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.
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This research is supported by Alexander von Humboldt-Stiftung and Iran National Science Foundation (INSF).
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