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A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection

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  • Osman, Hany
  • Demirli, Kudret

Abstract

The problem addressed in this paper is related to an aerospace company seeking to change its outsourcing strategies in order to meet the expected demand increase and customer satisfaction requirements regarding delivery dates and amounts. A bilinear goal programming model is developed to achieve the company's objectives. A modified Benders decomposition method is successfully applied to handle bilinear goal programming models in which the complicating binary variables affect the values of the deviational variables of goals attainment. This influence leads to formulate the master and the sub-problem as two goal programming models with different objective function decompositions as compared to the classical Benders one. Computational experiments show that the modified Benders algorithm outperforms a generic linearization method by reaching the optimal solution for larger problem with about 75% reduction in computation time.

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  • Osman, Hany & Demirli, Kudret, 2010. "A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection," International Journal of Production Economics, Elsevier, vol. 124(1), pages 97-105, March.
    • Handle: RePEc:eee:proeco:v:124:y:2010:i:1:p:97-105
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    Cited by:

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    2. Osman, Hany & Demirli, Kudret, 2012. "Economic lot and delivery scheduling problem for multi-stage supply chains," International Journal of Production Economics, Elsevier, vol. 136(2), pages 275-286.
    3. Linda Zhang & Carman Lee & Shuzhu Zhang, 2016. "An integrated model for strategic supply chain design: Formulation and ABC-based solution approach," Post-Print hal-01744868, HAL.
    4. 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.
    5. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    6. Zhang, Haifeng & Yang, Kai & Gao, Yuan & Yang, Lixing, 2022. "Accelerating Benders decomposition for stochastic incomplete multimodal hub location problem in many-to-many transportation and distribution systems," International Journal of Production Economics, Elsevier, vol. 248(C).
    7. Lin, Rong-Ho, 2012. "An integrated model for supplier selection under a fuzzy situation," International Journal of Production Economics, Elsevier, vol. 138(1), pages 55-61.
    8. Devesh Kumar & Gunjan Soni & Rohit Joshi & Vipul Jain & Amrik Sohal, 2022. "Modelling supply chain viability during COVID-19 disruption: A case of an Indian automobile manufacturing supply chain," Operations Management Research, Springer, vol. 15(3), pages 1224-1240, December.
    9. Mariel, Katharina & Minner, Stefan, 2017. "Benders decomposition for a strategic network design problem under NAFTA local content requirements," Omega, Elsevier, vol. 68(C), pages 62-75.
    10. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
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