Inequalities for Stochastic Linear Programming Problems

Consider a linear-programming problem in which the “right-hand side” is a random vector whose expected value is known and where the expected value of the objective function is to be minimized. An approximate solution is often found by replacing the “right-hand side” by its expected value and solving the resulting linear programming problem. In this paper conditions are given for the equality of the expected value of the objective function for the optimal solution and the value of the objective function for the approximate solution; bounds on these values are also given. In addition, the relation between this problem and a related problem, where one makes an observation on the “right-hand side” and solves the (nonstochastic) linear programming problem based on this observation, is discussed.

• 저널Management Science · 6(2) · 197–204
• 토픽Optimization and Mathematical Programming · Control and Systems Engineering
• DOI10.1287/mnsc.6.2.197
• 저자Albert Madansky