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Group Decision and Negotiation

, Volume 28, Issue 6, pp 1201–1230 | Cite as

DSmT-Based Group DEMATEL Method with Reaching Consensus

  • Yuan-Wei DuEmail author
  • Wen Zhou
Article
  • 23 Downloads

Abstract

The decision-making trial and evaluation laboratory (DEMATEL) method employs expert assessments expressed by crisp values to construct a group initial direct-relation (IDR) matrix. However, it tends to be a low-precision expression, especially in complex practical problems. Although significant efforts have been made to improve the DEMATEL method, these improvements tend to neglect individual characteristics and group consensus, resulting in unconvincing decision results. This study provides a Dezert–Smarandache theory-based group DEMATEL method with reaching consensus. In order to reasonably determine the group IDR matrix, basic belief assignment function is employed to extract expert assessments and the proportional conflict redistribution rule no.5 of DSmT is employed to make fusion to derive the temporary group IDR matrix. Moreover, the consensus measures at both expert level and pair-factors level are calculated to determine whether the acceptable consensus level has been reached or not. If the required consensus level is not reached, a feedback mechanism will be activated to help experts reach a consensus. A consensus group IDR matrix for the group DEMATEL can be obtained with the help of feedback mechanism, based on which an algorithm is summarized for the proposed method to identify major factors in a complex system. Finally, numerical comparison and discussion are introduced to verify the effectiveness and applicability of the proposed method and algorithm.

Keywords

DEMATEL Group decision making Dezert–Smarandache theory (DSmT) Consensus reaching Evidence distance Expert weight 

Notes

Acknowledgements

This research was supported by the Major Program of National Social Science Foundation of China under Grant No. 18ZDA055, the Key Program of National Social Science Fund of China under Grant No. 16AJL007, the National Natural Science Foundation of China (NSFC) under Grant Nos. 71874167, 71804170, 71901199 and 71462022, and the Special Funds of Taishan Scholars Project of Shandong Province under Grant No. tsqn20171205.

