Abstract
In (post-optimal) sensitivity analysis, one usually draws information from both the objective function and the feasible set. In this chapter, we propose an approach based mainly on the latter, and whose predictions are valid as long as the objective function satisfies some regularity conditions such as additivity, convexity and submodularity. The resulting “pre-optimal” analysis is qualitative and provides sensitivity analysis results prior to solving the problem, which is an attractive feature for large-scale problems where the use of conventional parametric programming methods has a very high computational cost. Among the results discussed, the Ripple Theorem provides an upper bound on the variation in the optimal value of a variable resulting from changes in problem parameters. The theory of substitutes and complements leads to necessary and sufficient conditions for changes in optimal values of two arbitrary decision variables to consistently be of the same (or opposite) sign. The Monotonicity and Smoothing Theorems link changes in problem parameters to changes in optimal variable values, and provide bounds on the absolute change in an optimal solution. Computational issues are discussed, and a qualitative analysis of a production/inventory problem and an asset allocation problem are presented as tutorial examples.
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Gautier, A., Granot, D., Granot, F. (1997). Qualitative Sensitivity Analysis. In: Gal, T., Greenberg, H.J. (eds) Advances in Sensitivity Analysis and Parametic Programming. International Series in Operations Research & Management Science, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6103-3_8
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DOI: https://doi.org/10.1007/978-1-4615-6103-3_8
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