میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,239
تعداد مقالات77,695
تعداد مشاهده مقاله161,373,288
تعداد دریافت فایل اصل مقاله120,920,767
Shokr, Iman. (1405). A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty. , 60(1), 71-92. doi: 10.22059/aie.2026.406269.1961
Iman Shokr. "A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty". , 60, 1, 1405, 71-92. doi: 10.22059/aie.2026.406269.1961
Shokr, Iman. (1405). 'A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty', , 60(1), pp. 71-92. doi: 10.22059/aie.2026.406269.1961
Shokr, Iman. A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty. , 1405; 60(1): 71-92. doi: 10.22059/aie.2026.406269.1961

A Robust Contract-Based Hybrid Procurement Model with Supplier Incentives for Humanitarian Supply Chains under Uncertainty

Advances in Industrial Engineering
دوره 60، شماره 1، شهریور 2026، صفحه 71-92    اصل مقاله (885.28 K)
شناسه دیجیتال (DOI): 10.22059/aie.2026.406269.1961
نویسنده
Iman Shokr*
Assistant Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
چکیده
Typically, humanitarian agencies (HAs) tend to preposition relief supplies before potential disasters to increase resource availability in the post-disaster phase. However, prepositioning imposes high costs on relief chains. On the other hand, although postponing relief supply procurement to the post-disaster phase may avoid prepositioning costs, it comes with significant supply risks. This study utilizes buyback, option, and quantity discount contracts which enables HAs to adopt a hybrid strategy for providing relief supplies, ensuring maximum post-disaster supply availability. In this paper, a robust bi-objective two-stage stochastic programming model is proposed for supplier selection and the procurement of critical and non-critical relief supplies. The model also emphasizes suppliers' profits to incentivize their participation in the relief chain. In this study, a combination of robust stochastic programming and robust convex programming is employed to manage uncertainty. The model is applied to a case study, and multiple sensitivity analyses are performed. The results demonstrate that, compared with the situation in which hybrid contracts are not utilized, the proposed model reduces the HA’s cost by 38.4% and expected shortages by 22.6%, while increasing suppliers’ profits by 41.8%.
کلیدواژه‌ها
Humanitarian Supply Chain؛ Procurement Strategies؛ Buyback؛ Option Contract؛ Robust Stochastic Optimization
مراجع
  1. Wang, X., F. Li, L. Liang, Z. Huang, and A. Ashley, Pre-purchasing with option contract and coordination in a relief supply chain. International Journal of Production Economics, 2015. 167: p. 170–176.
  2. John, L., A. Gurumurthy, A. Mateen, and G. Narayanamurthy, Improving the coordination in the humanitarian supply chain: exploring the role of options contract. Annals of Operations Research, 2022. 319(1): p. 15–40.
  3. Aghajani, M., S.A. Torabi, and J. Heydari, A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains. Socio-Economic Planning Sciences, 2020. 71: p. 100780.
  4. Falasca, M. and C.W. Zobel, A two‐stage procurement model for humanitarian relief supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 2011. 1(2): p. 151–169.
  5. Bozorgi-Amiri, A., M.S. Jabalameli, and S. Mirzapour Al-e-Hashem, A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum, 2013. 35: p. 905–933.
  6. Galindo, G. and R. Batta, Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sciences, 2013. 47(1): p. 20–37.
  7. Balcik, B. and D. Ak, Supplier selection for framework agreements in humanitarian relief. Production and Operations Management, 2014. 23(6): p. 1028–1041.
  8. Pradhananga, R., F. Mutlu, S. Pokharel, J. Holguín-Veras, and D. Seth, An integrated resource allocation and distribution model for pre-disaster planning. Computers & Industrial Engineering, 2016. 91: p. 229–238.
  9. Tofighi, S., S.A. Torabi, and S.A. Mansouri, Humanitarian logistics network design under mixed uncertainty. European journal of operational research, 2016. 250(1): p. 239–250.
  10. Safaei, A.S., S. Farsad, and M.M. Paydar, Robust bi-level optimization of relief logistics operations. Applied Mathematical Modelling, 2018. 56: p. 359–380.
  11. Hu, S. and Z.S. Dong, Supplier selection and pre-positioning strategy in humanitarian relief. Omega, 2019. 83: p. 287–298.
  12. Shokr, I., F. Jolai, and A. Bozorgi-Amiri, A novel humanitarian and private sector relief chain network design model for disaster response. International Journal of Disaster Risk Reduction, 2021. 65: p. 102522.
  13. Shokr, I., F. Jolai, and A. Bozorgi-Amiri, A collaborative humanitarian relief chain design for disaster response. COMPUTERS & INDUSTRIAL ENGINEERING, 2022. 172.
  14. Aghajani, M., S.A. Torabi, and N. Altay, Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption. Omega, 2023. 118: p. 102871.
  15. Torabi, S.A., I. Shokr, S. Tofighi, and J. Heydari, Integrated relief pre-positioning and procurement planning in humanitarian supply chains. Transportation Research Part E: Logistics and Transportation Review, 2018. 113: p. 123–146.
  16. Hu, Z., J. Tian, and G. Feng, A relief supplies purchasing model based on a put option contract. Computers & Industrial Engineering, 2019. 127: p. 253–262.
  17. Kord, H. and P. Samouei, Coordination of humanitarian logistic based on the quantity flexibility contract and buying in the spot market under demand uncertainty using NSGA-II and NRGA algorithms. Expert Systems with Applications, 2023. 214: p. 119187.
  18. Wang, D., K. Yang, L. Yang, and J. Dong, Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity. Transportation research part E: logistics and transportation review, 2023. 170: p. 103025.
  19. Hu, S., Z.S. Dong, and R. Dai, A machine learning based sample average approximation for supplier selection with option contract in humanitarian relief. Transportation research part E: logistics and transportation review, 2024. 186: p. 103531.
  20. Shokr, I., F. Jolai, and A. Bozorgi-Amiri, A new model for enhancing collaboration between humanitarian organizations and private sector in humanitarian relief chain. Computers & Industrial Engineering, 2025: p. 111556.
  21. Mulvey, J.M., R.J. Vanderbei, and S.A. Zenios, Robust optimization of large-scale systems. Operations research, 1995. 43(2): p. 264–281.
  22. Leung, S.C., S.O. Tsang, W.-L. Ng, and Y. Wu, A robust optimization model for multi-site production planning problem in an uncertain environment. European journal of operational research, 2007. 181(1): p. 224–238.
  23. Bertsimas, D. and M. Sim, The price of robustness. Operations research, 2004. 52(1): p. 35–53.
آمار
تعداد مشاهده مقاله: 162
تعداد دریافت فایل اصل مقاله: 21





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب