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MULTI-OBJECTIVE SIMULATION OPTIMIZATION USING GREY-BASED TAGUCHI METHOD WITH FUZZY AHP WEIGHTING

Year 2015, Volume: 33 Issue: 3, 341 - 350, 01.09.2015

Abstract

Simulation is a powerful tool for analyzing and designing of industrial and service systems. But simulation can’t optimize the system elements and needs additional methods for optimization. In this study a multi-objective simulation optimization is dealt with. To determine the optimum factor levels, grey-based Taguchi approach is used. Since Taguchi method is designed for single objective problems, grey relational analysis is combined with Taguchi method to solve this multi-objective simulation optimization problem. Additionally, in the stage of grade relational calculation of grey relational analysis (GRA) fuzzy AHP weighting process is adopted to determine the weights of grey relational coefficients.

References

  • [1] Rosen, S.L., Harmonosky C.M. and Traband M.T., “Optimization of Systems with Multiple Performance Measures via Simulation: Survey and Recommendations”, Computers & Industrial Engineering, 54: 327–339, (2008).
  • [2] Ding, H., Benyoucef, L., and Xie, X., “A Simulation Optimization Methodology for Supplier Selection Problem”, Int. J. Computer Integrated Manufacturing, Vol. 18, No. 2–3, March–May 2005, 210 – 224, (2004).
  • [3] Azadivar, F., “Simulation Optimization Methodologies,” in Proceedings of the 1999 Winter Simulation Conference, 93-100, (1999).
  • [4] Lin, R-C., Sir, M. Y., Pasupathy, K. S., “Multi-Objective Simulation Optimization Using Data Envelopment Analysis and Genetic Algorithm: Specific Application to Determining Optimal Resource Levels in Surgical Services”, Omega 41: 881–892, (2013).
  • [5] Zhang, H., “Multi-Objective Simulation-Optimization for Earthmoving Operations”, Automation in Construction 18: 79–86, (2008).
  • [6] Yang, T., Kuo, Y. and Chou, P., “Solving a Multiresponse Simulation Problem Using A Dual-Response System And Scatter Search Method”, Simulation Modelling Practice and Theory 13: 356–369, (2005).
  • [7] Willis, K.O., Jones, D.F., “Multi-objective Simulation Optimization Through Search Heuristics and Relational Database Analysis”, Decision Support Systems, 46: 277–286, (2008).
  • [8] Syberfeldt, A., Amos Ng, John, R.I., Moore, P., “Multi-objective Evolutionary Simulation-Optimisation of a Real-world Manufacturing Problem”, Robotics and Computer-Integrated Manufacturing, 25: 926–931, (2009).
  • [9] Lee, L.H., Chew, P. E., Teng, S., Chen, Y., “Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem”, European Journal of Operational Research 189:476–491, (2008).
  • [10] Alrefaei, M.H., Diabat, A.H., “A Simulated Annealing Technique for Multi-Objective Simulation Optimization”, Applied Mathematics and Computation 215:3029–3035, (2009).
  • [11] Teng, S., Lee, L.H., Chew, P.E., “Multi-objective Ordinal Optimization for Simulation Optimization Problems”, Automatica 43:1884 – 1895, (2007).
  • [12] Pasandideh, S.H.R., Niaki, S.T.A., “Multi-response Simulation Optimization Using Genetic Algorithm Within Desirability Function Framework”, Applied Mathematics and Computation, 175:366–382, (2006).
  • [13] Kuo, Y., Yang, T., and Huang, G., “The Use of a Grey-Based Taguchi Method for Optimizing Multi- Response Simulation Problems”, Engineering Optimization, 40:6, 517-528, (2008).
  • [14] Yang, T., Chou, P., “Solving a Multiresponse Simulation-Optimization Problem with Discrete Variables Using a Multiple-Attribute Decision-Making Method”, Mathematics and Computers in Simulation, 68: 9–21, (2005).
  • [15] Dengiz, B., “Redesign of PCB Production Line with Simulation and Taguchi Design”, Proceedings of the 2009 Winter Simulation Conference, 2197-2204, (2009).
  • [16] Chang, C.Y., Huang, R., Lee, P.C., Weng, T.L., “Application of a Weighted Grey-Taguchi Method for Optimizing Recycled Aggregate Concrete Mixtures”, Cement & Concrete Composites 33:1038–1049, (2011).
  • [17] Subbaya, K.M., Suresha, B., Rajendra, N., Varadarajan, Y.S., “Grey-Based Taguchi Approach for Wear Assessment of SiC Filled Carbon–Epoxy Composites”, Materials and Design, 41:124–130, (2012).
  • [18] Kabir, G., and Hasin, M.A.A., “Comparative Analysis of AHP and Fuzzy AHP Models for Multicriteria Inventory Classification”, International Journal of Fuzzy Logic Systems (IJFLS) Vol.1, No.1, (2011).
  • [19] Chang, D.–Y., Zhu, K.–J., Jing, Y., “A Discussion on Extent Analysis Method and Applications of Fuzzy AHP”, European Journal of Operational Research, 116:450-456, (1999).
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Önder Belgin This is me

Publication Date September 1, 2015
Submission Date January 27, 2015
Published in Issue Year 2015 Volume: 33 Issue: 3

Cite

Vancouver Belgin Ö. MULTI-OBJECTIVE SIMULATION OPTIMIZATION USING GREY-BASED TAGUCHI METHOD WITH FUZZY AHP WEIGHTING. SIGMA. 2015;33(3):341-50.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/