Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study
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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.
KeywordsBlood supply chain Disaster management Humanitarian relief Lexicographic weighted Tchebycheff method Neural learning Operational research Multi-objective programming
- Ahmadi, A., & Bazargan-Hejazi, S. (2018). 2017 Kermanshah earthquake; lessons learned. Journal of Injury and Violence Research, 10(1), 1.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.Google Scholar
- Geospatial Information Authority of Japan. (2017). https://www.gsi.go.jp/ENGLISH/.
- Iranian seismological center. (2017). http://irsc.ut.ac.ir/.
- 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
- 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.CrossRefGoogle 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
- 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.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.
- Pierskalla, W. P. (2005). Supply chain management of blood banks. In Operations research and health care, Springer, Boston, MA, pp. 103–145.Google Scholar
- Steuer, R. E. (1986). Multiple criteria optimization. In Theory, computation and applications, Willey, New York.Google Scholar