This study presents a new soft computing approach based on failure mode and effects analysis (FMEA)’s concept for sustainable supplier selection problem (SSSP) in the light of multi-attributes decision analysis. The approach determines weights of experts by considering interval-valued fuzzy sets (IVFSs) and asymmetric uncertainty information simultaneously. The presented group decision approach assesses the sustainable suppliers as indicated by risks of economic, social and environmental measurements. Ideas of fuzzy possibilistic statistical modeling are brought into the soft computing approach. New definitions are enhanced by fuzzy possibilistic statistical positive and negative ideal solutions. In addition, new relations of fuzzy evaluating and prioritizing of the risks are presented. Then, an application example is tackled by the presented soft computing approach to exhibit its capacity by three measurements of the sustainability for the evaluation of sustainable suppliers.
ForoozeshN., Tavakkoli-MoghaddamR. and MousaviS.M., A novel group decision model based on mean–variance–skewness concepts and interval-valued fuzzy sets for a selection problem of the sustainable warehouse location under uncertainty, Neural Computing and Applications (2017), Article in press doi:10.1007/s00521-017-2885-z.
2.
ViswanadhamN. and SamvediA., Supplier selection based on supply chain ecosystem, performance and risk criteria, International Journal of Production Research51(21) (2013), 6484–6498.
3.
PaulS.K., Supplier selection for managing supply risks in supply chain: A fuzzy aroach, International Journal of Advanced Manufacturing Technology79(1-4) (2015), 657–664.
4.
KumarD., RahmanZ. and ChanF.T., A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study, International Journal of Computer Integrated Manufacturing (2016), 1–17.
5.
BuddressL., Managing supply chain sustainability and risk: Keys to success, Strategic Management18(2) (2013), 3–16.
6.
BlackhurstJ.V., ScheibeK.P. and JohnsonD.J., Supplier risk assessment and monitoring for the automotive industry, International Journal of Physical Distribution & Logistics Management38(2) (2008), 143–165.
7.
KullT.J. and TalluriS., A supply risk reduction model using integrated multicriteria decision making, IEEE Transactions on Engineering Management55(3) (2008), 409–419.
8.
MatookS., LaschR. and TamaschkeR., Supplier development with benchmarking as part of a comprehensive supplier risk management framework, International Journal of Operations & Production Management29(3) (2009), 241–267.
9.
DalalahD., HayajnehM. and BatiehaF., A fuzzy multi-criteria decision-making model for supplier selection, Expert Systems with Applications38(7) (2011), 8384–8391.
10.
GoebelP., ReuterC., PibernikR. and SichtmannC., The influence of ethical culture on supplier selection in the context of sustainable sourcing, International Journal of Production Economics140(1) (2012), 7–17.
11.
ChaiJ., LiuJ.N. and NgaiE.W., Application of decision-making techniques in supplier selection: A systematic review of literature, Expert Systems with Applications40(10) (2013), 3872–3885.
12.
GiannakisM. and PapadopoulosT., Supply chain sustainability: A risk management approach, International Journal of Production Economics171 (2016), 455–470.
13.
GovindanK. and JepsenM.B., Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: A case illustration involving service suppliers, Journal of the Operational Research Society67(2) (2016), 339–376.
14.
ModakN.M., PandaS. and SanaS.S., Two-echelon supply chain coordination among manufacturer and duopolies retailers with recycling facility, International Journal of Advanced Manufacturing Technology87(5-8) (2016), 1531–1546.
15.
PandaS., ModakN.M. and Cárdenas-BarrónL.E., Coordinating a socially responsible closed-loop supply chain with product recycling, International Journal of Production Economics188 (2017), 11–21.
16.
YueZ., A method for group decision-making based on determining weights of decision makers using TOPSIS, Applied Mathematical Modelling35(4) (2011), 1926–1936.
17.
KahramanC., OnarS.C. and OztaysiB., Fuzzy multicriteria decision-making: A literature review, International Journal of Computational Intelligence Systems8(4) (2015), 637–666.
18.
KahramanC., ParchamiA., OnarS.C. and OztaysiB., Process capability analysis using intuitionistic fuzzy sets, Journal of Intelligent & Fuzzy Systems32(3) (2017), 1659–1671.
19.
GitinavardH., MousaviS.M. and VahdaniB., A new balancing and ranking method based on hesitant fuzzy sets for solving decision-making problems under uncertainty, International Journal of Engineering-Transactions B: Applications28(2) (2014), 214–223.