References

  1. Abdullah L, Zulkifli N, Liao H et al (2019) An interval-valued intuitionistic fuzzy DEMATEL method combined with Choquet integral for sustainable solid waste management. Eng Appl Artif Intell 82:207–215.  https://doi.org/10.1016/j.engappai.2019.04.005 CrossRefGoogle Scholar
  2. Acuña-Carvajal F, Pinto-Tarazona L, López-Ospina H et al (2019) An integrated method to plan, structure and validate a business strategy using fuzzy DEMATEL and the balanced scorecard. Expert Syst Appl 122:351–368.  https://doi.org/10.1016/j.eswa.2019.01.030 CrossRefGoogle Scholar
  3. Addae BA, Zhang L, Zhou P, Wang F (2019) Analyzing barriers of Smart Energy City in Accra with two-step fuzzy DEMATEL. Cities 89:218–227.  https://doi.org/10.1016/j.cities.2019.01.043 CrossRefGoogle Scholar
  4. Asan U, Kadaifci C, Bozdag E et al (2018) A new approach to DEMATEL based on interval-valued hesitant fuzzy sets. Appl Soft Comput J 66:34–49.  https://doi.org/10.1016/j.asoc.2018.01.018 CrossRefGoogle Scholar
  5. Balsara S, Jain PK, Ramesh A (2019) An integrated approach using AHP and DEMATEL for evaluating climate change mitigation strategies of the Indian cement manufacturing industry. Environ Pollut 252:863–878.  https://doi.org/10.1016/j.envpol.2019.05.059 CrossRefGoogle Scholar
  6. Baykasoğlu A, Gölcük İ (2017) Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst Appl 70:37–51.  https://doi.org/10.1016/j.eswa.2016.11.001 CrossRefGoogle Scholar
  7. Baykasoğlu A, Kaplanoglu V, Durmuşoglu ZDU, Şahin C (2013) Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Syst Appl 40:899–907.  https://doi.org/10.1016/j.eswa.2012.05.046 CrossRefGoogle Scholar
  8. Bhatia MS, Srivastava RK (2018) Analysis of external barriers to remanufacturing using grey-DEMATEL approach: an Indian perspective. Resour Conserv Recycl 136:79–87.  https://doi.org/10.1016/j.resconrec.2018.03.021 CrossRefGoogle Scholar
  9. Büyüközkan G, Çifçi G (2012) Evaluation of the green supply chain management practices: a fuzzy ANP approach. Prod Plan Control 23:405–418.  https://doi.org/10.1080/09537287.2011.561814 CrossRefGoogle Scholar
  10. Cabrerizo FJ, Al-Hmouz R, Morfeq A et al (2017) Soft consensus measures in group decision making using unbalanced fuzzy linguistic information. Soft Comput 21:3037–3050.  https://doi.org/10.1007/s00500-015-1989-6 CrossRefGoogle Scholar
  11. Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, New York In collaboration with Frank P. Hwang CrossRefGoogle Scholar
  12. Chen Z, Ming X, Zhang X et al (2019) A rough-fuzzy DEMATEL–ANP method for evaluating sustainable value requirement of product service system. J Clean Prod 228:485–508.  https://doi.org/10.1016/j.jclepro.2019.04.145 CrossRefGoogle Scholar
  13. Cheng CH, Lin Y (2002) Evaluating the best main battle tank using fuzzy decision theory. Eur J Oper Res 142:174–186.  https://doi.org/10.1016/S0377-2217(01)00280-6 CrossRefGoogle Scholar
  14. Cui L, Chan HK, Zhou Y et al (2019) Exploring critical factors of green business failure based on grey-decision making trial and evaluation laboratory (DEMATEL). J Bus Res 98:450–461.  https://doi.org/10.1016/j.jbusres.2018.03.031 CrossRefGoogle Scholar
  15. Denœux T, Kanjanatarakul O, Sriboonchitta S (2015) EK-NNclus: a clustering procedure based on the evidential K-nearest neighbor rule. Knowl Based Syst 88:57–69.  https://doi.org/10.1016/j.knosys.2015.08.007 CrossRefGoogle Scholar
  16. Dinçer H, Yüksel S, Martínez L (2019) Interval type 2-based hybrid fuzzy evaluation of financial services in E7 economies with DEMATEL–ANP and MOORA methods. Appl Soft Comput J 79:186–202.  https://doi.org/10.1016/j.asoc.2019.03.018 CrossRefGoogle Scholar
  17. Du YW, Wang YM (2017) Evidence combination rule with contrary support in the evidential reasoning approach. Expert Syst Appl 88:193–204.  https://doi.org/10.1016/j.eswa.2017.06.045 CrossRefGoogle Scholar
