214
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Cloud model-based best-worst method for group decision making under uncertainty

ORCID Icon, ORCID Icon, &
Pages 232-258 | Received 14 Sep 2022, Accepted 27 Jul 2023, Published online: 12 Oct 2023

References

  • Abel E, Mikhailov L, Keane J. 2015. Group aggregation of pairwise comparisons using multi-objective optimization. Inf Sci. 322:257–275. doi:10.1016/j.ins.2015.05.027.
  • Al-Fraihat D, Joy M, Masa’deh R, Sinclair J. 2020. Evaluating E-learning systems success: an empirical study. Comput Hum Behav. 102:67–86. doi:10.1016/j.chb.2019.08.004.
  • Billard L, Diday E. 2003. From the statistics of data to the statistics of knowledge: symbolic data analysis. J Am Stat Assoc. 98(462):470–487. doi:10.1198/016214503000242.
  • Blagojevic B, Srdjevic B, Srdjevic Z, Zoranovic T. 2016. Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm. Inf Sci. 330:260–273. doi:10.1016/j.ins.2015.10.033.
  • Bode C, Wagner SM. 2015. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. J Ops Manage. 36(1):215–228. doi:10.1016/j.jom.2014.12.004.
  • Bolloju N. 2001. Aggregation of analytic hierarchy process models based on similarities in decision makers’ preferences. Eur J Oper Res. 128(3):499–508. doi:10.1016/S0377-2217(99)00369-0.
  • Chen Z, Ming X. 2020. A rough–fuzzy approach integrating best–worst method and data envelopment analysis to multi-criteria selection of smart product service module. Appl Soft Comput. 94:106479. doi:10.1016/j.asoc.2020.106479.
  • de Carvalho FdAT, Neto EdAL, da Silva KCF. 2021. A clusterwise nonlinear regression algorithm for interval-valued data. Inf Sci. 555:357–385. doi:10.1016/j.ins.2020.10.054.
  • Delone WH, McLean ER. 2003. The DeLone and McLean model of information systems success: a ten-year update. J Manage Inf Syst. 19(4):9–30.
  • Durbach IN, Stewart TJ. 2012. Modeling uncertainty in multi-criteria decision analysis. Eur J Oper Res. 223(1):1–14. doi:10.1016/j.ejor.2012.04.038.
  • Edwards W. 1977. How to use multiattribute utility measurement for social decisionmaking. IEEE Trans Syst Man Cybern. 7(5):326–340. doi:10.1109/TSMC.1977.4309720.
  • Fernández E, Navarro J, Solares E. 2022. A hierarchical interval outranking approach with interacting criteria. Eur J Oper Res. 298(1):293–307. doi:10.1016/j.ejor.2021.06.065.
  • Forman E, Peniwati K. 1998. Aggregating individual judgments and priorities with the analytic hierarchy process. Eur J Oper Res. 108(1):165–169. doi:10.1016/S0377-2217(97)00244-0.
  • Golany B, Kress M. 1993. A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices. Eur J Oper Res. 69(2):210–220. doi:10.1016/0377-2217(93)90165-J.
  • Haddad M, Sanders D, Tewkesbury G. 2020. Selecting a discrete multiple criteria decision making method for Boeing to rank four global market regions. Transp Res A Policy Pract. 134:1–15. doi:10.1016/j.tra.2020.01.026.
  • Havens TC, Wagner C, Anderson DT. 2017. Efficient modeling and representation of agreement in interval-valued data. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6. doi:10.1109/FUZZ-IEEE.2017.8015466.
  • Khorshidi HA, Aickelin U. 2021. Multicriteria group decision-making under uncertainty using interval data and cloud models. J Oper Res Soc. 72(11):2542–2556. doi:10.1080/01605682.2020.1796541.
  • Khorshidi HA, Gunawan I, Nikfalazar S. 2016. Application of fuzzy risk analysis for selecting critical processes in implementation of SPC with a case study. Group Decis Negot. 25(1):203–220. doi:10.1007/s10726-015-9439-5.
  • Li D, Du Y. 2007. Artificial intelligence with uncertainty. CRC Press.
  • Li D, Liu C, Gan W. 2009. A new cognitive model: cloud model. Int J Intell Syst. 24(3):357–375. doi:10.1002/int.20340.
  • Liu H-C, Wang L-E, Li Z, Hu Y-P. 2019. Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans Fuzzy Syst. 27(1):84–95. doi:10.1109/TFUZZ.2018.2861719.
  • Liu Y, Wang X-K, Wang J-Q, Li L, Cheng P-F. 2020. Cloud model-based PROMETHEE method under 2D uncertain linguistic environment. IFS. 38(4):4869–4887. doi:10.3233/JIFS-191546.
  • Maadi M, Aickelin U, Khorshidi HA. 2020. An interval-based aggregation approach based on bagging and interval agreement approach in ensemble learning. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 692–699. doi:10.1109/SSCI47803.2020.9308611.
  • Meni̇z B. 2021. An advanced TOPSIS method with new fuzzy metric based on interval type-2 fuzzy sets. Expert Syst Appl. 186:115770. doi:10.1016/j.eswa.2021.115770.
  • Mi X, Tang M, Liao H, Shen W, Lev B. 2019. The state-of-the-art survey on integrations and applications of the best worst method in decision making: why, what, what for and what’s next? Omega. 87:205–225. doi:10.1016/j.omega.2019.01.009.
  • Mohammadi M, Rezaei J. 2020. Bayesian best-worst method: a probabilistic group decision making model. Omega. 96:102075. doi:10.1016/j.omega.2019.06.001.
