Linear programming (LP) is the operations research technique frequently used in the fields of science, economics, business, management science, and engineering. Although it is investigated and applied for more than six decades, and LP models with different level of generalization of information about parameters including models with interval, fuzzy, generalized fuzzy, and random numbers are considered, until now there is no approach to account for reliability of information within the framework of LP. Professor L. Zadeh introduced the concept of a Z-number to describe uncertain information, which is a more generalized notion closely related to reliability. The use of Z-information is more adequate and intuitively meaningful for formalizing information structure of a decision problem. In this paper, we suggest a study of fully Z-number based LP (Z-LP) model to better fit real-world problems within the framework of LP. We propose the method to solve Z-LP problems, which utilize differential evolution optimization and Z-number arithmetic developed by the authors. The suggested model and solution method for Z-LP are illustrated on the basis of a benchmark LP problem, where we conduct comparative analysis, which shows validity of the approach.

In decision analysis, uncertainty is usually described in the framework of probability. However, a large number of experimental and theoretical studies showed that a single nature of probability does not accurately capture human preferences. To avoid this drawback, they use imprecise probabilities. But, as decision maker is usually uncertain about first-order imprecise probabilities, imprecise hierarchical probability models are used. For most of such models, the second levels are precise. There also exist studies on two-level imprecise hierarchical models, which use imprecise probabilities or possibilities at the second level. Most of these works are based on lower prevision theory leading to a large number of optimization problems. In the present paper, we propose an imprecise hierarchical decision-making model where the first and the second level are described by interval probabilities. The method associates with the construction of a nonadditive measure as a lower prevision and uses this capacity in Choquet integral for constructing a utility function.

Generally decision making for solving ill-structured problems in DSS takes place in uncertain situations. The main drawbacks of existing traditional DSS are inefficiencies associated with dealing with complex models and large databases. Usually a fuzzy DSS has many input variables and, hence, its knowledge base, containing the totality of fuzzy rules, is very large. Large rule base leads to disadvantages in speed, reliability, and complexity of DSS. This paper introduces an alternative concept for designing fuzzy DSS based on multi-agent distributed artificial intelligent technology and fuzzy decision making. The main idea of the proposed DSS is based on granulation of the overall system intelligence between cooperative autonomous intelligent agents capable of competing and cooperating with each other in order to propose a total solution to the problem and organization (combining individual solutions) of the proposed solution into the final solution. It is supposed that every agent in DSS is characterized by a set of fuzzy criteria of unequal importance and definition of a “winner” agent is based on multi-criteria fuzzy decision making involving unequal objectives. © 2000 John Wiley & Sons, Inc.