Solving linear programming problem with uncertainty : probability interval and random set parameters

Abstract

In this thesis, we concentrate on the relationship of probability intervals and random sets. Furthermore, we are interested in solving uncertain linear programming problems with probability interval and random set parameters. We discover the conditions to verify when a given probability interval obtains the same information as a random set information. If these conditions are satisfied, we can transform a problem that contains both types of uncertainty into a problem which has only the random set information. In addition, we use an idea from decision making theory with random sets for solving this problem. If a probability interval does not satisfy these conditions, we can solve the problem for finding the optimistic and pessimistic expected recourse values. Finally, we present an algorithm for checking these conditions and constructing appropriate distributions for each of the optimistic and pessimistic approaches.