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Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment

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  • Hêris Golpîra

Abstract

This paper introduces the problem of designing a single-product supply chain network in an agile manufacturing setting under a vendor managed inventory (VMI) strategy to seize a new market opportunity. The problem addresses the level of risk aversion of the retailer when dealing with the uncertainty of market related information through a conditional value at risk (CVaR) approach. This approach leads to a bilevel programming problem. The Karush–Kuhn–Tucker (KKT) conditions are employed to transform the model into a single-level, mixed-integer linear programming problem by considering some relaxations. Since realizations of imprecisely known parameters are the only information available, a data-driven approach is employed as a suitable, more practical, methodology of avoiding distributional assumptions. Finally, the effectiveness of the proposed model is demonstrated through a numerical example.

Suggested Citation

  • Hêris Golpîra, 2017. "Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(1), pages 21-41.
    • Handle: RePEc:wut:journl:v:1:y:2017:p:21-41:id:1267
    DOI: 10.5277/ord170102
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    References listed on IDEAS

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    1. Liu, Songsong & Papageorgiou, Lazaros G., 2013. "Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry," Omega, Elsevier, vol. 41(2), pages 369-382.
    2. Bilge Bilgen & Yelda Çelebi, 2013. "Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling," Annals of Operations Research, Springer, vol. 211(1), pages 55-82, December.
    3. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    4. Kopanos, Georgios M. & Puigjaner, Luis & Georgiadis, Michael C., 2012. "Simultaneous production and logistics operations planning in semicontinuous food industries," Omega, Elsevier, vol. 40(5), pages 634-650.
    5. Chahar, Kiran & Taaffe, Kevin, 2009. "Risk averse demand selection with all-or-nothing orders," Omega, Elsevier, vol. 37(5), pages 996-1006, October.
    6. Kang, Jae-Hun & Kim, Yeong-Dae, 2012. "Inventory control in a two-level supply chain with risk pooling effect," International Journal of Production Economics, Elsevier, vol. 135(1), pages 116-124.
    7. Gotoh, Jun-ya & Takano, Yuichi, 2007. "Newsvendor solutions via conditional value-at-risk minimization," European Journal of Operational Research, Elsevier, vol. 179(1), pages 80-96, May.
    8. Kostis Taxakis & Chrissoleon Papadopoulos, 2016. "A design model and a production–distribution and inventory planning model in multi-product supply chain networks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6436-6457, November.
    9. Qin, Yan & Wang, Ruoxuan & Vakharia, Asoo J. & Chen, Yuwen & Seref, Michelle M.H., 2011. "The newsvendor problem: Review and directions for future research," European Journal of Operational Research, Elsevier, vol. 213(2), pages 361-374, September.
    10. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    11. Pan, Feng & Nagi, Rakesh, 2013. "Multi-echelon supply chain network design in agile manufacturing," Omega, Elsevier, vol. 41(6), pages 969-983.
    12. Dimitris Bertsimas & David B. Brown, 2009. "Constructing Uncertainty Sets for Robust Linear Optimization," Operations Research, INFORMS, vol. 57(6), pages 1483-1495, December.
    13. Karthik Natarajan & Dessislava Pachamanova & Melvyn Sim, 2009. "Constructing Risk Measures from Uncertainty Sets," Operations Research, INFORMS, vol. 57(5), pages 1129-1141, October.
    14. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    15. Wu, Meng & Zhu, Stuart X. & Teunter, Ruud H., 2013. "The risk-averse newsvendor problem with random capacity," European Journal of Operational Research, Elsevier, vol. 231(2), pages 328-336.
    16. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    17. Yu, Min & Nagurney, Anna, 2013. "Competitive food supply chain networks with application to fresh produce," European Journal of Operational Research, Elsevier, vol. 224(2), pages 273-282.
    18. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    19. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    20. S S Chauhan & J-M Proth & A M Sarmiento & R Nagi, 2006. "Opportunistic supply chain formation from qualified partners for a new market demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1089-1099, September.
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