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Optimization of humanitarian relief supply chain reliability: a case study of the Ya’an earthquake

  • Jihai Zhang
  • Zhile Wang
  • Fan Ren
S.I.: Applications of OR in Disaster Relief Operations, Part II
  • 56 Downloads

Abstract

This article seeks to propose a mathematical method to optimize the reliability of the humanitarian relief supply chain. Reliability and cost are both important in response to the disasters. To optimize the reliability of humanitarian relief supply chain and to find a trade-off between the reliability and cost, this article establishes a reliability integrated optimization model for the humanitarian relief supply chain and investigates the methods for optimizing the coordination between flow quantity and unit reliability, optimizes the allocation of reliability for each unit, to optimize the total reliability and cost of the humanitarian relief supply chain. To make the results of this article more applicable, this article applies a case study of the Ya’an earthquake to the built model and subsequently proves the related conclusions subsequently. These theoretical results can be used to improve the disaster operations efficiency of the humanitarian relief supply in the crisis state, achieve a win–win situation between the total reliability and cost.

Keywords

Supply chain Optimization model Humanitarian relief Reliability 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 71473015.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

References

  1. Abidi, H., de Leeuw, S., & Klumpp, M. (2014). Humanitarian supply chain performance management: A systematic literature review. Supply Chain Management: An International Journal, 19(5/6), 592–608.CrossRefGoogle Scholar
  2. Ahmadi-Javid, A., & Hoseinpour, P. (2015). Incorporating location, inventory and price decisions into a supply chain distribution network design problem. Computers & Operations Research, 56, 110–119.CrossRefGoogle Scholar
  3. An, S., Cui, N., Bai, Y., Xie, W., Chen, M., & Ouyang, Y. (2015). Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing. Transportation Research Part E: Logistics and Transportation Review, 82, 199–216.CrossRefGoogle Scholar
  4. Badri, H., Bashiri, M., & Hejazi, T. H. (2013). Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method. Computers & Operations Research, 40(4), 1143–1154.CrossRefGoogle Scholar
  5. Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems, 12(2), 51–63.CrossRefGoogle Scholar
  6. Beamon, B. M., & Balcik, B. (2008). Performance measurement in humanitarian relief chains. International Journal of Public Sector Management, 21(1), 4–25.CrossRefGoogle Scholar
  7. Bertok, B., Kalauz, K., Sule, Z., & Friedler, F. (2012). Combinatorial algorithm for synthesizing redundant structures to increase reliability of supply chains: Application to biodiesel supply. Industrial and Engineering Chemistry Research, 52(1), 181–186.Google Scholar
  8. Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, 60(1–4), 357–371.CrossRefGoogle Scholar
  9. Charles, A., & Lauras, M. (2011). An enterprise modelling approach for better optimization modelling: Application to the humanitarian relief chain coordination problem. OR Spectrum, 33(3), 815–841.CrossRefGoogle Scholar
  10. Chen, L. M., Liu, Y. E., & Yang, S. J. S. (2015). Robust supply chain strategies for recovering from unanticipated disasters. Transportation Research Part E: Logistics and Transportation Review, 77, 198–214.CrossRefGoogle Scholar
  11. Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2681-2.
  12. Ghezavati, V., Soltanzadeh, F., & Hafezalkotob, A. (2015). Optimization of reliability for a hierarchical facility location problem under disaster relief situations by a chance-constrained programming and robust optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229(6), 542–555.Google Scholar
  13. Gong, B., Chen, X., & Hu, C. (2012). Fuzzy entropy clustering approach to evaluate the reliability of emergency logistics system. Energy Procedia, 16, 278–283.CrossRefGoogle Scholar
  14. Hamedi, M., Haghani, A., & Yang, S. (2012). Reliable transportation of humanitarian supplies in disaster response: Model and heuristic. Procedia-Social and Behavioral Sciences, 54, 1205–1219.CrossRefGoogle Scholar
  15. Hsu, C. I., & Li, H. C. (2011). Reliability evaluation and adjustment of supply chain network design with demand fluctuations. International Journal of Production Economics, 132(1), 131–145.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. Kaur, H., & Singh, S. P. (2016). Sustainable procurement and logistics for disaster resilient supply chain. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2374-2.
  18. Kumar, S., & Havey, T. (2013). Before and after disaster strikes: A relief supply chain decision support framework. International Journal of Production Economics, 145(2), 613–629.CrossRefGoogle Scholar
  19. Kunz, N., & Gold, S. (2017). Sustainable humanitarian supply chain management—exploring new theory. International Journal of Logistics Research and Applications, 20(2), 85–104.CrossRefGoogle Scholar
  20. Lin, Y. K., Yeh, C. T., & Huang, C. F. (2016). A simple algorithm to evaluate supply-chain reliability for brittle commodity logistics under production and delivery constraints. Annals of Operations Research, 244(1), 67–83.CrossRefGoogle Scholar
  21. Miman, M., & Pohl, E. (2008). Modelling and analysis of risk and reliability for a contingency logistics supply chain. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 222(4), 477–494.Google Scholar
  22. Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108–1118.CrossRefGoogle Scholar
  23. Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42(5), 2615–2623.CrossRefGoogle Scholar
  24. Taki, P., Barzinpour, F., & Teimoury, E. (2016). Risk-pooling strategy, lead time, delivery reliability and inventory control decisions in a stochastic multi-objective supply chain network design. Annals of Operations Research, 244(2), 619–646.CrossRefGoogle Scholar
  25. Tayal, A., & Singh, S. P. (2018). Formulating multi-objective stochastic dynamic facility layout problem for disaster relief. Annals of Operations Research, 5, 1–27.Google Scholar
  26. Thomas, M. U. (2002). Supply chain reliability for contingency operations. Paper presented at the annual meeting for reliability and maintainability symposium, Seattle, August, pp. 61–67.Google Scholar
  27. Ukkusuri, S., & Yushimito, W. (2008). Location routing approach for the humanitarian prepositioning problem. Transportation Research Record: Journal of the Transportation Research Board, 2089, 18–25.CrossRefGoogle Scholar
  28. Vitoriano, B., Ortuño, M. T., Tirado, G., & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189–208.CrossRefGoogle Scholar
  29. Wang, X., Wu, Y., Liang, L., & Huang, Z. (2016). Service outsourcing and disaster response methods in a relief supply chain. Annals of Operations Research, 240(2), 471–487.CrossRefGoogle Scholar
  30. Xiao, T., & Qi, X. (2016). A two-stage supply chain with demand sensitive to price, delivery time, and reliability of delivery. Annals of Operations Research, 241(1–2), 475–496.CrossRefGoogle Scholar
  31. Xu, Z., Ren, S., Guo, X., & Yuan, Z. (2015). Evaluation of emergency supply chain reliability under uncertain information. Operations Research and Management Science, 24(3), 35–44.Google Scholar
  32. Yildiz, H., Yoon, J., Talluri, S., & Ho, W. (2016). Reliable supply chain network design. Decision Sciences, 47(4), 661–698.CrossRefGoogle Scholar
  33. Zhang, Y. M. (2012). Reliability analysis of the emergency logistics supply chain. Applied Mechanics and Supplies, 170–173, 101–105.Google Scholar
  34. Zhang, Y. L., & Chen, L. (2016). Emergency supplies reserve of government for natural disasters. Natural Hazards, 81(1), 41–54.CrossRefGoogle Scholar
  35. Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied Mathematical Modelling, 40(17), 7996–8016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management Science and Engineering, School of Management and EconomicsBeijing Institute of TechnologyBeijingChina

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