  18. Du YW, Xu WM (2017) Multiattribute group decision making based on interval-valued intuitionistic fuzzy sets and analytically evidential reasoning methodology. J Intell Fuzzy Syst 33:2953–2960.  https://doi.org/10.3233/JIFS-169346 CrossRefGoogle Scholar
  19. Du YW, Zhou W (2019) New improved DEMATEL method based on both subjective experience and objective data. Eng Appl Artif Intell 83:57–71.  https://doi.org/10.1016/j.engappai.2019.05.001 CrossRefGoogle Scholar
  20. Du YW, Yang N, Zhou W, Li CX (2018a) A reliability-based consensus model for multiattribute group decision-making with analytically evidential reasoning approach. Math Probl Eng 2018:1–14.  https://doi.org/10.1155/2018/1651857 CrossRefGoogle Scholar
  21. Du YW, Wang YM, Qin M (2018b) New evidential reasoning rule with both weight and reliability for evidence combination. Comput Ind Eng 124:493–508.  https://doi.org/10.1016/j.cie.2018.07.037 CrossRefGoogle Scholar
  22. Du YW, Yang N, Ning J (2018c) IFS/ER-based large-scale multiattribute group decision-making method by considering expert knowledge structure. Knowl Based Syst 162:124–135.  https://doi.org/10.1016/j.knosys.2018.07.034 CrossRefGoogle Scholar
  23. Du YW, Wang SS, Wang YM (2019) Group fuzzy comprehensive evaluation method under ignorance. Expert Syst Appl 126:92–111.  https://doi.org/10.1016/j.eswa.2019.02.006 CrossRefGoogle Scholar
  24. Faux F, Luthon F (2012) Theory of evidence for face detection and tracking. Int J Approx Reason 53:728–746.  https://doi.org/10.1016/j.ijar.2012.02.002 CrossRefGoogle Scholar
  25. Fontela EG (1974) Structural analysis of the world problematique. Battelle-Genèva Research CentreGoogle Scholar
  26. Gabus (1973) Communicating with those bearing collective responsibility. Battelle-Genèva Research CentreGoogle Scholar
  27. Ghaemi Rad T, Sadeghi-Niaraki A, Abbasi A, Choi SM (2018) A methodological framework for assessment of ubiquitous cities using ANP and DEMATEL methods. Sustain Cities Soc 37:608–618.  https://doi.org/10.1016/j.scs.2017.11.024 CrossRefGoogle Scholar
  28. Gölcük I, Baykasoğlu A (2016) An analysis of DEMATEL approaches for criteria interaction handling within ANP. Expert Syst Appl 46:346–366.  https://doi.org/10.1016/j.eswa.2015.10.041 CrossRefGoogle Scholar
  29. Guo Q, He Y, Jian T et al (2016) An evidence clustering DSmT approximate reasoning method for more than two sources. Digit Signal Process A Rev J 56:79–92.  https://doi.org/10.1016/j.dsp.2016.05.007 CrossRefGoogle Scholar
  30. Herrera F, Herrera-Viedma E, Verdegay JL (1997) A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets Syst 88:31–49CrossRefGoogle Scholar
  31. Herrera-viedma E, Alonso S, Chiclana F, Herrera F (2007) A consensus model for group decision making with incomplete fuzzy preference relations. IEEE Trans Fuzzy Syst 15:863–877CrossRefGoogle Scholar
  32. Huang S, Su X, Hu Y et al (2014) A new decision-making method by incomplete preferences based on evidence distance. Knowl Based Syst 56:264–272.  https://doi.org/10.1016/j.knosys.2013.11.019 CrossRefGoogle Scholar
  33. Kacprzyk J, Zadrożny S (2010) Soft computing and web intelligence for supporting consensus reaching. Soft Comput 14:833–846.  https://doi.org/10.1007/s00500-009-0475-4 CrossRefGoogle Scholar
  34. Lan S, Zhong RY (2016) An evaluation model for financial reporting supply chain using DEMATEL–ANP. Procedia CIRP 56:516–519.  https://doi.org/10.1016/j.procir.2016.10.101 CrossRefGoogle Scholar
  35. Lee HS, Tzeng GH, Yeih W et al (2013) Revised DEMATEL: resolving the infeasibility of DEMATEL. Appl Math Model 37:6746–6757.  https://doi.org/10.1016/j.apm.2013.01.016 CrossRefGoogle Scholar
