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Articles

Heterogeneous group decision making with thermodynamical parameters

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Pages 6601-6625 | Received 30 Apr 2021, Accepted 07 Mar 2022, Published online: 07 Apr 2022

References

  • Atanassov, K. (1983). Intuitionistic fuzzy sets. Sofia. In V. Sgurev (Ed.), VII ITKR's Session, Sofia.
  • Atanassov, K. (1986). Intuitionistic fuzzy set. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
  • Bateman, I. J., Harwood, A. R., Mace, G. M., Watson, R. T., Abson, D. J., Andrews, B., Binner, A., Crowe, A., Day, B. H., Dugdale, S., Fezzi, C., Foden, J., Hadley, D., Haines-Young, R., Hulme, M., Kontoleon, A., Lovett, A. A., Munday, P., Pascual, U., … Termansen, M. (2013). Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science (New York, N.Y.), 341(6141), 45–50. https://doi.org/10.1126/science.1234379
  • Buyukozkan, G., & CifcI, G. (2011). A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Computers in Industry, 62, 164–174.
  • Buyukozkan, G., & Guleryuz, S. (2016). An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. International Journal of Production Economics, 182, 435–448.
  • Charles, L., & Herbert, K. (1980). Thermal physics. Macmillan.
  • Cheng, S. W. (2010). New energy and low carbon economy. Business Review, 22, 4–8.
  • Coban, V., & Onar, S. C. (2021). Emergency decision making problem of power cut in Turkey using Pythagorean fuzzy thermodynamic approach with prospect theory [Paper presentation].Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020 Conference (Vol. 1197, pp. 415–422).
  • Farahani, R. Z., & Asgari, N. (2007). Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research, 176(3), 1839–1858. https://doi.org/10.1016/j.ejor.2005.10.039
  • Gomes, L., & Lima, M. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Science, 16, 113–127.
  • Herrera, F., & Martinez, L. (2000). An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 08(05), 539–562. https://doi.org/10.1142/S0218488500000381
  • Humphreys, P., McIvor, R., & Chan, F. (2003). Using case-based reasoning to evaluate supplier environmental management performance. Expert Systems with Applications, 25(2), 141–153. https://doi.org/10.1016/S0957-4174(03)00042-3
  • Johansson-Stenman, O. (2018). Animal welfare and social decisions: Is it time to take Bentham seriously. Ecological Economics, 145, 90–103. https://doi.org/10.1016/j.ecolecon.2017.08.019
  • Kahneman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8, 637–666.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. [Database] https://doi.org/10.2307/1914185
  • Kaya, I., Colak, M., & Terzi, F. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Reviews, 24, 207–228. https://doi.org/10.1016/j.esr.2019.03.003
  • Lee, A. H. I., Kang, H. Y., Hsu, C. F., & Hung, H. C. (2009). A green supplier selection model for high-tech industry. Expert Systems with Applications, 36(4), 7917–7927. https://doi.org/10.1016/j.eswa.2008.11.052
  • Leiser, D., & Azar, O. H. (2008). Behavioral economics and decision making: Applying insights from psychology to understand how people make economic decisions. Journal of Economic Psychology, 29(5), 613–618. https://doi.org/10.1016/j.joep.2008.08.001
  • Li, D. F., Huang, Z. G., & Chen, G. H. (2010). A systematic approach to heterogeneous multiattribute group decision making. Computers & Industrial Engineering, 59(4), 561–572. https://doi.org/10.1016/j.cie.2010.06.015
  • Liang, Y. Y., Qin, J. D., Martínez, L., & Liu, J. (2020). A heterogeneous QUALIFLEX method with criteria interaction for multi-criteria group decision making. Information Sciences, 512, 1481–1502. https://doi.org/10.1016/j.ins.2019.10.044
  • Liao, H. C., & Xu, Z. S. (2015). Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for HFLTSs and their application in qualitative decision making. Expert Systems with Applications, 42(12), 5328–5336. https://doi.org/10.1016/j.eswa.2015.02.017
  • Liao, H. C., Wu, D., Huang, Y. L., Ren, P. J., Xu, Z. S., & Verma, M. (2018). Green logistic provider selection with a hesitant Fuzzy linguistic thermodynamic method integrating cumulative prospect theory and PROMETHEE. Sustainability, 10(4), 1291. https://doi.org/10.3390/su10041291
