A possibilistic decision support system for imprecise mathematical programming problems

An approach for decision support under uncertainty based on a nondeterministic possibilistic approach is considered. Problem uncertainties are defined by fuzzy numbers and characterized by membership functions. A method for the efficient numerical implementation of the proposed decision support system is presented. The method is basically an algebraic process which can be implemented for general decision making problems. A set of representative samples of the solution behavior can be obtained directly from the formulation. At the same time, information about the global effect of the problem uncertainties on the optimal solution can be evaluated immediately from the analysis. The effectiveness of the method is illustrated by the solution of a programming model that represents the logistics management of the sulfuric acid business in Chile. Numerical results show the usefulness and potential of the proposed possibilistic approach.