Fuzzy Methodologies (Logic)
for Business Applications

Dr. Chao-Hsien Chu
School of Information Sciences and Technology
The Pennsylvania State University, USA
E-Mail: chu@ist.psu.edu

Introduction:

Emerging technologies in decision/information sciences.
Expert systems, fuzzy methodologies, or neural networks?
Crisp vs. fuzzy logic (sets).
Overview of fuzzy information processing.

Fundamental of Fuzzy Set Theory:

Overview of crisp and fuzzy set operators.
Fuzzy relations.
Measures of Fuzziness.
Fuzzification and defuzzification methods.

Fuzzy Methodologies:

Taxonomy of fuzzy methodologies.
Fuzzy analysis.
Fuzzy evaluation.
Fuzzy clustering/recognition.
Fuzzy modeling/programming.
Fuzzy expert systems (inferencing, logic).

System Development Issues:

Process of fuzzy information processing.
Selection of fuzzy methodologies.
Determination/selection of membership function.
Selection of fuzzy measures and operators.
Selection of fuzzy clustering algorithm.
How to acquire fuzzy rules?
System integration and maintenance issues.

Illustration/Demonstration of Applications:

Fuzzy analysis-lot sizing; flow shop scheduling.
Fuzzy evaluation-facility location selection; corporate credit assessment.
Fuzzy clustering-manufacturing cell formation; personnel selection.
Fuzzy modeling-manufacturing cell formation.
Fuzzy expert system-aggregate production planning.

Implementation and Future Directions:

Strengths and weaknesses of fuzzy methodologies.
Summary of potential applications.
Strategies for successful implementation.
Integration with expert systems/neural networks/genetic algorithm.

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