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Multi-Objective Stochastic Programming Approaches for Supply Chain Management

In: New Developments in Multiple Objective and Goal Programming

Author

Listed:
  • Amir Azaron

    (Universität Karlsruhe (TH)
    School of Science and Engineering, Reykjavik University)

  • Kai Furmans
  • Mohammad Modarres

Abstract

A multi-objective stochastic programming model is developed to design robust supply chain configuration networks. Demands, supplies, processing, and transportation costs are all considered as the uncertain parameters, which will be revealed after building the sites at the strategic level. The decisions about the optimal flows are made at the tactical level depending upon the actual values of uncertain parameters. It is also assumed that the suppliers are unreliable. To develop a robust model, two additional objective functions are added into the traditional supply chain design problem. So, the proposed model accounts for the minimization of the expected total cost and the risk, reflected by the variance of the total cost and the downside risk or the risk of loss. Finally, different simple and interactive multi-objective techniques such as goal attainment, surrogate worth trade-off (SWT), and STEM methods are used to solve the proposed multi-objective model.

Suggested Citation

  • Amir Azaron & Kai Furmans & Mohammad Modarres, 2010. "Multi-Objective Stochastic Programming Approaches for Supply Chain Management," Lecture Notes in Economics and Mathematical Systems, in: Dylan Jones & Mehrdad Tamiz & Jana Ries (ed.), New Developments in Multiple Objective and Goal Programming, pages 1-14, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-10354-4_1
    DOI: 10.1007/978-3-642-10354-4_1
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    Citations

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    Cited by:

    1. Huang, Edward & Goetschalckx, Marc, 2014. "Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 508-518.
    2. Alireza Azimian & Belaid Aouni, 2017. "Supply chain management through the stochastic goal programming model," Annals of Operations Research, Springer, vol. 251(1), pages 351-365, April.

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