Linear programming with interval coefficients

In order to solve a linear program, the model coefficients must be fixed at specific values, which implies that the coefficients are perfectly accurate. In practice, however, the coefficients are generally estimates. The only way to deal with uncertain coefficients is to test the sensitivity of the model to changes in their values, either singly or in very small groups. We propose a new approach in which some or all of the coefficients of the LP are specified as intervals. We then find the best optimum and the worst optimum for the model, and the point settings of the interval coefficients that yield these two extremes. This provides the range of the optimised objective function, and the coefficient settings give some insight into the likelihood of these extremes.