Trade-off in robustness, cost and performance by a multi-objective robust production optimization method

Abstract: Designing a production process normally is involved with some important constraints such as uncertainty, trade-off between production costs and quality, customer’s expectations and production tolerances. In this paper, a novel multi-objective robust optimization model is introduced to investigate the best levels of design variables. The primary objective is to minimize the production cost while increasing robustness and performance. The response surface methodology is utilized as a common approximation model to fit the relationship between responses and design variables in the worst-case scenario of uncertainties. The target mean ratio α is applied to ensure the quality of the process by providing the robustness for all types of quality characteristics and with a trade-off between variability and deviance from the ideal point. The Lp metric method is used to integrate all objectives in one overall function. In order to estimate target value of the quality loss by considering production tolerances, the process capability ratio (Cpm) is applied. At the end, a numerical chemical mixture problem is served to show the applicability of the proposed method.

How to cite this paper

 Parnianifard, A., Azfanizam, A., Ariffin, M & Ismail, M. (2019). Trade-off in robustness, cost and performance by a multi-objective robust production optimization method.International Journal of Industrial Engineering Computations , 10(1), 133-148.

Refrences

Ardakani, M. K., & Noorossana, R. (2008). A new optimization criterion for robust parameter design - The case of target is best. International Journal of Advanced Manufacturing Technology, 38(9), 851–859.
Ben-Tal, A., Ghaoui, L. El, & Nemirovski, A. (2009). Robust optimization.
Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and Applications of Robust Optimization. SIAM Review, 53(3), 464–501.
Beyer, H. G., & Sendhoff, B. (2007). Robust optimization - A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33), 3190–3218.
Boyaci, A. I., Hatipoglu, T., & Balci, E. (2017). Drilling process optimization by using fuzzy-based multi-response surface methodology. Advances in Production Engineering & Management, 12(2), 163.
Chan, L. K., Cheng, S. W., & Spiring, F. A. (1988). A New Measure of Process Capability: Cpm. Journal of Quality Technology, 20(3), 162–175.
Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39–54.
Chen, H.-W., Wong, W. K., & Xu, H. (2012). An augmented approach to the desirability function. Journal of Applied Statistics, 39(3), 599–613.
Chen, W., Wiecek, M. M., & Zhang, J. (1999). Quality utility : a Compromise Programming approach to robust design. Journal of Mechanical Design, 121(2), 179–187.
Chinchuluun, A., & Pardalos, P. M. (2007). A survey of recent developments in multiobjective optimization. Annals of Operations Research, 154(1), 29–50.
Costa, N. R., Louren, J., & Pereira, Z. L. (2011). Desirability function approach: A review and performance evaluation in adverse conditions. Chemometrics and Intelligent Laboratory Systems, 107(2), 234–244.
Deb, K. (2011). Multi-objective optimization using evolutionary algorithms: an introduction, 3–34.
Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European Journal of Operational Research, 235(3), 471–483.
He, Z., Wang, J., Jinho, O., & H. Park, S. (2010). Robust optimization for multiple responses using response surface methodology. Applied Stochastic Models in Business and Industry, 26, 157–171.
Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science {&} Business Media.
Khan, J., Teli, S. N., & Hada, B. P. (2015). Reduction Of Cost Of Quality By Using Robust Design : A Research Methodology. International Journal of Mechanical and Industrial Technology, 2(2), 122–128.
Lukic, D., Milosevic, M., Antic, A., Borojevic, S., & Ficko, M. (2017). Multi-criteria selection of manufacturing processes in the conceptual process planning. Advances in Production Engineering And Management, 12(2), 151–162.
Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395.
Messac, A., & Ismail-Yahaya, A. (2002). Multiobjective robust design using physical programming. Structural and Multidisciplinary Optimization, 23(5), 357–371.
Miettinen, K. (2001). Some methods for nonlinear multi-objective optimization. Evolutionary Multi-Criterion Optimization, 1–20.
Miettinen, K. M. (2012). Nonlinear multiobjective optimization (Vol. 12). Springer Science {&} Business Media.
Myers, R., C.Montgomery, D., & Anderson-Cook, M, C. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments-Fourth Edittion. John Wiley & Sons.
Nha, V. T., Shin, S., & Jeong, S. H. (2013). Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment. European Journal of Operational Research, 229(2), 505–517.
Park, C., & Leeds, M. (2016). A Highly Efficient Robust Design Under Data Contamination. Computers {&} Industrial Engineering, 93, 131–142.
Park, S., & Antony, J. (2008). Robust design for quality engineering and six sigma. World Scientific Publishing Co Inc.
Parkinson, A., Sorensen, C., & Pourhassan, N. (1993). A general approach for robust optimal design. Journal of Mechanical Design, Transactions of the ASME, 115(1), 74–80.
Parnianifard, A., Azfanizam, A. S., Ariffin, M. K. A., & Ismail, M. I. S. (2018). An overview on robust design hybrid metamodeling : Advanced methodology in process optimization under uncertainty. International Journal of Industrial Engineering Computations, 9(1), 1–32.
Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall PTR.
Sahali, M. A., Serra, R., Belaidi, I., & Chibane, H. (2015). Bi-objective robust optimization of machined surface quality and productivity under vibrations limitation. In MATEC Web of Conferences (Vol. 20). EDP Sciences.
Sharma, N. K., & Cudney, E. A. (2011). Signal-to-Noise ratio and design complexity based on Unified Loss Function – LTB case with Finite Target. International Journal of Engineering, Science and Technology, 3(7), 15–24.
Sharma, N. K., Cudney, E. A., Ragsdell, K. M., & Paryani, K. (2007). Quality Loss Function – A Common Methodology for Three Cases. Journal of Industrial and Systems Engineering, 1(3), 218–234.
Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for Computer-based Engineering Design: Survey and recommendations. Engineering With Computers, 17(2), 129–150.
Wang, G., & Shan, S. (2007). Review of Metamodeling Techniques in Support of Engineering Design Optimization. Journal of Mechanical Design, 129(4), 370–380.
Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. Automatic Control, IEEE Transactions on, 8(1), 59–60.