  • Mou Q, Xu Z, Liao H. 2016. An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Inf Sci. 374:224–239. doi:10.1016/j.ins.2016.08.074.
  • Mustajoki J, Hämäläinen RP, Salo A. 2005. Decision support by interval SMART/SWING—incorporating imprecision in the SMART and SWING methods. Decision Sci. 36(2):317–339. doi:10.1111/j.1540-5414.2005.00075.x.
  • Ozkan S, Koseler R. 2009. Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation. Comput Educ. 53(4):1285–1296. doi:10.1016/j.compedu.2009.06.011.
  • Peng H-G, Wang J-Q. 2018. A multicriteria group decision-making method based on the normal cloud model with Zadeh’sz-numbers. IEEE Trans Fuzzy Syst. 26(6):3246–3260. doi:10.1109/TFUZZ.2018.2816909.
  • Pérez IJ, Cabrerizo FJ, Alonso S, Dong YC, Chiclana F, Herrera-Viedma E. 2018. On dynamic consensus processes in group decision making problems. Inf Sci. 459:20–35. doi:10.1016/j.ins.2018.05.017.
  • Qin J, Liu X, Pedrycz W. 2017. An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. Eur J Oper Res. 258(2):626–638. doi:10.1016/j.ejor.2016.09.059.
  • Rabiee M, Aslani B, Rezaei J. 2021. A decision support system for detecting and handling biased decision-makers in multi criteria group decision-making problems. Expert Syst Appl. 171:114597. doi:10.1016/j.eswa.2021.114597.
  • Raj A, Srivastava SK. 2018. Sustainability performance assessment of an aircraft manufacturing firm. BIJ. 25(5):1500–1527. doi:10.1108/BIJ-01-2017-0001.
  • Ren J. 2018. Selection of sustainable prime mover for combined cooling, heat, and power technologies under uncertainties: an interval multicriteria decision‐making approach. Int J Energy Res. 42(8):2655–2669. doi:10.1002/er.4050.
  • Rezaei J. 2015. Best-worst multi-criteria decision-making method. Omega. 53:49–57. doi:10.1016/j.omega.2014.11.009.
  • Rezaei J. 2016. Best-worst multi-criteria decision-making method: some properties and a linear model. Omega. 64:126–130. doi:10.1016/j.omega.2015.12.001.
  • Rezaei J. 2021. Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making. J Decision Syst. 30(1):72–96. doi:10.1080/12460125.2020.1840705.
  • Rezaei J, Nispeling T, Sarkis J, Tavasszy L. 2016. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J Cleaner Prod. 135:577–588. doi:10.1016/j.jclepro.2016.06.125.
  • Saaty TL. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol. 15(3):234–281. doi:10.1016/0022-2496(77)90033-5.
  • Saaty TL. 2008a. Decision making with the analytic hierarchy process. IJSSCI. 1(1):83–98. doi:10.1504/IJSSCI.2008.017590.
  • Saaty TL. 2008b. Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. Rev R Acad Cien Serie A Mat. 102(2):251–318. doi:10.1007/BF03191825.
  • Saaty TL, Vargas LG. 2006. Decision making with the analytic network process. Springer.
  • van de Kaa G, Fens T, Rezaei J. 2019. Residential grid storage technology battles: a multi-criteria analysis using BWM. Technol Anal Strateg Manage. 31(1):40–52. doi:10.1080/09537325.2018.1484441.
  • Wagner C, Miller S, Garibaldi JM, Anderson DT, Havens TC. 2015. From interval-valued data to general type-2 fuzzy sets. IEEE Trans Fuzzy Syst. 23(2):248–269. doi:10.1109/TFUZZ.2014.2310734.
  • Wang F. 2021. Preference degree of triangular fuzzy numbers and its application to multi-attribute group decision making. Expert Syst Appl. 178:114982. doi:10.1016/j.eswa.2021.114982.
  • Wang J, Peng J, Zhang H, Liu T, Chen X. 2015. An uncertain linguistic multi-criteria group decision-making method based on a cloud model. Group Decis Negot. 24(1):171–192. doi:10.1007/s10726-014-9385-7.
  • Wang J, Wu J, Wang J, Zhang H, Chen X. 2014. Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Inf Sci. 288:55–72. doi:10.1016/j.ins.2014.07.034.
  • Wang P, Xu X, Huang S, Cai C. 2018. A linguistic large group decision making method based on the cloud model. IEEE Trans Fuzzy Syst. 26(6):3314–3326. doi:10.1109/TFUZZ.2018.2822242.
  • Yu C, Shao Y, Wang K, Zhang L. 2019. A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Syst Appl. 121:1–17. doi:10.1016/j.eswa.2018.12.010.
  • Zadeh LA. 1975. Fuzzy logic and approximate reasoning. Synthese. 30(3–4):407–428. doi:10.1007/BF00485052.
  • Zhang H, Dong Y, Chiclana F, Yu S. 2019. Consensus efficiency in group decision making: a comprehensive comparative study and its optimal design. Eur J Oper Res. 275(2):580–598. doi:10.1016/j.ejor.2018.11.052.
  • Zhang Z, Gao J, Gao Y, Yu W. 2021. Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information. Expert Syst Appl. 168:114311. doi:10.1016/j.eswa.2020.114311.
  • Zhao D, Li C, Wang Q, Yuan J. 2020. Comprehensive evaluation of national electric power development based on cloud model and entropy method and TOPSIS: a case study in 11 countries. J Cleaner Prod. 277:123190. doi:10.1016/j.jclepro.2020.123190.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.