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A multiobjective chance constrained programming model for supplier selection under uncertainty

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  • Bilsel, R. Ufuk
  • Ravindran, A.
Abstract
Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model's solution is an optimal supplier order assignment, considering operational risks.

Suggested Citation

  • Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    • Handle: RePEc:eee:transb:v:45:y:2011:i:8:p:1284-1300
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    References listed on IDEAS

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    5. Lv, Y. & Yan, X.D. & Sun, W. & Gao, Z.Y., 2015. "A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 188-199.
    6. Faiza Hamdi & Ahmed Ghorbel & Faouzi Masmoudi & Lionel Dupont, 2018. "Optimization of a supply portfolio in the context of supply chain risk management: literature review," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 763-788, April.
    7. D. K. Mohanty & Avik Pradhan & M. P. Biswal, 2020. "Chance constrained programming with some non-normal continuous random variables," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1281-1298, December.
    8. Kanokporn Kungwalsong & Abraham Mendoza & Vasanth Kamath & Subramanian Pazhani & Jose Antonio Marmolejo-Saucedo, 2022. "An application of interactive fuzzy optimization model for redesigning supply chain for resilience," Annals of Operations Research, Springer, vol. 315(2), pages 1803-1839, August.
    9. Cardona-Valdés, Y. & Álvarez, A. & Pacheco, J., 2014. "Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 66-84.
    10. Sun, X.T. & Chung, S.H. & Chan, Felix T.S. & Wang, Zheng, 2018. "The impact of liner shipping unreliability on the production–distribution scheduling of a decentralized manufacturing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 242-269.
    11. Angus Jeang, 2019. "Robust DEA methodology via computer model for conceptual design under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1221-1245, March.
    12. Remica Aggarwal & S. P. Singh, 2019. "An integrated NPV-based supply chain configuration with third-party logistics services," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(5), pages 367-375, October.
    13. Fattahi, Mohammad, 2021. "Resilient procurement planning for supply chains: A case study for sourcing a critical mineral material," Resources Policy, Elsevier, vol. 74(C).
    14. Mukherjee, Krishnendu, 2014. "Supplier selection criteria and methods: past, present and future," MPRA Paper 60079, University Library of Munich, Germany.
    15. Klibi, Walid & Martel, Alain, 2012. "Scenario-based Supply Chain Network risk modeling," European Journal of Operational Research, Elsevier, vol. 223(3), pages 644-658.
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