The selection of proper material is a formative stage in engineering design and production process. Without a systematic approach, many practical engineering materials may be neglected for selection. Multiple attribute decision making techniques as efficient methodologies can aid decision-makers in finding the optimal option. As a robust multiple attribute decision making approach, the MULTIMOORA method can be usefully employed in materials selection process. In this study, we extend the MULTIMOORA approach based on Shannon entropy concept under fuzzy environment to solve a materials selection case that is a group multiple attribute decision making problem. In the present paper, subjective weights of attributes and the ratings of alternatives on attributes are assumed as fuzzy sets. The entropy concept is considered to assign objective relative importance to decision-making attributes. The fuzzy subjective weights of attributes that are based on decision-makers comments and the crisp objective weights of attributes obtained using the entropy concept are consolidated to generate the fuzzy integrated entropy weights. We evaluate a practical materials selection problem related to automotive industry. We perform a sensitivity analysis by altering aggregation and normalization techniques besides the types of weighting. The materials rankings of the example are compared with the related study.
FaragM.M., Quantitative methods of materials selection, in: Handbook of Materials Selection, KutzM, ed., John Wiley & Sons, Canada, 2002, pp. 3–24.
2.
BakhoumE.S. and BrownD.C., A hybrid approach using AHP–TOPSIS–entropy methods for sustainable ranking of structural materials, International Journal of Sustainable Engineering6 (2013), 212–224.
3.
DasD., Selection of materials in engineering design using Ashby’s chart and AHP-TOPSIS, Bachelor Dissertation, Kolkata, India, Jadavpur University, 2012.
4.
HuangH., ZhangL., LiuZ. and SutherlandJ.W., Multi-criteria decision making and uncertainty analysis for materials selection in environmentally conscious design, International Journal of Advanced Manufacturing Technology52 (2011), 421–432.
5.
JeeD.-H. and KangK.-J., A method for optimal material selection aided with decision making theory, Materials and Design21 (2000), 199–206.
6.
DweiriF. and Al-OqlaF.M., Material selection using analytical hierarchy process, International Journal of Computer Applications in Technology26 (2006), 182–189.
7.
ChauhanA. and VaishR., Pareto optimal microwave dielectric materials, Advanced Science, Engineering and Medicine5 (2013), 149–155.
8.
MilaniA.S., ShanianA., LynamC. and ScarinciT., An application of the analytic network process in multiple criteria material selection, Materials and Design44 (2013), 622–632.
9.
MaityS.R. and ChakrabortyS., Turbine blade material selection using fuzzy analytic network process, International Journal of Materials and Structural Integrity6 (2012), 169–189.
10.
JahanA. and EdwardsK.L., VIKOR method for material selection problems with interval numbers and target-based criteria, Materials and Design47 (2013), 759–765.
11.
LiuH.-C., LiuL. and WuJ., Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights, Materials and Design52 (2013), 158–167.
12.
ChatterjeeP., AthawaleV.M. and ChakrabortyS., Selection of materials using compromise ranking and outranking methods, Materials and Design30 (2009), 4043–4053.
13.
ShanianA. and SavadogoO., A methodological concept for material selection of highly sensitive components based on multiple criteria decision analysis, Expert Systems with Applications36 (2009), 1362–1370.
14.
AnojkumarL., IlangkumaranM. and SasirekhaV., Comparative analysis of MCDM methods for pipe material selection in sugar industry, Expert Systems with Applications41 (2014), 2964–2980.
15.
PengA.-H. and XiaoX.-M., Material selection using PROMETHEE combined with analytic network process under hybrid environment, Materials and Design47 (2013), 643–652.
16.
JiaoQ., LanY., GuanZ. and LiZ., A new material selection approach using PROMETHEE method, Proceeding of the 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), Vol. 6, IEEE, 2011, 2950–2954.
17.
RaoR.V., A material selection model using graph theory and matrix approach, Materials Science and Engineering: A431 (2006), 248–255.
18.
ChanJ.W.K. and TongT.K.L., Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach, Materials and Design28 (2007), 1539–1546.
19.
ZhaoR., NeighbourG., DeutzP. and McGuireM., Materials selection for cleaner production: An environmental evaluation approach, Materials and Design37 (2012), 429–434.
20.
ChatterjeeP. and ChakrabortyS., Material selection using preferential ranking methods, Materials and Design35 (2012), 384–393.
21.
MaityS.R., ChatterjeeP. and ChakrabortyS., Cutting tool material selection using grey complex proportional assessment method, Materials and Design36 (2012), 372–378.
22.
ManiyaK. and BhattM.G., A selection of material using a novel type decision-making method: Preference selection index method, Materials and Design31 (2010), 1785–1789.
23.
AthawaleV.M., KumarR. and ChakrabortyS., Decision making for material selection using the UTA method, International Journal of Advanced Manufacturing Technology57 (2011), 11–22.
24.
FindikF. and TuranK., Materials selection for lighter wagon design with a weighted property index method, Materials and Design37 (2012), 470–477.
25.
JahanA., IsmailM.Y., MustaphaF. and SapuanS.M., Material selection based on ordinal data, Materials and Design31 (2010), 3180–3187.
26.
ManshadiB.D., MahmudiH., AbedianA. and MahmudiR., A novel method for materials selection in mechanical design: Combination of non-linear normalization and a modified digital logic method, Materials and Design28 (2007), 8–15.
27.
TorrezJ.B., MilaniA.S. and SchuetzeD.D.C., Material selection for light-weight naval crafts using the modified digital logic method: Dealing with ranking abnormalities, International Journal of Materials and Structural Integrity6 (2012), 241–256.
28.
FayazbakhshK., AbedianA., ManshadiB.D. and KhabbazR.S., Introducing a novel method for materials selection in mechanical design using Z-transformation in statistics for normalization of material properties, Materials and Design30 (2009), 4396–4404.
29.
FayazbakhshK. and AbedianA., Materials selection for applications in space environment considering outgassing phenomenon, Advances in Space Research45 (2010), 741–749.
30.
MayyasA., ShenQ., MayyasA., ShanD., QattawiA. and OmarM., Using quality function deployment and analytical hierarchy process for material selection of body-in-white, Materials and Design32 (2011), 2771–2782.
31.
PrasadK. and ChakrabortyS., A quality function deployment-based model for materials selection, Materials and Design49 (2013), 525–535.
32.
PrasadK., Selection of materials, machining centers and advanced machining processes using quality function deployment technique, Master Dissertation, Kolkata, India,Jadavpur University, 2013.
33.
XueY.-X., YouJ.-X., LaiX.-D. and LiuH.-C., An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information, Applied Soft Computing38 (2016), 703–713.
34.
JahanA. and EdwardsK.L., Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design, UK: Butterworth-Heinemann: 2013.
35.
JahanA., IsmailM.Y., SapuanS.M. and MustaphaF., Material screening and choosing methods — a review, Materials and Design31 (2010), 696–705.
36.
BrauersW.K.M. and ZavadskasE.K., The MOORA method and its application to privatization in a transition economy, Control and Cybernetics35 (2006), 445–469.
37.
ChakrabortyS., Applications of the MOORA method for decision making in manufacturing environment, International Journal of Advanced Manufacturing Technology54 (2011), 1155–1166.
38.
BrauersW.K.M. and ZavadskasE.K., Project management by MULTIMOORA as an instrument for transition economies, Technological and Economic Development of Economy16 (2010), 5–24.
39.
KarandeP. and ChakrabortyS., Decision making for supplier selection using the MOORA method, The IUP Journal of Operations Management11 (2012), 6–18.
40.
SiddiquiZ. and TyagiK., Application of fuzzy-MOORA method: Ranking of components for reliability estimation of component-based software systems, Decision Science Letters5 (2016), 169–188.