  • Liao, H. C., Xu, Z. S., & Zeng, X.-J. (2014). Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 271, 125–142. https://doi.org/10.1016/j.ins.2014.02.125
  • Liu, M., Milke, M. W., Heiler, D., & Giovinazzi, S. (2018). Postearthquake decision making on sewer recovery and the roles of damage and repair data: Case study of Christchurch, New Zealand. Journal of Infrastructure Systems, 24(1), 05017007.1–05017007.11. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000406
  • Lourenzutti, R., Krohling, R. A., & Reformat, M. Z. (2017). Choquet based TOPSIS and TODIM for dynamic and heterogeneous decision making with criteria interaction. Information Sciences, 408, 41–69. https://doi.org/10.1016/j.ins.2017.04.037
  • Lu, L. Y. Y., Wu, C. H., & Kuo, T. C. (2007). Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. International Journal of Production Research, 45(18–19), 4317–4331. https://doi.org/10.1080/00207540701472694
  • Mendoza, R. L. (2018). Bringing the patient back in: Behavioral decision-making and choice in medical economics. Journal of Medical Economics, 21(4), 313–317. https://doi.org/10.1080/13696998.2018.1434532
  • Mori, Y., Mizutani, Y., Kang, J. D., & Idota, H. (2018). Upgrade decision-making for earthquake-vulnerable wooden houses using probabilistic damage index functions. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(1), 04017037. https://doi.org/10.1061/AJRUA6.0000945
  • Noci, G. (1997). Designing “green” vendor rating systems for the assessment of a suppliers environmental performance. European Journal of Purchasing & Supply Management, 3(2), 103–114. https://doi.org/10.1016/S0969-7012(96)00021-4
  • Peng, D. H., Gao, C. Y., & Wu, L. X. (2011). TOPSIS-based multi-criteria group decision making under heterogeneous information setting. International Journal of Computational Intelligence Systems, 378–379, 525–530.
  • Perrot, P. (1998). A to Z of thermodynamics. Oxford University Press.
  • Rani, P., Mishra, A. R., Mardani, A., Cavallaro, F., Alrasheedi, M., & Alrashidi, A. (2020). A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection. Journal of Cleaner Production, 257, 120352. https://doi.org/10.1016/j.jclepro.2020.120352
  • Ren, P. J., Xu, Z. S., & Hao, Z. N. (2017a). Hesitant fuzzy thermodynamic method for emergency decision making based on prospect theory. IEEE Transactions on Cybernetics, 47(9), 2531–2543. https://doi.org/10.1109/TCYB.2016.2638498
  • Ren, P. J., Xu, Z. S., Liao, H. C., & Zeng, X.-J. (2017b). A thermodynamic method of intuitionistic fuzzy MCDM to assist the hierarchical medical system in China. Information Sciences, 420, 490–504. https://doi.org/10.1016/j.ins.2017.08.070
  • Ren, P. J., Xu, Z. S., Wang, X. X., & Zeng, X. J. (2021). Group decision making with hesitant fuzzy linguistic preference relations based on modified extent measurement. Expert Systems with Applications, 171, 114235. https://doi.org/10.1016/j.eswa.2020.114235
  • Rodríguez, R. M., Martínez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic terms sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076
  • Salinas, G. (2017). The decision to attack: Military and intelligence cyber decision-making. Perspectives on Politics, 15(1), 283–284. https://doi.org/10.1017/S1537592716005119
  • Sánchez-Lozano, J. M., Serna, J., & Dolón-Payán, A. (2015). Evaluating military training aircrafts through the combination of multi-criteria decision making processes with fuzzy logic. A case study in the Spanish Air Force Academy. Aerospace Science and Technology, 42, 58–65. https://doi.org/10.1016/j.ast.2014.12.028
  • Santoyo-Castelazo, E., & Azapagic, A. (2014). Sustainability assessment of energy systems: Integrating environmental, economic and social aspects. Journal of Cleaner Production, 80, 119–138. https://doi.org/10.1016/j.jclepro.2014.05.061
  • Strantzali, E., & Aravossis, K. (2016). Decision making in renewable energy investments: A review. Renewable and Sustainable Energy Reviews, 55, 885–898. https://doi.org/10.1016/j.rser.2015.11.021
  • Tan, R. P., Zhang, W. D., Yang, L. H., & Chen, S. Q. (2019). Multi-attribute decision-making method based on prospect theory in heterogeneous information environment and its application in typhoon disaster assessment. International Journal of Computational Intelligence Systems, 12, 881–896.
  • Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25, 529–539. https://doi.org/10.1002/int.20418
  • Verma, M., & Rajasankar, J. (2017). A thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource selection. Applied Soft Computing, 52, 323–332. https://doi.org/10.1016/j.asoc.2016.10.033
  • Verma, M., Rajasankar, J., Anandavalli, N., Prakash, A., & Iyer, N. R. (2015). Fuzzy similarity approach for ranking and health assessment of towers based on visual inspection. Advances in Structural Engineering, 18(9), 1399–1414. https://doi.org/10.1260/1369-4332.18.9.1399
  • Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.
  • Wang, M. W., Liang, D. C., Xu, Z. S., & Ye, D. J. (2020). The evaluation of mobile health apps: A psychological perception-based probabilistic linguistic belief thermodynamic multiple attribute decision making method. Journal of the Operational Research Society, 8, 1–15.
  • Wang, X. X. (2017). Review of researches on green supplier selection. Economic Research Guide, 4, 194–196.
  • Wessel, J., Bidwell, R., & Pullen, E. J. (1984). Decision making in coastal engineering. Water Science and Technology, 16(3–4), 759–765. https://doi.org/10.2166/wst.1984.0109
  • Xu, J., Wan, S. P., & Dong, J. Y. (2016). Aggregating decision information into Atanassov’s intuitionistic fuzzynumbers for heterogeneous multi-attribute group decision making. Applied Soft Computing, 41, 331–351. https://doi.org/10.1016/j.asoc.2015.12.045
  • Xu, Z. S. (2005). Deviation measures of linguistic preference relations in group decision making. Omega, 33(3), 249–254. https://doi.org/10.1016/j.omega.2004.04.008
  • Xu, Z. S. (2007). Multiple-attribute group decision making with different formats of preference information on attributes. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37, 1500–1511.
  • Yue, C. (2019). A normalized projection-based group decision-making method with heterogeneous decision information and application to software development effort assessment. Applied Intelligence, 49(10), 3587–3605. https://doi.org/10.1007/s10489-019-01473-w
  • Zadeh, L. A. (1965). Fuzzy sets. Information Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-Part I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
  • Zhang, B. W., Dong, Y. C., & Herrera-Viedma, E. (2019). Group decision making with heterogeneous preference structures: An automatic mechanism to support consensus reaching. Group Decision and Negotiation, 28(3), 585–617. https://doi.org/10.1007/s10726-018-09609-y
  • Zhang, H. H., Kou, G., & Peng, Y. (2019). Soft consensus cost models for group decision making and economic interpretations. European Journal of Operational Research, 277(3), 964–980. https://doi.org/10.1016/j.ejor.2019.03.009
  • Zhang, Z. M., & Wu, C. (2014). On the use of multiplicative consistency in hesitant fuzzy linguistic preference relations. Knowledge-Based Systems, 72, 13–27. https://doi.org/10.1016/j.knosys.2014.08.026
  • Zheng, J., Wang, Y. M., & Zhang, K. (2020). Solution of heterogeneous multi-attribute case-based decision making problems by using method based on TODIM. Soft Computing, 24(10), 7081–7091. https://doi.org/10.1007/s00500-020-04844-5
  • Zhu, B., & Xu, Z. S. (2014). Consistency measures for hesitant fuzzy linguistic preference relations. IEEE Transactions on Fuzzy Systems, 22(1), 35–45. https://doi.org/10.1109/TFUZZ.2013.2245136