Abstract
This chapter proposes the modification of a technique of simultaneous optimization of multiple response variables that works using a Bayesian predictive distribution to incorporate different weights to the response variables according to their importance in the cost or functionality of products. To achieve this, the desirability function has been incorporated into the original proposal. This research shows through the simulation of different scenarios in a case study taken from literature that the proposed optimum process operating conditions always moved towards regions where the response variables with the highest weights had the best results, at the expense of performance in variables with the lowest weights.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ames AE, Matucci N, Macdonald S, Szonyi G, Hawkins DM (1997) Quality loss function for optimization across multiple response surfaces. J Qual Technol 29(3):339–346
Bahloul B, Lassoued MA, Sfar S (2014) A novel approach for the development and optimization of self emulsifying drug delivery system using HLB and response surface methodology: application to fenofibrate encapsulation. Int J Pharm 466(1–2):341–348
Ch’ng CK, Quah SH, Low HC (2005) Index C*pm in multiple response optimization. Qual Eng 17(1):165–171
Chiao CH, Hamada M (2001) Analyzing experiments with correlated multiple responses. J Qual Technol 33(4):451–465
De Figueiredo AK, Rodríguez LM, Riccobene IC, Nolasco SM (2014) Analysis of the performance of a dehulling system for confectionary sunflower seeds. Food Nutr Sci 5:541–548
Del Castillo E, Montgomery DC, McCarville DR (1996) Of self emulsifying drug delivery system using HLB and response surface methodology: application to fenofibrate encapsulation. Int J Pharm 341–348
Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12(4):214–219
Gutierrez H, De la Vara R (2012) Análisis y Diseño de Experimentos. Mc Graw Hill, Mexico
Jhonson ME (1987) Multivariate statistical simulation: a guide to selecting and generating continuous multivariate distributions. Wiley, USA
Ko YH, Kim KJ, Jun CH (2005) A new loss function-based method for multiresponse optimization. J Qual Technol 37(1):50–59
Lee MS, Kim KJ (2007) Expected desirability function: consideration of both location and dispersion effects in desirability function approach. Qual Technol Quant Manage 4(3):365–377
Miró-Quesada G, Del Castillo E, Peterson JJ (2004) A bayesian approach for multiple response surface optimization in the presence of noise variables. J Appl Stat 31(3):251–270
Montgomery DC (2005) Diseño y Análisis de Experimentos. Limusa Wiley, México
Myers RH, Montgomery DC (1995) Response surface methodology process and product optimization using designed experiments. Wiley lnterscience, New York
Ortiz F, Simpson JR, Pignatiello JJ, Heredia-Lagner A (2004) A genetic algorithm approach to multiple-response optimization. J Qual Technol 36(4):432–450
Peterson JJ (2004) A posterior predictive approach to multiple response surface optimization. J Qual Technol 36(2):139–153
Plante RD (2001) Process capability: a criterion for optimizing multiple response product and process design. IIE Trans 33(6):497–509
Simsek B, Tansel Y, Simsek EH (2013) A fullfactorial design based desirability function approach for optimization of properties of C 40/50 concrete class. J Math Comput Appl 18(3):330–339
Vera CL, De Zan M, Cámara MS, Goicoechea HC (2014) Experimental design and multiple response optimization. Using the desirability function in analytical methods development. J Talanta 124:123–138
Zhang X, Lu X, Li S, Zhong M, Shi X, Luo G, Ding L (2004) Investigation of 2,4-dichlorophenoxyacetic acid adsorption onto MIEX resin: Optimization using response surface methodology. J Taiwan Inst Chem Eng 45(4):1835–1841
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Limon-Romero, J., Luz-Tortorella, G., Puente, C., Moreno-Jiménez, J.M., Maciel-Monteon, M. (2018). An Alternative to Multi-response Optimization Using a Bayesian Approach. In: García-Alcaraz, J., Alor-Hernández, G., Maldonado-Macías, A., Sánchez-Ramírez, C. (eds) New Perspectives on Applied Industrial Tools and Techniques. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-56871-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-56871-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56870-6
Online ISBN: 978-3-319-56871-3
eBook Packages: EngineeringEngineering (R0)