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Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems

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Listed:
  • Nha, Vo Thanh
  • Park, Kyungjin
  • Jang, Hyeonae
  • Lee, Gyu M.
  • Le, Tuan-Ho
  • Jeong, Seong Hoon
  • Shin, Sangmun

Abstract

Experimental design and robust design (RD) methodologies have received attention from researchers to improve the performance of many different quality characteristics and solve problems at low costs. However, there is room for improvement to simultaneously solve interdisciplinary optimization problems associated with time-oriented, multiple, and hierarchical responses. This paper proposes a new RD modeling and optimization algorithm for drug development based on three research motivations: Firstly, customized experiments and estimation frameworks for representing pharmaceutical quality characteristics (i.e., time-oriented, multiple, and hierarchical responses) and functional relationships between input factors and hierarchical time-oriented output responses are proposed. Secondly, new hierarchical time-oriented robust design (HTRD) optimization models (i.e., priority-based, weight-based, and integrated models) are developed for these interdisciplinary pharmaceutical formulation problems. Finally, the pharmaceutical case study for drug formulation development is conducted for demonstration purposes. Based on the case study results, the proposed approach can provide optimal solutions with significantly small biases and variances.

Suggested Citation

  • Nha, Vo Thanh & Park, Kyungjin & Jang, Hyeonae & Lee, Gyu M. & Le, Tuan-Ho & Jeong, Seong Hoon & Shin, Sangmun, 2025. "Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems," Operations Research Perspectives, Elsevier, vol. 15(C).
    • Handle: RePEc:eee:oprepe:v:15:y:2025:i:c:s2214716025000314
    DOI: 10.1016/j.orp.2025.100355
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    References listed on IDEAS

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    1. Sangmun Shin & Byung Cho, 2009. "Studies on a biobjective robust design optimization problem," IISE Transactions, Taylor & Francis Journals, vol. 41(11), pages 957-968.
    2. 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.
    3. Tong, Lee-Ing & Wang, Chung-Ho & Chen, Chih-Chien & Chen, Chun-Tzu, 2004. "Dynamic multiple responses by ideal solution analysis," European Journal of Operational Research, Elsevier, vol. 156(2), pages 433-444, July.
    4. Seong Beom Lee & Chanseok Park & Byung-Rae Cho, 2007. "Development of a highly efficient and resistant robust design," International Journal of Production Research, Taylor & Francis Journals, vol. 45(1), pages 157-167, January.
    5. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    Full references (including those not matched with items on IDEAS)

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