Stochastic optimization for the carrier selection problem in humanitarian relief

Document Type : Article

Authors

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

Abstract

In this paper, the carrier selection problem is addressed with the purpose of avoiding shortage regarding vehicle necessity and creating a vehicle rental framework agreement. Achieving in-time delivery of relief supplies to the disaster inflicted areas is of utmost significance in terms of humanitarian relief. We propose a new two-stage stochastic model for determining the pre-disaster and post-disaster decisions in order to delivering relief items to the injured survivors. The pre-disaster phase focuses on determining amount of vehicle in the framework of contracts with suppliers, and deciding an appropriate coverage distance with regards to time and cost. The post-disaster phase aims to respond to the requests made by disaster inflicted areas swiftly and cost-effectively. The proposed MILP model considers a scenario-based approach to handle the uncertainty of demand. The L-shaped algorithm is used to solve this model. A real case study is presented with the aim of demonstrating the efficiency of the model. Moreover, numerical analyses are practiced to illustrate the importance and impact of the cost and the number of vehicle rental contracts in the studied problem. Finally, managerial insights have been presented to assist the relief organization management in making appropriate and efficient decisions.

Keywords

References

References:
1. Athukorala, P.C. and Resosudarmo, B.P. "The Indian Ocean tsunami: Economic impact, disaster management, and lessons", Asian Economic Papers, 4(1), pp. 1-39 (2005).
2. Altay, N. and Green, W.G.I. "OR/MS research in disaster operations management", European Journal of Operational Research, 175, p. 475-493 (2006).
3. Habib, M.S., Lee, Y.H., and Memon, M.S. "Mathematical models in humanitarian supply chain management: A systematic literature review", Mathematical Problems in Engineering, 20(1), pp. 1-20 (2016).
4. Zheng, Y.J., Chen, S.Y., and Ling, H.F. "Evolutionary optimization for disaster relief operations: A survey", Applied Soft Computing, 27, pp. 553-566 (2015).
5. Barbarosoglu, G. and Arda, Y. "A two-stage stochastic programming framework for transportation planning in disaster response", Journal of the Operational Research Society, 55(1), pp. 43-53 (2004).
6. Das, R. and Hanaoka, S. "Relief inventory modelling with stochastic lead-time and demand", European Journal of Operational Research, 235(3), pp. 616-623 (2014).
7. Van Wassenhove, L.N. "Humanitarian aid logistics: supply chain management in high gear", Journal of the Operational Research Society, 57(5), pp. 475-489 (2006).
8. Alem, D., Clark, A., and Moreno, A. "Stochastic network models for logistics planning in disaster relief", European Journal of Operational Research, 255(1), pp. 187-206 (2016).
9. Perez-Rodriguez, N., and Holguin-Veras, J. "Inventory -allocation distri-bution models for post-disaster humanitarian logistics with explicit consideration of deprivation costs", Transportation Science, 50(4), pp. 1261-1285 (2015).
10. Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., et al. "An interactive approach for designing a robust disaster relief logistics network with perishable commodities", Computer and Industrial Engineering, 94, pp. 201-215 (2016).
11. Ruan, J.H., Wang, X.P., Chan, F.T.S., et al. "Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering", International Journal of Production Research, 54(14), pp. 4368-4386 (2016).
12. Moreno, A., Alem, D., Ferreira, D., et al. "An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains", European Journal of Operational Research, 269(3), pp. 1050-1071 (2018).
13. Rodriguez-Espindola, O., Albores, P., and Brewster, C. "Disaster preparedness in humanitarian logistics: a collaborative approach for resource management in floods", European Journal of Operational Research, 264, pp. 978-993 (2018).
14. Tikani, H. and Setak, M. "Ecient solution algorithms for a time-critical reliable transportation problem in multigraph networks with FIFO property", Applied Soft Computing, 74, pp. 504-528 (2019).
15. Aissaoui, N., Haouari, M., and Hassini. E. "Supplier selection and order lot sizing modeling: A review", Computer and Industrial Engineering, 34(12), pp. 3516-3540 (2007).
16. Alfredsson, P. and Verrijdt, J. "Modeling emergency supply flexibility in a two-echelon inventory system", Management Science, 45(10), pp. 1416-1431 (1999).
17. Tagaras, G. and Vlachos, D. "Effectiveness of stock transshipment under various demand distributions and non-negligible transshipment times", Production and Operations Management, 2, pp. 183-198 (2002).
18. Lee, Y., Jung, J., and Jeon, Y. "An effective lateral transshipment policy to improve service level in the supply chain", International Journal of Production Economics, 106(1), pp. 115-126 (2007).
