Advances in Production Engineering & Management
Volume 9 | Number 2 | June 2014 | pp 83–94
http://dx.doi.org/10.14743/apem2014.2.178
Imprecise data envelopment analysis model for robust design with multiple fuzzy quality responses
Al-Refaie, A.; Li, M.-H.; Jarbo, M.; Yeh, C.-H.B.; Nour, B.
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A B S T R A C T
In this research, Imprecise Data Envelopment Analysis (IDEA) model was utilized to improve fuzzy multiple responses in robust design. The combination of process factor levels at each experiment was considered as a Decision Making Unit (DMU) with responses treated as inputs and outputs for all DMUs. The Fuzzy C-Means Clustering (FCMC) technique is used to fit the response fuzziness by clustering the average values, relative to each response, into a suitable number of clusters with triangular/trapezoidal membership functions. IDEA models were used to estimate the fuzzy triangular/trapezoidal efficiency values for each DMU. Finally, the preference degree-based ranking approach was used to discriminate between the fuzzy efficiency values and identifying the best combination of factors levels that would improve fuzzy multiple responses. Two case studies are utilized to illustrate the proposed approach, including optimizing wire electrical discharge machining and sputtering process parameters. The results showed that the proposed approach provides better anticipated improvements than the fuzzy multiple regression based approach. This approach would provide great assistant to process engineers in improving process performance with fuzzy multiple responses over a wide range of business applications.
A R T I C L E I N F O
Keywords • Imprecise data envelopment analysis, Robust design, Multi fuzzy quality response
Corresponding author • Al-Refaie, A.
Article history • Received 30 September 2013, Revised 23 April 2014, Accepted 5 May 2014
Published on-line • 12 June 2014
E X P O R T C I T A T I O N
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