Using stochastic goal programming: some applications to management and a case of industrial production

This paper deals with stochastic goal programming as a method capable of providing “satisficing” solutions in the uncertainty case from the standard expected utility perspective. By extending recent results on ARA-based weighted achievement function and goal structures leading to mean-variance optimization, the paper combined random with non-random goals and explains the role of ARA coefficients in the model. Eight applications to real world problems in managerial environments are described. A case studying the textile industry, the choice of fibers to make blends in yarn production, is developed from empirical information and numerically solved.