41.
KarabasevicD., StanujkicD., UrosevicS. and MaksimovicM., Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods, Acta Montanistica Slovaca20 (2015), 116–124.
42.
StanujkicD., MagdalinovicN., MilanovicD., MagdalinovicS. and PopovicG., An efficient and simple multiple criteria model for a grinding circuit selection based on moora method, Informatica25 (2014), 73–93.
43.
HafezalkotobA., HafezalkotobA. and SayadiM.K., Extension of MULTIMOORA method with interval numbers: An application in materials selection, Applied Mathematical Modelling40 (2016), 1372–1386.
44.
HafezalkotobA. and HafezalkotobA., Extended MULTIMOORA method based on Shannon entropy weight for materials selection, Journal of Industrial Engineering International12 (2016), 1–13.
45.
HafezalkotobA. and HafezalkotobA., Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications, Materials and Design87 (2015), 949–959.
46.
KarandeP. and ChakrabortyS., Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection, Materials and Design37 (2012), 317–324.
47.
ÖzçelikG., AydoganE.K and GencerC, A hybrid moora-fuzzy algorithm for special education and rehabilitation center selection, Journal of Military and Information Science2 (2014), 53–62.
48.
El-SantawyM.F., An IEW-MOORA approach for solving MCDM problems, Computing & Information Systems18 (2014), 21–23.
49.
BaležentisA., BaležentisT and BrauersW.K, Personnel selection based on computing with words and fuzzy MULTIMOORA, Expert Systems with Applications39 (2012), 7961–7967.
50.
BaležentisA., BaležentisT and BrauersW.K, MULTIMOORA-FG: A multi-objective decision makingmethod for linguistic reasoning with an application to personnel selection, Informatica23 (2012), 173–190.
51.
KarandeP. and ChakrabortyS., A fuzzy-MOORA approach for ERP system selection, Decision Science Letters1 (2012), 11–21.
52.
MandalU.K. and SarkarB., Selection of best intelligent manufacturing system (IMS) under fuzzy MOORA conflicting MCDM environment, International Journal of Emerging Technology and Advanced Engineering2 (2012), 2250–2459.
53.
DeyB., BairagiB., SarkarB. and SanyalS., A MOORA based fuzzy multi-criteria decision making approach for supply chain strategy selection, International Journal of Industrial Engineering Computations3 (2012), 649–662.
54.
DattaS., SahuN. and MahapatraS., Robot selection based on grey-MULTIMOORA approach, Grey Systems: Theory and Application3 (2013), 201–232.
55.
BrauersW.K.M. and ZavadskasE.K., Robustness of MULTIMOORA: A method for multi-objective optimization, Informatica23 (2012), 1–25.
56.
ShannonC.E., A mathematical theory of communication, Bell System Technical Journal27 (1948), 379–423.
57.
HwangC.L. and YoonK., Multiple Attribute Decision Making: Methods and Applications, A State-of-the-Art Survey, Berlin: Springer; 1981.
58.
ZadehL.A., Fuzzy sets, Information and Control8 (1965), 338–353.
59.
BellmanR.E. and ZadehL.A., Decision-making in a fuzzy environment, Management Science17 (1970), B–141–B–164.
60.
LiuH.-C., FanX.-J., LiP. and ChenY.-Z., Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment, Engineering Applications of Artificial Intelligence34 (2014), 168–177.
61.
WanS.-P. and LiD.-F., Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees, Omega41 (2013), 925–940.
62.
WangY.-M., Centroid defuzzification and the maximizing set and minimizing set ranking based on alpha level sets, Computers & Industrial Engineering57 (2009), 228–236.
63.
BrauersW.K.M. and BaležentisA. and BaležentisT, MULTIMOORA for the EU Member States updated with fuzzy number theory, Technological and Economic Development of Economy17 (2011), 259–290.
64.
GirubhaR.J. and VinodhS., Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component, Materials and Design37 (2012), 478–486.
