Parametric optimization with uncertainty on the left hand side of linear programs

• Detailed review of the evolution of parametric linear programming (pLP).
• Inversion of parametric matrix is a crucial barrier fronting the development of pLP.
• A two-stage methodology to solve parametric LP problems with uncertainty on the LHS.

Although parametric optimization with uncertainties on the objective function (OF) or on the so-called “right-hand-side” (RHS) of the constraints has been addressed successfully in recent papers, very little work exists on the same with uncertainties on the left-hand-side (LHS) of the constraints or in the coefficients of the constraint matrix. The goal of this work has been to develop a systematic method to solve such parametric optimization problems. This is a very complex problem and we have begun with the simplest of optimization problems, namely the linear programming problem with a single parameter on the LHS. This study reviews the available work on parametric optimization, describes the challenges and issues specific to LHS parametric linear programming (LHS-pLP), and presents a solution algorithm using some classic results from matrix algebra.