Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

Abstract

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.

Keywords:

• manufacturing technology selection
• decision-making
• fuzzy decision trees
• data mining
• aerospace manufacturing

Acknowledgements

We wish to thank Airbus Operations Limited for providing provision throughout this research project and the data to demonstrate the decision models applicability within the domain. The research has been conducted by the Nottingham Innovation Manufacturing Research Centre (NIMRC) at the University of Nottingham, England, and funded by the Engineering and Physical Sciences Research Council (EPSRC), grant EP/E001904/1.