Supply Chain Network Design Optimization with Risk-Averse Retailer

Abstract

This paper proposes a model to formulate a supply chain network design (SCND) problem against uncertainty. The objective of the model is to minimize total cost of the network. The model employs risk averseness of retailers to obtain more realistic model regarding uncertain demand. Using Conditional Value at Risk (CVaR) to deal with this uncertainty makes the model to be robust. In this way, data-driven approach is used to avoid any distributional assumptions because realizations of uncertain parameters are the only information obtainable. This approach reformulates the initial uncertain model as a mixed integer linear programming problem. Numerical results show that the proposed model is efficient for robust SCND with respect to retailers risk averseness.

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