Modified chance constrained linear programming under uncertainty

摘要

This paper considers the sensitivity of chance constrained linear programming solutions where the coefficients of the left-hand side of a constraint function are estimated from a sample using multiple linear regression. The modified nonlinear constraint provides considerable assurance that the true, but unknown, stochastic linear constraint will be satisfied at a given level of probability for the conditions of the simulation herein. Ordinary least squares and least absolute value regression criteria are considered along with normal, uniform and double exponential distributions of error.