A Data Collection Strategy for Estimation of Cost Coefficients of a Linear Programming Model

The problem considered is one of selecting from a number of alternative sources of information for the refinement of estimates of objective function coefficients of a linear programming model. The approach used is that of Bayesian decision theory. A sequential scheme for the computing of bounds on the value of information is devised. Using these bounds the information of greatest value may be identified.