Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design
Abstract
Recently, considerable attention has been devoted to application of multi-attribute decision-making (MADM) method in materials selection. Normalization can be considered as a foundation for rational MADM methods, which should deal with target-based criteria in addition to cost and benefit criteria. Although a good number of applications have been reported for point target criteria in MADM problems, in selection problems related to engineering design, it might be better to let the material and design criteria vary over a range in order to increase flexibility in subsequent design stages. The mentioned point supports a readily adaptable design in changing the customer requirements, which is also significant in offering a robust design. In this research, performance of three promising target-based normalization methods was investigated using simulation experiments to examine the effect of simulation parameters. The effect of parameters and normalization methods was examined using analysis of variance (ANOVA). Moreover, the best structure formula was identified to propose an inclusive range target-based normalization method. The suggested normalization method was used to enhance the capability of Weighted Aggregated Sum Product Assessment (WASPAS) method and applied to a real-word problem dealing with benefit-, cost-, and point target-based criteria as well as the range criterion.
Keyword : target-based criteria in MADM, ANOVA, selection, normalization, robust design, WASPAS-RTB
How to Cite
Jahan, A. (2018). Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design. Technological and Economic Development of Economy, 24(4), 1362–1387. https://doi.org/10.3846/20294913.2017.1295288
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ashby, M. F. 1989. Materials selection in conceptual design, Materials Science and Technology 5(6): 517–525. https://doi.org/10.1179/mst.1989.5.6.517
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Barzilai, J.; Golany, B. 1994. AHP rank reversal, normalization and aggregation rules, Infor-Information Systems and Operational Research 32(2): 57–64. https://doi.org/10.1080/03155986.1994.11732238
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Chakraborty, S.; Zavadskas, E. K. 2014. Applications of WASPAS method in manufacturing decision making, Informatica 25(1): 1–20. https://doi.org/10.15388/Informatica.2014.01
Chatterjee, P.; Athawale, V. M.; Chakraborty, S. 2011. Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design 32: 851–860. https://doi.org/10.15388/Informatica.2014.01
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Hafezalkotob, A.; Hafezalkotob, A. 2016. Risk-based material selection process supported on information theory: a case study on industrial gas turbine, Applied Soft Computing 52: 1116–1129. https://doi.org/10.1016/j.asoc.2016.09.018
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Ishak, N. M.; Malingam, S. D.; Mansor, M. R. 2016. Selection of natural fibre reinforced composites using fuzzy VIKOR for car front hood, International Journal of Materials and Product Technology 53(3–4): 267–285. https://doi.org/10.1504/IJMPT.2016.079205
Jahan, A.; Bahraminasab, M.; Edwards, K. L. 2012. A target-based normalization technique for materials selection, Materials & Design 35: 647–654. https://doi.org/10.1016/j.matdes.2011.09.005
Jahan, A.; Edwards, K. L. 2015. A state-of-the-art survey on the influence of normalization techniques in ranking: improving the materials selection process in engineering design, Materials & Design 65: 335–342. https://doi.org/10.1016/j.matdes.2014.09.022
Jahan, A.; Edwards, K. L.; Bahraminasab, M. 2016. Multi-criteria decision analysis for supporting the selection of engineering materials in product design. Oxford, Butterworth-Heinemann.
Jahan, A.; Ismail, M. Y.; Sapuan, S. M.; Mustapha, F. 2010. Material screening and choosing methods – a review, Materials & Design 31(2): 696–705. https://doi.org/10.1016/j.matdes.2009.08.013
Jahan, A.; Mustapha, F.; Ismail, M. Y.; Sapuan, S. M.; Bahraminasab, M. 2011. A comprehensive VIKOR method for material selection, Materials & Design 32(3): 1215–1221. https://doi.org/10.1016/j.matdes.2010.10.015
Kabir, G.; Lizu, A. 2016. Material selection for femoral component of total knee replacement integrating fuzzy AHP with PROMETHEE, Journal of Intelligent and Fuzzy Systems 30(6): 3481–3493. https://doi.org/10.3233/IFS-162094
Liou, J. J. H.; Tamošaitienė, J.; Zavadskas, E. K.; Tzeng, G.-H. 2016. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management, International Journal of Production Research 54(1): 114–134. https://doi.org/10.1080/00207543.2015.1010747
Liu, H.-T. 2011. Product design and selection using fuzzy QFD and fuzzy MCDM approaches, Applied Mathematical Modelling 35(1): 482–496. https://doi.org/10.1016/j.apm.2010.07.014
Mardani, A.; Zavadskas, E. K.; Govindan, K.; Amat Senin, A.; Jusoh, A. 2016. VIKOR technique: a systematic review of the state of the art literature on methodologies and applications, Sustainability 8(1): 1–37. https://doi.org/10.3390/su8010037
Mastura, M.; Sapuan, S.; Mansor, M.; Nuraini, A. 2016. Environmentally conscious hybrid bio-composite material selection for automotive anti-roll bar, The International Journal of Advanced Manufacturing Technology 89(5–8): 2203–2219.
https://doi.org/10.1007/s00170-016-9217-9
Milani, A. S.; Shanian, A.; Madoliat, R.; Nemes, J. A. 2005. The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection, Structural and Multidisciplinary Optimization 29(4): 312–318. https://doi.org/10.1007/s00158-004-0473-1
Nayak, S.; Misra, B.; Behera, H. 2014. Impact of data normalization on stock index forecasting, International Journal of Computer Information Systems and Industrial Management Applications 6: 257–269.
Opricovic, S.; Tzeng, G. H. 2004. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research 156(2): 445–455.
https://doi.org/10.1016/S0377-2217(03)00020-1
Peldschus, F. 2008. Experience of the game theory application in construction management, Technological and Economic Development of Economy 14(4): 531–545.
https://doi.org/10.3846/1392-8619.2008.14.531-545
Perez, E. C.; Lamata, M.; Verdegay, J. 2016. RIM-Reference Ideal Method in Multicriteria Decision Making, Information Sciences 337–338: 1–10.
Prasad, K.; Chakraborty, S. 2013. A quality function deployment-based model for materials selection, Materials & Design 49: 525–535. https://doi.org/10.1016/j.matdes.2013.01.035
Sapuan, S. M.; Mujtaba, I. M.; Wright, C. S. 2009. State of the art review of engineering materials selection methods, Multidiscipline Modeling in Materials and Structures 3(5): 263–268. https://doi.org/10.1108/15736105200900021
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