In the previous chapters dealing with linear programming, models were formulated and solved in order to aid the manager in making a decision. The solutions to the models were represented by values for decision variables . However, these linear programming models were all formulated under the assumption that certainty existed. In other words, it was assumed that all the model coefficients, constraint values, and solution values were known with certainty and did not vary.
In actual practice, however, many decision-making situations occur under conditions of uncertainty . For example, the demand for a product may be not 100 units next week, but 50 or 200 units, depending on the state of the market (which is uncertain ). Several decision-making techniques are available to aid the decision maker in dealing with this type of decision situation in which there is uncertainty.
Decision situations can be categorized into two classes: situations in which probabilities cannot be assigned to future occurrences and situations in which probabilities can be assigned. In this chapter we will discuss each of these classes of decision situations separately and demonstrate the decision-making criterion most commonly associated with each. Decision situations in which there are two or more decision makers who are in competition with each other are the subject of game theory , a topic included on the CD that accompanies this text.
The two categories of decision situation are probabilities that can be assigned to future occurrences and probabilities that cannot be assigned .