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

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

Multiple response is a widely used method of increasing product quality, optimizing cost andtime in industry. However, technological developments and processes are becoming more andmore complex, which means that more than one response is effective rather than a singleresponse, in product or process optimization. The Response Surface Methodology (RSM) canbe used to optimize a single response or multiple responses. It is known that when there arenumerous responses, it is difficult and complex to optimize responses simultaneously. DataEnvelopment Analysis (DEA) is a statistical approach where multiple inputs and multipleoutputs, 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 withThe Response Surface Methodology (RSM) and this enabled us to optimize more than oneresponse concurrently.

___

  • Tsai, C.W., Tong, L. and Wang, C., “Optimization of Multiple Responses Using Data Envelopment Analysis and Response Surface Methodology”, Tamkang Journal of Science and Engineering, 13(2): 197–203, (2010).
  • Shadkam, E. and Bijari, M., “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, (2015).
  • Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M. and Kowalski, S.M., “Response Surface Methodology: A Retrospective and Literature Survey”, Journal of Quality Technology, 36: 53 –77, (2004).
  • Derringerand G.C. and Suich, R., “Simultaneous Optimization of Several Response Variables”, Journal of Quality Technology, 12(1): 214–219, (1980).
  • Khuri, A.I. and Conlon, M., “Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions”, Techno Metrics, 23: 363–375, (1981).
  • Tong, L.I. and Su, C. T., “Optimization Multi-Response Problems in Taguchi Method by Fuzzy Multiple Attribute Decision Making”, Quality and Reliability Engineering International, 13: 25–34, (1997).
  • Köksoy, O., “Dual Response Optimization: The Desirability Approach”, International Journal of Industrial Engineering: Theory, Applications and Practice, 12(4): 335–342, (2005).
  • Sahu, J., Mohanty, C.C. and Mahapatra, S.S., “A DEA Approach for Optimization of Multiple Responses in Electrical Discharge Machining of AISI D2 steel”, Procedia Engineering, 51: 585-591, (2013).
  • Díaz-García, A.J. and Bashiri, M., “Multiple Response Optimization: An Approach from Multi Objective Stochastic Programming”, Applied Mathematical Modelling, 38(7-8): 2015–2027, (2014).
  • Charnes, A., Cooper, W.W. and Rhodes, E., “Measuring the Efficiency of Decision Making Units”, European Journal of Operational Research, 2: 429–444, (1978).
  • Banker, R.D., Charnes, A. and Cooper, W.W., “Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis”, Management Science, 30: 1078–1092, (1984).
  • Kao, C. and Hwang, S.N., “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, (2008).
  • Myers, R.H. and Montgomery, D.C., Response Surface Methodology, Process and Product Optimization Using Designed Experiments, USA: Second edition Wiley&Sons Publications, New York, (1995).
  • Ryan, P., Modern Experimental Design. Canada: Wiley&Sons Publications, New Jersey, (2007).
  • Bayrak, H., Özkaya, B. ve Tekindal, M.A., “Productivity in the First Degree for the Optimum Point Determination of Factorial Trials: An Application”, Turkey Clinics Journal of Biostatistics, 2(1): 18– 27, (2010).