20.
GitinavardH., MousaviS.M. and VahdaniB., Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study, Energy118 (2017), 556–577.
21.
GitinavardH., MousaviS.M., VahdaniB. and SiadatA., A distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems, Scientia Iranica Transaction E, Industrial Engineering23(4) (2016), 1928–1940.
22.
VahdaniB., SalimiM. and MousaviS.M., A compromise decision-making model based on VIKOR for multi-objective large-scale nonlinear programming problems with a block angular structure under uncertainty, Scientia Iranica Transaction E, Industrial Engineering22(6) (2015), 2571–2584.
23.
ForoozeshN., GitinavardH., MousaviS.M. and VahdaniB., A hesitant fuzzy extension of VIKOR method for evaluation and selection problems under uncertainty, International Journal of Applied Management Science9(2) (2017), 95–113.
24.
ForoozeshN., Tavakkoli-MoghaddamR., MousaviS.M. and VahdaniB., Dispatching Rule Evaluation in Flexible Manufacturing Systems by a New Fuzzy Decision Model with Possibilistic-Statistical Uncertainties, Arabian Journal for Science and Engineering42(7) (2017), 2947–2960.
25.
RoshanaeiV., VahdaniB., MousaviS.M., MousakhaniM. and ZhangG., CAD/CAM system selection: A multi-component hybrid fuzzy MCDM model, Arabian Journal for Science and Engineering38(9) (2013), 2579–2594.
26.
HashemiH., BazarganJ. and MousaviS.M., A compromise ratio method with an application to water resources management: An intuitionistic fuzzy set, Water resources management27(7) (2013), 2029–2051.
27.
MousaviS.M., JolaiF., Tavakkoli-MoghaddamR. and VahdaniB., A fuzzy grey model based on the compromise ranking for multi-criteria group decision making problems in manufacturing systems, Journal of Intelligent & Fuzzy Systems24(4) (2013), 819–827.
28.
MousaviS.M., VahdaniB., Tavakkoli-MoghaddamR. and TajikN., Soft computing based on a fuzzy grey group compromise solution approach with an application to the selection problem of material handling equipment, International Journal of Computer Integrated Manufacturing27(6) (2014), 547–569.
29.
MousaviS.M., VahdaniB. and BehzadiS.S., Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems, Iranian Journal of Fuzzy Systems13(1) (2016), 45–65.
30.
EbrahimnejadS., HashemiH., MousaviS.M. and VahdaniB., A new interval-valued intuitionistic fuzzy model to group decision making for the selection of outsourcing providers, Journal of Economic Computation and Economic Cybernetics Studies and Research49(2) (2015), 269–290.
31.
EbrahimnejadS., MousaviS.M., Tavakkoli-MoghaddamR. and HeydarM., Risk ranking in mega projects by fuzzy compromise approach: A comparative analysis, Journal of Intelligent & Fuzzy Systems26(2) (2014), 949–959.
32.
MousaviS.M. and VahdaniB., Cross-docking location selection in distribution systems: A new intuitionistic fuzzy hierarchical decision model, International Journal of Computational Intelligence Systems9(1) (2016), 91–109.
33.
VahdaniB., MousaviS.M. and EbrahimnejadS., Soft computing-based preference selection index method for human resource management, Journal of Intelligent & Fuzzy Systems26(1) (2014), 393–403.
34.
ZhangW.G., WangY.L., ChenZ.P. and NieZ.K., Possibilistic mean–variance models and efficient frontiers for portfolio selection problem, Information Sciences177(13) (2007), 2787–2801.
35.
YeF. and LinQ., Partner selection in a virtual enterprise: A group multiattribute decision model with weighted possibilistic mean values, Mathematical Problems in Engineering 2013, Vol. 2013 (2013) doi:.
36.
DengX. and LiR., Gradually tolerant constraint method for fuzzy portfolio based on possibility theory, Information Sciences259 (2014), 16–24.
37.
LiX., QinZ. and KarS., Mean-variance-skewness model for portfolio selection with fuzzy returns, European Journal of Operational Research202(1) (2010), 239–247.
38.
KamdemJ.S., DeffoC.T. and FonoL.A., Moments and semi-moments for fuzzy portfolio selection, Insurance: Mathematics and Economics51(3) (2012), 517–530.
