Chance constraint problem having parameters as pareto random variables

Abstract

Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance constrained programming (CCP) has been used for modeling and analysis of risks in a number of application domains. This paper presents a deterministic reduction of linear and nonlinear chance constraint programming problem using geometric inequality, assuming the coefficients of the decision variables in the chance constraints as Pareto random variables. After implicative reduction of the proposed chance constraint programming problem into a deterministic problem, which is usually nonlinear, standard generic package is used to find the compromise solution. Then MATLAB programming code is used to verify the validity of solution as well as that of the reduced model. This method leads to an efficient reduced model as well as an optimal compromise solution.