  36. Li X, Dezert J, Smarandache F, Huang X (2011) Evidence supporting measure of similarity for reducing the complexity in information fusion. Inf Sci 181:1818–1835.  https://doi.org/10.1016/j.ins.2010.10.025 CrossRefGoogle Scholar
  37. Li W, Liang W, Zhang L, Tang Q (2015) Performance assessment system of health, safety and environment based on experts’ weights and fuzzy comprehensive evaluation. J Loss Prev Process Ind 35:95–103.  https://doi.org/10.1016/j.jlp.2015.04.007 CrossRefGoogle Scholar
  38. Lian C, Ruan S, Denœux T (2015) An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognit 48:2318–2327.  https://doi.org/10.1016/j.patcog.2015.01.019 CrossRefGoogle Scholar
  39. Liang H, Ren J, Gao Z et al (2016) Identification of critical success factors for sustainable development of biofuel industry in China based on grey decision-making trial and evaluation laboratory (DEMATEL). J Clean Prod 131:500–508.  https://doi.org/10.1016/j.jclepro.2016.04.151 CrossRefGoogle Scholar
  40. Liu W (2006) Analyzing the degree of conflict among belief functions. Artif Intell 170:909–924.  https://doi.org/10.1016/j.artint.2006.05.002 CrossRefGoogle Scholar
  41. Liu ZG, Dezert J, Pan Q, Mercier G (2011) Combination of sources of evidence with different discounting factors based on a new dissimilarity measure. Decis Support Syst 52:133–141.  https://doi.org/10.1016/j.dss.2011.06.002 CrossRefGoogle Scholar
  42. Liu ZG, Dezert J, Mercier G, Pan Q (2012a) Dynamic evidential reasoning for change detection in remote sensing images. IEEE Trans Geosci Remote Sens 50:1955–1967.  https://doi.org/10.1109/TGRS.2011.2169075 CrossRefGoogle Scholar
  43. Liu ZG, Dezert J, Mercier G, Pan Q (2012b) Belief C-Means: an extension of Fuzzy C-Means algorithm in belief functions framework. Pattern Recognit Lett 33:291–300.  https://doi.org/10.1016/j.patrec.2011.10.011 CrossRefGoogle Scholar
  44. Liu ZG, Pan Q, Dezert J (2013) Evidential classifier for imprecise data based on belief functions. Knowl Based Syst 52:246–257.  https://doi.org/10.1016/j.knosys.2013.08.005 CrossRefGoogle Scholar
  45. Liu ZG, Pan Q, Dezert J, Mercier G (2015a) Credal c-means clustering method based on belief functions. Knowl Based Syst 74:119–132.  https://doi.org/10.1016/j.knosys.2014.11.013 CrossRefGoogle Scholar
  46. Liu ZG, Pan Q, Mercier G, Dezert J (2015b) A new incomplete pattern classification method based on evidential reasoning. IEEE Trans Cybern 45:635–646.  https://doi.org/10.1109/TCYB.2014.2332037 CrossRefGoogle Scholar
  47. Liu ZG, Pan Q, Dezert J, Martin A (2016) Adaptive imputation of missing values for incomplete pattern classification. Pattern Recognit 52:85–95.  https://doi.org/10.1016/j.patcog.2015.10.001 CrossRefGoogle Scholar
  48. Michnik J (2013) Weighted influence non-linear gauge system (WINGS)—an analysis method for the systems of interrelated components. Eur J Oper Res 228:536–544.  https://doi.org/10.1016/j.ejor.2013.02.007 CrossRefGoogle Scholar
  49. Pérez IJ, Cabrerizo FJ, Herrera-Viedma E (2011) Group decision making problems in a linguistic and dynamic context. Expert Syst Appl 38:1675–1688.  https://doi.org/10.1016/j.eswa.2010.07.092 CrossRefGoogle Scholar
  50. Pérez IJ, Cabrerizo FJ, Alonso S et al (2018) On dynamic consensus processes in group decision making problems. Inf Sci 459:20–35.  https://doi.org/10.1016/j.ins.2018.05.017 CrossRefGoogle Scholar
  51. Quezada LE, López-Ospina HA, Palominos PI, Oddershede AM (2018) Identifying causal relationships in strategy maps using ANP and DEMATEL. Comput Ind Eng 118:170–179.  https://doi.org/10.1016/j.cie.2018.02.020 CrossRefGoogle Scholar
  52. Ren J, Manzardo A, Toniolo S, Scipioni A (2013) Sustainability of hydrogen supply chain. Part I: identification of critical criteria and cause-effect analysis for enhancing the sustainability using DEMATEL. Int J Hydrog Energy 38:14159–14171.  https://doi.org/10.1016/j.ijhydene.2013.08.126 CrossRefGoogle Scholar
