Using bounds on the data in linear programming: The tolerance approach to sensitivity analysis

Abstract

In contrast to traditional sensitivity analysis in linear programming, the tolerance approach considers simultaneous and independent variations in a number of parameters. A primary focus of this approach is to determine a maximum tolerance percentage for selected right-hand-side terms in which the same basis is optimal as long as each term is accurate to within that percentage of its estimated value. Similarly, the approach yields a maximum tolerance percentage for selected objective function coefficients. This paper shows how the tolerance approach can exploit information on the range of possible values over which terms and coefficients can vary to yield larger maximum tolerance percentages.