Optimization Of Multi Responses Using Data Envelopment Analysis: The Application in Food Industry

Year 2019, , 1083 - 1090, 01.09.2019

Duygu Kılıc , Meral Ebegıl , Hulya Bayrak , Berrin Ozkaya Başak Apaydın Avsar

https://doi.org/10.35378/gujs.394984

Cited By: 2

Abstract

Multiple response is a widely used method of increasing product quality, optimizing cost and time in industry. However, technological developments and processes are becoming more and more complex, which means that more than one response is effective rather than a single response, in product or process optimization. The Response Surface Methodology (RSM) can be used to optimize a single response or multiple responses. It is known that when there are numerous responses, it is difficult and complex to optimize responses simultaneously. Data Envelopment Analysis (DEA) is a statistical approach where multiple inputs and multiple outputs, regardless of how many they are can simultaneously be optimized. For this reason, in this study Data Envelopment Analysis (DEA) technique was applied in combination with The Response Surface Methodology (RSM) and this enabled us to optimize more than one response concurrently.

Keywords

Corn Bread, Data Envelopment Analysis, Multiple Response, Optimization, Response Surface Methodology

References

• [1] Tsai, C., W., Tong, L., Wang, C. (2010). Optimization of Multiple Responses Using Data Envelopment Analysis and Response Surface Methodology, Tamkang Journal of Science and Engineering, 13(2), 197–203.
• Shadkam, E., Bijari, M. (2015). The Optimization of Bank Branches Efficiency by Means of Response Surface Method and Data Envelopment Analysis: A Case of Iran. Journal of Asian finance, Economics and Business, 2(2), 13–18.
• Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., Kowalski, S. M. (2004). Response Surface Methodology: A retrospective and literature survey, J. Qual. Technol., 36, 53 –77.
• Derringerand G. C., Suich, R. (1980). Simultaneous Optimization of Several Response Variables, J. Qual. Technol., 12(1), 214–219.
• Khuri, A. I., Conlon, M. (1981). Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions, Techno metrics, 23, 363–375.
• Tong, L. I., Su, C. T. (1997). Optimization Multi-Response Problems in Taguchi Method by Fuzzy Multiple Attribute Decision Making, Quality and Reliability Engineering International, 13, 25–34.
• Koksoy, O. (2005). Dual Response Optimization: The Desirability Approach. Int. J. Ind. Eng.-Theory, 12(4), 335–342.
• Sahu, J., Mohanty, C. C., Mahapatra, S. S. (2013). A DEA Approach for Optimization of Multiple Responses in Electrical Discharge Machining of AISI D2 steel. Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering (NUiCONE 2012).
• Díaz-García, A. J., Bashiri, M., (2014). Multiple Response Optimization: An Approach From Multi Objective Stochastic Programming, Applied Mathematical Modelling, 38 (7-8), 2015–2027.
• Charnes, A., Cooper, W. W., Rhodes, E., (1978). Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2, 429–444.
• Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis, Management Science, 30, 1078–1092.
• Kao C., S. N. Hwang, (2008). Efficiency Decomposition in Two-stage Data Envelopment Analysis: An Application to Non-life Insurance Companies in Taiwan, European Journal of Operational Research, 185 (1), 418–429.
• Myers, R.H., Montgomery, D.C. (1995). Response Surface Methodology, Process and Product Optimization Using Designed Experiments. 2nd ed. John Wiley and Sons, New York, NY.
• Ryan, P (2007). Modern Experimental Design, John Wiley and Sons.
• Bayrak, H., Özkaya, B., M. A. Tekindal (2010). Productivity in the First Degree for the Optimum Point Determination of Factorial Trials: An Application. Turkey Clinics J Bio stat, 2(1), 18–27.