Abstract
This paper presents a new method for finding the optimum solution for a linear programming problem with uncertainty in the coefficients.
Total cost is defined by adding penalty cost to activity cost when the constraints are violated, and a stochastic programming problem is set up to minimize the expected total cost. It is shown that the problem is reduced to a convex programming. The algorithm for finding the optimum solution is also presented, using the gradient method.
