Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design

    Ali Jahan Affiliation


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.
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Jul 4, 2018
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Adan, O. C.; Ng-a-Tham, J.; Hanke, W.; Sigsgaard, T.; Van Den Hazel, P.; Wu, F. 2007. In search of a common European approach to a healthy indoor environment, Environmental health perspectives 115(6): 983–988.

Al-Oqla, F. M.; Sapuan, S. 2016. Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments, JOM 67(10): 2450–2463.

Al-Oqla, F. M.; Sapuan, S.; Ishak, M. R.; Nuraini, A. 2016. A decision-making model for selecting the most appropriate natural fiber–Polypropylene-based composites for automotive applications, Journal of Composite Materials 50(4): 543–556.

Alemi-Ardakani, M.; Milani, A.; Yannacopoulos, S.; Shokouhi, G. 2015. A multicriteria experimental analysis of impact on fiber reinforced polymer composite laminates, Materials Today Communications 4: 6–15.

Alemi-Ardakani, M.; Milani, A. S.; Yannacopoulos, S.; Shokouhi, G. 2016. On the effect of subjective, objective and combinative weighting in multiple criteria decision making: a case study on impact optimization of composites, Expert Systems with Applications 46: 426–438.

Ardeshirilajimi, A.; Aghanouri, A.; Abedian, A.; Milani, A. 2015. An exponential placement method for materials selection, The International Journal of Advanced Manufacturing Technology 78(1–4): 641–650.

Arvidsson, M.; Gremyr, I. 2008. Principles of robust design methodology, Quality and Reliability Engineering International 24(1): 23–35.

Ashby, M. F. 1989. Materials selection in conceptual design, Materials Science and Technology 5(6): 517–525.

Ashby, M. F. 2000. Multi-objective optimization in material design and selection, Acta Materialia 48(1): 359–369.

Ashby, M. F.; Brechet, Y. J. M.; Cebon, D.; Salvo, L. 2004. Selection strategies for materials and processes, Materials and Design 25(1): 51–67.

Bahraminasab, M.; Jahan, A. 2011. Material selection for femoral component of total knee replacement using comprehensive VIKOR, Materials & Design 32: 4471–4477.

Barzilai, J.; Golany, B. 1994. AHP rank reversal, normalization and aggregation rules, Infor-Information Systems and Operational Research 32(2): 57–64.

Brifcani, N.; Day, R.; Walker, D.; Hughes, S.; Ball, K.; Price, D. 2012. A review of cutting-edge techniques for material selection, in 2nd international conference on advanced composite materials and technologies for aerospace applications, 11–13 June 2012, Wrexham, UK, 58–64.

Cavallini, C.; Giorgetti, A.; Citti, P.; Nicolaie, F. 2013. Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm, Materials & Design 47: 27–34.

Celen, A. 2014. Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market, Informatica 25(2): 185–208.

Chakraborty, S.; Zavadskas, E. K. 2014. Applications of WASPAS method in manufacturing decision making, Informatica 25(1): 1–20.

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.

Dejus, T.; Antucheviciene, J. 2013. Assessment of health and safety solutions at a construction site, Journal of Civil Engineering and Management 19: 728–737.

Deng, H. 2007. A similarity-based approach to ranking multicriteria alternatives, in Advanced intelligent computing theories and applications. With aspects of artificial intelligence. Springer.

Edwards, K. L. 2004. Strategic substitution of new materials for old: applications in automotive product development, Materials & Design 25(6): 529–533.

Edwards, K. L. 2005. Selecting materials for optimum use in engineering components, Materials and Design 26(5): 469–473.

Edwards, K. L.; Deng, Y. M. 2007. Supporting design decision-making when applying materials in combination, Materials and Design 28(4): 1288–1297.

Farag, M. M. 2008. Quantitative methods of materials substitution: application to automotive components, Materials and Design 29(2): 374–380.

Farag, M. M. 2013. Materials and process selection for engineering design. CRC Press.

Figueira, J. R.; Mousseau, V.; Roy, B. 2016. ELECTRE Methods, in S. Greco, M. Ehrgott, R. J. Figueira (Eds.). Multiple criteria decision analysis: state of the art surveys. New York, NY: Springer New York.

Ghorshi Nezhad, M. R.; Zolfani, S. H.; Moztarzadeh, F.; Zavadskas, E. K.; Bahrami, M. 2015. Planning the priority of high tech industries based on SWARA-WASPAS methodology: the case of the nanotechnology industry in Iran, Economic Research-Ekonomska Istrazivanja 28(1): 1111–1137.

