A Fuzzy Expert System for Industrial Location Factor Analysis

Abstract

The identification of a new industrial location requires consideration of a complex set of factors in the decision making process. These factors are generally described with a number of different indicators, expressed in quantitative and/or qualitative ways, thus resulting in a nonlinear optimization problem. Besides, some of the input data are imprecise, incomplete or not totally reliable. Therefore, it is necessary to interpret, standardize and fuse data in specific factors suitable for comparison. To take into account all of these aspects above and allow for identification of an optimal solution by reasoning on available information, this paper proposes the use of an expert system for industrial location factor analysis. Management of uncertainty is an important issue in the design of expert systems, since data maybe indefinite, inaccurate and ambiguous. Fuzzy logic provides an approach to data fusion and reasoning for uncertain data by using the human expert knowledge. The proposed expert system is based on Fuzzy Inference Systems (FIS), which solve the nonlinear optimization problem by using the available knowledge. Results show that the proposed approach obtains accurate results in industrial location factor analysis, similar to those devised by experts of the field.