Operational Research Approach to Decision Making

The decision making (DM) problem is of great practical value in many areas of human activities. Most widely used DM methods are based on probabilistic approaches. The well-known Bayesian theorem for a conditional probability density function (PDF) is a background for such techniques. It is needed due to some uncertainty in many parameters entered in any model which describes the functioning of many real systems or objects. Uncertainty in our knowledge might be expressed in an alternative form. We offer to employ appropriate confidence intervals for model parameters instead of a relevant PDF. Thus one can formulate a prior uncertainty in model parameters by means of a set of linear constraints. The related cost or goal function should be defined at a corresponding set of parameters. That leads us to stating the problem in terms of operational research or mathematical linear programming. It is more convenient to formulate such optimization problems for discreet or Boolean variables. A review of relevant problem statements and numerical techniques are presented as well as many examples.