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Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment

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  • Nha, Vo Thanh
  • Shin, Sangmun
  • Jeong, Seong Hoon

Abstract

The primary objective of this paper is to develop a new robust design (RD) optimization procedure based on a lexicographical dynamic goal programming (LDGP) approach for implementing time-series based multi-responses, while the conventional experimental design formats and frameworks may implement static responses. First, a parameter estimation method for time-dependent pharmaceutical responses (i.e., drug release and gelation kinetics) is proposed using the dual response estimation concept that separately estimates the response functions of the mean and variance, as a part of response surface method. Second, a multi-objective RD optimization model using the estimated response functions of both the process mean and variance is proposed by incorporating a time-series components within a dynamic modeling environment. Finally, a pharmaceutical case study associated with a generic drug development process is conducted for verification purposes. Based on the case study results, we conclude that the proposed LDGP approach effectively provides the optimal drug formulations with significantly small biases and MSE values, compared to other models.

Suggested Citation

  • Nha, Vo Thanh & Shin, Sangmun & Jeong, Seong Hoon, 2013. "Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment," European Journal of Operational Research, Elsevier, vol. 229(2), pages 505-517.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:2:p:505-517
    DOI: 10.1016/j.ejor.2013.02.017
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    References listed on IDEAS

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    1. Kazemzadeh, Reza B. & Bashiri, Mahdi & Atkinson, Anthony C. & Noorossana, Rassoul, 2008. "A general framework for multiresponse optimization problems based on goal programming," European Journal of Operational Research, Elsevier, vol. 189(2), pages 421-429, September.
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    Cited by:

    1. Wang, Jianjun & Ma, Yizhong & Ouyang, Linhan & Tu, Yiliu, 2016. "A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability," European Journal of Operational Research, Elsevier, vol. 249(1), pages 231-237.

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