 Distance between donor groups and blood collection centers
Full size table
Table 7 Distance between regional hospitals and the local hospitals
Full size table
Table 8 Number of available transportation mean type v at the local blood center g at period t (IBTO)
Full size table
Table 9 Capacity of local hospitals (IBTO)
Full size table
Table 10 Transportation cost of blood from blood collection facilities to regional blood centers (IBTO)
Full size table
Table 11 Transportation cost of blood from local blood centers to regional hospitals using different transportation modes (IBTO)
Full size table
Table 12 Number of injured people in local hospitals at each period (IBTO)
Full size table
Table 13 Blood demand data in regional hospitals
Full size table
Table 14 Values of main parameters (Khalilpourazari and Khamseh 2017)
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khalilpourazari, S., Soltanzadeh, S., Weber, GW. et al. Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Ann Oper Res 289, 123–152 (2020). https://doi.org/10.1007/s10479-019-03437-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-019-03437-2

Keywords

Search

Navigation