Emergency Management

Emergency Management

Development of an Innovative Algorithm Based on Complete Counting for Bilateral Optimization Problem Design of Hub Logistics Network in Relief and Disaster Supply Chain

Document Type : Original Article

Authors
1 industrial management, Yazd university
2 Industrial Management, Yazd University
Abstract
In this study, a new two-level mathematical model is presented in order to design a logistics hub network in the supply chain of disasters and disasters. The leader level model uses the location of hubs and the determination of vehicles used. The objective function of this model is to minimize the cost of building hubs, transportation costs, and infrastructure costs to develop a logistics hub network. In the follow-up model, it determines the optimal routes for sending products to applicants with the aim of minimizing service delivery time. Given that solving two-tier models in the case where the follow-up model has an MIP structure, it is known as a difficult problem in research in operations and there is not always a definite solution for it, in this research an innovative algorithm based on complete counting. It is provided with high quality answers. According to the computational results, it can be observed that the developed algorithm is able to produce justified answers and of course it has an acceptable level in terms of the quality of the answers. Using the results of this research can be used as a management tool by crisis management organizations.
Keywords

  1. Van Wassenhove, L.N. and A.J. Pedraza Martinez, Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in Operational Research, 2012. 19(1-2): p. 307-322.
  2. Tatham, P. and M. Christopher, Humanitarian logistics: Meeting the challenge of preparing for and responding to disasters. 2018: Kogan Page Publishers.
  3. Mejia‐Argueta, C., et al., Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods. International Transactions in Operational Research, 2018. 25(3): p. 1053-1079.
  4. Sarma, D., et al. A multi-objective post-disaster relief logistic model. in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). 2017. IEEE.
  5. Hugos, M.H., Essentials of supply chain management. 2018: John Wiley & Sons.
  6. Maharjan, R. and S. Hanaoka, A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 2018. 8(1): p. 2-21.
  7. Alumur, S. and B.Y. Kara, Network hub location problems: The state of the art. European journal of operational research, 2008. 190(1): p. 1-21.
  8. Tofighi, S., S. Torabi, and S. Mansouri, Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 2016. 250(1): p. 239-250.
  9. Maharjan, R. and S. Hanaoka, A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 2018.
  10. Fahimnia, B., et al., Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 2017. 183: p. 700-709.
  11. Cao, C., et al., A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of cleaner production, 2018. 174: p. 1422-1435.
  12. Zhang, J., Z. Wang, and F. Ren, Optimization of humanitarian relief supply chain reliability: a case study of the Ya’an earthquake. Annals of Operations Research, 2019: p. 1-22.
  13. Maharjan, R. and S. Hanaoka, A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty. Socio-Economic Planning Sciences, 2019: p. 100727.
  14. Zarei, M.H., R. Carrasco‐Gallego, and S. Ronchi, On the role of regional hubs in the environmental sustainability of humanitarian supply chains. Sustainable Development, 2019.
  15. Maharjan, R. and S. Hanaoka, Fuzzy multi-attribute group decision making to identify the order of establishing temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 2019. 9(1): p. 2-21.
  16. Gutjahr, W.J. and N. Dzubur, Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 2016. 85: p. 1-22.
  17. Aarts, E., E.H. Aarts, and J.K. Lenstra, Local search in combinatorial optimization. 2003: Princeton University Press.

  • Receive Date 08 April 2020
  • Revise Date 31 March 2021
  • Accept Date 17 July 2021