[1] Dempster AP. Upper and lower probabilities introduced by multivalued mappings. Annals of the institute of statistical mathematics 1967; 38(2): 325-339.
[2] Shafer G. A mathematical theory of evidence. Princeton University Press, Princeton, 1976.
[3] Wu W, Zhang M, Li H, Mi J. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Information Sciences 2005; 174(3): 143-164.
[4] Yang B, Kim KJ. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing 2006; 20(2): 403-420.
[5] Liu Y, Jiang Y, Liu X, Yang S. CSMC: a combination strategy for multi-class classification based on multiple association rules. Knowledge-Based Systems 2008; 21(8): 786-793.
[6] Xiao Z, Yang X, Niu Q, Dong Y, Gong K, Xia S, Pang Y. A new evaluation method based on D-S generalized fuzzy soft sets and its application in medical diagnosis problem. Applied Mathematical Modelling 2012; 36(10): 4592-4604.
[7] Li P, Li S. Interval-valued intuitionistic fuzzy numbers decision-making method based on grey incidence analysis and D–S theory of evidence, Acta Autom. Sin. 2011; 37: 993–998.
[8] Wu D. Supplier selection in a fuzzy group setting: a method using grey related analysis and Dempster–Shafer theory, Expert Syst. Appl. 2009; 36: 8892–8899.
[9] Verbert K, Babuška R, Schutter B. Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis – A comparative study. Engineering Applications of Artificial Intelligence 2017; 60: 136–150.
[10] Walley P. Statistical reasoning with imprecise probabilities. Chapman and Hall, 1991.
[11] Pearl J.. Probabilistic reasoning in intelligent systems: networks of plausible inference. San Francisco, CA: Morgan Kauffmann, 1988.
[12] Zadeh L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1999; 100: 9–34.
[13] Shafer, G. A mathematical theory of evidence. New Jersey: Princeton University Press, 1976.
[14] Yager R.R, Kacprzyk J, Fedrizzi M. Advances in the Dempster–Shafer theory of evidence. John Wiley & Sons, Inc. 1994.
[19] Ghorbani Z, Tavakkoli-Moghaddam R, Vahdani B, Minaee M, Mousavi S.M. Solving an analysis network process model for selection of the dispatching rules by an interval-valued intuitionistic fuzzy set, Management Researches in Iran (Modares Human Sciences) 2014; 18(2):195-214.
[20] Wang X, Zhu J, Song Y, Lei L. Combination of unreliable evidence sources in intuitionistic fuzzy MCDM framework. Knowledge-Based Systems 2016; 97: 24–39.
[21] Razavi Hajiagha S.H,
Hashemi S.S,
Mohammadi Y,
Zavadskas E.K. Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria. Transport 2016; 31: 108-118.
[22]
Zhou H,
Wang J.Q,
Zhang H.Y,
Chen X.H. Linguistic hesitant fuzzy multi-criteria decision-making method based on evidential reasoning. International Journal of Systems Science 2016; 47: 314-327.
[23] Yang J.-B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. European Journal of Operational Research 2001; 131: 31–61.
[24] Yang J-B, Sen P. A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Transactions on Systems, Man, and Cybernetics 1994; 24:1458–1473.
[25] Kabak, O, Ruan D. A comparison study of fuzzy MADM methods in nuclear safeguards evaluation. Journal of Global Optimization 2011; 51: 209–226.
[26] Kabak O, Ruan D. A cumulative belief degree-based approach for missing values in nuclear safeguards evaluation. IEEE Transactions on Knowledge and Data Engineering 2011; 23: 1441–1454.
[27] Zadeh L.A. Fuzzy sets. Information and Control 1965; 8: 338–353.
[28] Atanassov K.T. Intuitionistic fuzzy sets. Fuzzy Sets and Systems 1986; 20: 87–96.