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An intelligent decision support system for on-demand fixture retrieval, adaptation and manufacture

Fentahun Moges Kasie (Department of Mechanical Engineering, University of KwaZulu-Natal, Durban, South Africa) (Hawassa University, Hawassa, Ethiopia)
Glen Bright (Department of Mechanical Engineering, University of KwaZulu-Natal, Durban, South Africa)
Anthony Walker (Department of Mechanical Engineering, University of KwaZulu-Natal, Durban, South Africa)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 6 March 2017

Abstract

Purpose

The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed.

Design/methodology/approach

The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS.

Findings

The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP.

Research limitations/implications

Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems.

Practical implications

The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world.

Originality/value

Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.

Keywords

Citation

Kasie, F.M., Bright, G. and Walker, A. (2017), "An intelligent decision support system for on-demand fixture retrieval, adaptation and manufacture", Journal of Manufacturing Technology Management, Vol. 28 No. 2, pp. 189-211. https://doi.org/10.1108/JMTM-08-2016-0116

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited