Simultaneous optimization of mean and variance of objective functions in a supply chain problem under uncertainty

To attain a setting of decision variables that minimize the total cost of supply chain is the one of important problems in the literature review of the supply chain. Recently, several methods have suggested approaches addressing these problem but most of them assume that the parameters such as demands, supply and deliveries to be deterministic and ignore potential correlation among the supply chain objectives. In this study, a desirability function-based optimization approach is proposed that not only considers uncertainty in parameters and sets simultaneously all objectives in a minimum desirability level but also takes into account potential correlation between objectives and minimizes the effect of uncontrollable variables or noise factors.