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.

Hafezalkotob, A.; Hafezalkotob, A.; Sayadi, M. K. 2016a. Extension of MULTIMOORA method with interval numbers: an application in materials selection, Applied Mathematical Modelling 40(2): 1372–1386.

Hafezalkotob, A.; Hafezalkotob, A.; Sayadi, M. K. 2016b. Extension of MULTIMOORA method with interval numbers: an application in materials selection, Applied Mathematical Modelling 40(2): 1372–1386.

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.

Jahan, A.; Bahraminasab, M.; Edwards, K. L. 2012. A target-based normalization technique for materials selection, Materials & Design 35: 647–654.

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.

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.

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.

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.

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.

Liu, H.-T. 2011. Product design and selection using fuzzy QFD and fuzzy MCDM approaches, Applied Mathematical Modelling 35(1): 482–496.

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.

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.

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.

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.

Peldschus, F. 2008. Experience of the game theory application in construction management, Technological and Economic Development of Economy 14(4): 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.

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.

Shan, M. M.; You, J. X.; Liu, H. C. 2016. Some Interval 2-Tuple linguistic harmonic mean operators and their application in material selection, Advances in Materials Science and Engineering. Article ID 7034938, 1–13.

Shih, H. S.; Shyur, H. J.; Lee, E. S. 2007. An extension of TOPSIS for group decision making, Mathematical and Computer Modelling 45(7–8): 801–813.

Sirisalee, P.; Ashby, M. F.; Parks, G. T.; Clarkson, P. J. 2004. Multi criteria material selection in engineering design, Advanced Engineering Materials 6(1–2): 84–92.

Sirisalee, P.; Ashby, M. F.; Parks, G. T.; John Clarkson, P. 2006. Multi criteria material selection of monolithic and multi materials in engineering design, Advanced Engineering Materials 8(1–2): 48–56.

Stanujkic, D.; Magdalinovic, N.; Jovanovic, R. 2013. A multi-attribute decision making model based on distance from decision maker’s preferences, Informatica 24(1): 103–118.

Stanujkic, D.; Zavadskas, E. K. 2015. A modified weighted sum method based on the decision-maker’s preferred levels of performances, Studies in Informatics and Control 24(4): 61–470.

Turskis, Z.; Zavadskas, E. K.; Antucheviciene, J. ; Kosareva, N. 2015. A hybrid model based on Fuzzy AHP and Fuzzy WASPAS for construction site selection, International Journal of Computers Communications & Control 10(6): 113–128.

Vafaei, N.; Ribeiro, R. A.; Camarinha-Matos, L. M. 2016. Normalization techniques for multi-criteria decision making: analytical hierarchy process case study, in Doctoral Conference on Computing, Electrical and Industrial Systems. Springer, 261–269.

Xue, Y.-X.; You, J.-X.; Lai, X.-D.; Liu, H.-C. 2016. An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information, Applied Soft Computing 38: 703–713.

Zavadskas, E.; Turskis, Z.; Antucheviciene, J.; Zakarevicius, A. 2012. Optimization of weighted aggregated sum product assessment, Elektronika ir elektrotechnika 122(6): 3–6.

Zavadskas, E.; Vilutienė, T.; Turskis, Z.; Šaparauskas, J. 2014a. Multi-criteria analysis of Projects’ performance in construction, Archives of Civil and Mechanical Engineering 14(1): 114–121.

Zavadskas, E. K.; Antucheviciene, J.; Razavi Hajiagha, S. H.; Hashemi, S. S. 2014b. Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF), Journal Applied Soft Computing 24: 1013–1021.

Zavadskas, E. K.; Kalibatas, D.; Kalibatiene, D. 2016. A multi-attribute assessment using WASPAS for choosing an optimal indoor environment, Archives of Civil and Mechanical Engineering 16: 76–85.

Zavadskas, E. K.; Turskis, Z. 2008. A new logarithmic normalization method in games theory, Informatica 19(2): 303–314.

Zavadskas, E. K.; Turskis, Z.; Kildiene, S. 2014c. State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy 20(1): 165–179.

Zeng, Q.-L.; Li, D.-D.; Yang, Y.-B. 2013. VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management, Journal of medical systems 37(2): 1–9.