On the Distribution of Solutions in Linear Programming Problems

Abstract

Objection is taken to Babbar's [1] treatment of the problem of the distribution of solutions in a linear programming model. It is argued that Babbar's approximation neglects the mathematical constraint of non-negative solutions, and that his general approach to stochastic linear programming deals with a restricted (if not unrealistic) set of assumptions as to availability of data and the framework for decision making. An outline is given of other applications of linear programming models in which a policy decision must be made under conditions of uncertainty, and the difficulty of obtaining theoretical solutions to the associated problems is cited.