  53. Saaty TL (2003) Decision-making with the AHP: why is the principal eigenvector necessary. Eur J Oper Res 145:85–91.  https://doi.org/10.1016/S0377-2217(02)00227-8 CrossRefGoogle Scholar
  54. Saaty TL (2007) Time dependent decision-making; dynamic priorities in the AHP/ANP: generalizing from points to functions and from real to complex variables. Math Comput Model 46:860–891.  https://doi.org/10.1016/j.mcm.2007.03.028 CrossRefGoogle Scholar
  55. Sara J, Stikkelman RM, Herder PM (2015) Assessing relative importance and mutual influence of barriers for CCS deployment of the ROAD project using AHP and DEMATEL methods. Int J Greenh Gas Control 41:336–357.  https://doi.org/10.1016/j.ijggc.2015.07.008 CrossRefGoogle Scholar
  56. Shafer G (1996) A mathematical theory of evidence. Princeton University Press, PrincetonGoogle Scholar
  57. Shieh JI, Wu HH, Huang KK (2010) A DEMATEL method in identifying key success factors of hospital service quality. Knowl Based Syst 23:277–282.  https://doi.org/10.1016/j.knosys.2010.01.013 CrossRefGoogle Scholar
  58. Singh R, Vatsa M, Noore A (2008) Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recognit 41:880–893.  https://doi.org/10.1016/j.patcog.2007.06.022 CrossRefGoogle Scholar
  59. Singhal D, Tripathy S, Kumar Jena S (2018) DEMATEL approach for analyzing the critical factors in remanufacturing process. Mater Today Proc 5:18568–18573.  https://doi.org/10.1016/j.matpr.2018.06.200 CrossRefGoogle Scholar
  60. Smarandache F, Dezert J (2004) Advances and applications of DSmT for information fusion. Elsevier, AmsterdamGoogle Scholar
  61. Smarandache F, Dezert J (2006) Advances and applications of DSmT for information fusion. Elsevier, AmsterdamGoogle Scholar
  62. Smarandache F, Dezert J (2009) Advances and applications of DSmT for information fusion. Elsevier, AmsterdamGoogle Scholar
  63. Smarandache F, Dezert J, Tacnet J (2011) Fusion of sources of evidence with different importances and reliabilities. Inf Fusion.  https://doi.org/10.1109/icif.2010.5712071 CrossRefGoogle Scholar
  64. Smets P (2005) Decision making in the TBM: the necessity of the pignistic transformation. Int J Approx Reason 38:133–147.  https://doi.org/10.1016/j.ijar.2004.05.003 CrossRefGoogle Scholar
  65. Stebler N, Schuepbach-Regula G, Braam P, Falzon LC (2015) Use of a modified Delphi panel to identify and weight criteria for prioritization of zoonotic diseases in Switzerland. Prev Vet Med 121:165–169.  https://doi.org/10.1016/j.prevetmed.2015.05.006 CrossRefGoogle Scholar
  66. Wu WW, Lee YT (2007) Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Syst Appl 32:499–507.  https://doi.org/10.1016/j.eswa.2005.12.005 CrossRefGoogle Scholar
  67. Wu J, Chiclana F, Fujita H, Herrera-Viedma E (2017) A visual interaction consensus model for social network group decision making with trust propagation. Knowl Based Syst 122:39–50.  https://doi.org/10.1016/j.knosys.2017.01.031 CrossRefGoogle Scholar
  68. Xu J, Wu Z (2011) A discrete consensus support model for multiple attribute group decision making. Knowl Based Syst 24:1196–1202.  https://doi.org/10.1016/j.knosys.2011.05.007 CrossRefGoogle Scholar
  69. Yang FB, Wang XX (2010) Conflict evidence composition method of D-S evidence. National Defense Industry Press, BeijingGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Management CollegeOcean University of ChinaQingdaoPeople’s Republic of China
  2. 2.Marine Development Studies Institute of OUC, Key Research Institute of Humanities and Social Sciences at UniversitiesMinistry of EducationQingdaoPeople’s Republic of China

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