DECISION MAKING PRINCIPLE FROM THE PERSPECTIVE OF POSSIBILISTIC THEORY

Mathematical programming plays a pivotal role in finding the solution for optimization problems in various practical, real-life applications. Conventionally, the modeling used in mathematical programming is based on numerical values. It is however complicated to accurately provide such rigid numerical values because uncertain elements do exist in the decision-making process. Furthermore, building a mathematical programming model with crisp and precise values can result in the production of an infeasible or improper solution. Hence, uncertain based decision making is exemplify in this paper by using possibilistic theory to capture human uncertain judgment to develop mathematical programming model which sufficiently able to find an acceptable solution. The implementation of the proposed method shows the significant capabilities to solve real application problem which retain the uncertainties in its problem model.