A Multi-Attribute Stochastic Programming Approach for Supply Chain Planning with VAR

This study deals with the problem of supply chain planning with risk management. A new multi-attribute stochastic programming model is proposed. Demand, supply, transportation, shortage, and capacity expansion costs are considered as uncertain parameters in the supply chain investment decision. To develop a robust model, an additional attribute function is added to the traditional supply chain design problem. This multi-attribute model includes: (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage, and capacity expansion costs, and (ii) the minimization of the VaR (Value-at-Risk) of the total future cost risk. Finally, a numerical example is given to illustrate the ability of the proposed model to supply chain planning.