19. Hu, S.L., Han, C.F., and Meng, L.P. "Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief", Computer and Industrial Engineering, 111, pp. 39-49 (2017).
20. Hazra, J. and Mahadevan, B. "A procurement model using capacity reservation", European Journal of Operational Research, 193, pp. 303-316 (2009).
21. Li, L. and Zabinsky, Z. "Incorporating uncertainty into a supplier selection problem", International Journal of Production Economics, 134, pp. 344-356 (2011).
22. Liao, Z. and Rittsche, J. "A multi-objective supplier selection model under stochastic demand conditions", International Journal of Production Economies, 105, pp. 150-159 (2007).
23. Choi, T.M. "Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme", Computer and Operations Research, 40(11), pp. 2646-2655 (2013).
24. Hammami, R., Temponi, C., and Fein, Y. "A scenariobased stochastic model for supplier global context with multiple buyers, currency  fluctuation uncertainties, and price discounts", European Journal of Operational Research, 233(1), pp. 159-170 (2014).
25. Park, S.C. and Lee, J.H. "Supplier selection and stepwise benchmarking: a new hybrid model using DEA and AHP based on cluster analysis", Journal of the Operational Research Society, 69(3), pp. 1-20 (2018).
26. Lin, Y-K. and Yeh, C-T. "Optimal carrier selection based on network reliability criterion for stochastic logistics networks", International Journal of Production Economics, 128, pp. 510-517 (2010).
27. Lin, Y-K. and Yeh, Ch-T. "Determine the optimal carrier selection for a logistics network based on multicommodity reliability criterion", International Journal of Systems Science, 44, pp. 949-964 (2013).
28. Meixell, M.J. and Norbis, M. "Integrating carrier selection with supplier selection decisions to improve supply chain security", International Transaction in Operational Research, 19, pp. 711-732 (2012).
29. Kolaee, M.H. and Torabi, S.A. "Supplier and carrier selection and order allocation by considering disruption with AHP and multi-objective mathematical programming", International Journal of Supply and Operations Management, 4, pp. 359-369 (2017).
30. Davis-Sramek, B., Thomas, R.W., and Fugate, B.S. "Integrating behavioral decision theory and sustainable supply chain management: Prioritizing economic, environmental, and social dimensions in carrier selection", Journal of Business Logistics, 39, pp. 87-100 (2018).
31. Pazirandeh, A. "Sourcing in global health supply chains for developing countries", International Journal of Physical Distribution Logistics Management, 41(4), pp. 364-384 (2011).
32. Zhang, G. and Ma, L. "Optimal acquisition policy with quantity discounts and uncertain demands", International Journal of Production Research, 47(9), pp. 2409-2425 (2009).
33. Balcik, B. and Ak, D. "Supplier selection for framework agreement in humanitarian relief", Production and Operation Management, 23(6), pp. 1028-1041 (2014).
34. Nikkho, F., Bozorg-Amiri, A., and Heydari, J. "Coordination of relief items procurement in humanitarian logostic based on quantity  flexibility contract", International Journal of Disaster Risk Reduction, 31, pp. 331-340 (2018).
35. Kuo, C.W., Huang, K.L., and Yang, C.L. "Optimal contract design for cloud computing service with resource service  guarantee", Journal of the Operational Research Society, 68(9), pp. 1030-1044 (2017).
36. Zhang, J-H., Sun, X-Q., Zhu, R., et al. "Solving an emergency rescue materials problem under the joint reserves mode of government and framework agreement suppliers", PLoS ONE, 12(10), p. e0186747 (2017).
37. Shamsi, G.N., Torabi, S.A., and Shakouri, G.H. "An option contract for vaccine procurement using the SIR epidemic model", European Journal of Operational Research, 267(3), pp. 1122-1140 (2018).
38. Birge, J.R. and Louveaux, F., Introduction to Stochastic Programing, New York: Springer (1997).
39. Chaharsooghi, S.K., Honarvar, M., Modarres, M., et al. "Developing a two stage-stochastic programming model of the price and lead-time decision problem in the multi-class make-to-order firm", Computers and Industrial Engineering, 61(4), pp. 1086-1097 (2011).
40. Noyan, N. "Risk-averse two-stage stochastic programming with an application to disaster management", Computers and Operations Research, 39(3), pp. 541-559 (2012).
41. Tofighi, S., Torabi, S.A., and Mansouri, S.A. "Humanitarian logistics network design under mixed uncertainty", European Journal of Operational Research, 250(1), pp. 239-250 (2016).
42. Alumur, S.A., Nickel, S., Saldanha-da-Gama, F., et al. "Multi-period reverse logistics network design", European Journal of Operational Research, 220(1), pp. 67-78 (2012).
43. Xu, N. and Nozick, L. "Modeling supplier selection and the use of option contracts for global supply chain design", Computers and Operations Research, 36(10) pp. 2786-2800 (2009).
Scientia Iranica
Volume 29, Issue 5
Transactions on Industrial Engineering (E)
September and October 2022
Pages 2710-2727

Files

Share

How to cite

Statistics