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Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method

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  • Yang, Taho
  • Chou, Pohung

Abstract

The simulation model is a proven tool in solving nonlinear and stochastic problems and allows examination of the likely behavior of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. A practical problem often embodies many characteristics of a multiresponse optimization problem. The present paper proposes to solve the multiresponse simulation-optimization problem by a multiple-attribute decision-making method—a technique for order preference by similarity to ideal solution (TOPSIS). The method assumes that the control factors have discrete values and that each control factor has exactly three control levels. Taguchi quality-loss functions are adapted to model the factor mean and variance effects. TOPSIS is then used to find the surrogate objective function for the multiple responses. The present paper predicts the system performances for any combination of levels of the control factors by using the main effects of the control factors according to the principles of a robust design method. The optimal design can then be obtained. A practical case study from an integrated-circuit packaging company illustrates the efficiency and effectiveness of the proposed method. Finally, constraints of the proposed method are addressed.

Suggested Citation

  • Yang, Taho & Chou, Pohung, 2005. "Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(1), pages 9-21.
    • Handle: RePEc:eee:matcom:v:68:y:2005:i:1:p:9-21
    DOI: 10.1016/j.matcom.2004.09.004
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    References listed on IDEAS

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    1. S-K S Fan & E del Castillo, 1999. "Calculation of an optimal region of operation for dual response systems fitted from experimental data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(8), pages 826-836, August.
    2. Azadivar, Farhad & Lee, Young-Hae, 1988. "Optimization of discrete variable stochastic systems by computer simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 331-345.
    3. T Yang & L Tseng, 2002. "Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 211-221, February.
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    Cited by:

    1. Yang, Taho & Wen, Yuan-Feng & Wang, Fang-Fang, 2011. "Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method," International Journal of Production Economics, Elsevier, vol. 134(2), pages 458-466, December.
    2. Kuo, Yiyo & Yang, Taho & Cho, Chiwoon & Tseng, Yao-Ching, 2008. "Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(1), pages 40-56.
    3. Kannan, Govindan & Pokharel, Shaligram & Sasi Kumar, P., 2009. "A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider," Resources, Conservation & Recycling, Elsevier, vol. 54(1), pages 28-36.
    4. Yao, Shiqing & Jiang, Zhibin & Li, Na & Zhang, Huai & Geng, Na, 2011. "A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 130(1), pages 125-133, March.
    5. Dengiz, Berna & İç, Yusuf Tansel & Belgin, Onder, 2016. "A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 120(C), pages 120-128.

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