A group decision-making model for wastewater treatment plans selection based on intuitionistic fuzzy sets
Abstract
As the need for environmental protection and resource sustainability has increased in recent times, wastewater treatment has become increasingly important. In this paper, a group decision-making model for plans selection in wastewater treatment is proposed. In order to deal with uncertainties and multiple attributes in wastewater treatment, an intuitionistic fuzzy set is employed to evaluate wastewater treatment plans effectively. A distance measure is defined to obtain an objective weight measuring the expert’s judgment. More specifically, experts first use group decision-making on the various plans with an intuitionistic fuzzy set. Meanwhile, Due to the decision-makers psychological behavior, the prospect theory is applied. Next, the various plans are ranked by The Order of Preference by Similarity to Ideal Solution (TOPSIS) method and prospect theory. Finally, an illustrative example of wastewater treatment plans selection is used to verify the proposed model.
Keyword : wastewater treatment plans selection, group decision-making, intuitionistic fuzzy set, TOPSIS, prospect theory
How to Cite
Zhou, Z., Dou, Y., Zhang, X., Zhao, D., & Tan, Y. (2018). A group decision-making model for wastewater treatment plans selection based on intuitionistic fuzzy sets. Journal of Environmental Engineering and Landscape Management, 26(4), 251-260. https://doi.org/10.3846/jeelm.2018.6122
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets & Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
Barber, W. P., & Stuckey, D. C. (1999). The use of the anaerobic baffled reactor (ABR) for wastewater treatment: a review. Water Research, 33(7), 1559-1578. https://doi.org/10.1016/S0043-1354(98)00371-6
Chhipi-Shrestha, G., Hewage, K., & Sadiq, R. (2017). Fit-forpurpose wastewater treatment: Conceptualization to development of decision support tool (I). Science of the Total Environment, 607-608, 600-612. https://doi.org/10.1016/j.scitotenv.2017.06.269
Choudhury, P., Uday, U. S. P., Mahata, N., Tiwari, O., Ray, R., Bandyopadhyay, T., & Bhunia, B. (2017). Performance improvement of microbial fuel cells for waste water treatment along with value addition: A review on past achievements and recent perspectives. Renewable & Sustainable Energy Reviews, 79, 372-389. https://doi.org/10.1016/j.rser.2017.05.098
Dursun, M. (2016). A fuzzy approach for the assessment of wastewater treatment alternatives. Engineering Letters, 24(2), 231-236.
Fawcett, H. (1993). High-level dollars: low-level sense: a critique of present policy for the management of long-lived radioactive waste and discussion of an alternative approach, by A. Makhijani and Scott Saleska, A Report of the Institute for Energy and Environmental Reseal. Journal of Hazardous Materials, 33(3), 330-343. https://doi.org/10.1016/0304-3894(93)85087-U
Fisher, B. (2003). Fuzzy environmental decision-making: applications to air pollution. Atmospheric Environment, 37(14), 1865-1877. https://doi.org/10.1016/S1352-2310(03)00028-1
Gilcreas, F. W. (1966). Standard methods for the examination of water and waste water. American Journal of Public Health & the Nations Health, 56(3), 387-388. https://doi.org/10.2105/AJPH.56.3.387
Ghosh, P., Roy, T. K., & Majumder, C. (2016). Optimization of industrial wastewater treatment using intuitionistic fuzzy goal geometric programming problem. Fuzzy Information & Engineering, 8(3), 329-343. https://doi.org/10.1016/j.fiae.2016.09.002
Hadipour, A., Rajaee, T., Hadipour, V., & Seidirad, S. 2016. Multi-criteria decision-making model for wastewater reuse application: a case study from iran. Desalination & Water Treatment, 57(30), 13857-13864. https://doi.org/10.1080/19443994.2015.1060905
Hashemi, H., & Mousavi, S. M. (2013). A compromise ratio method with an application to water resources management: an intuitionistic fuzzy set. Water Resources Management, 27(7), 2029-2051. https://doi.org/10.1007/s11269-013-0271-x
Herrera-Viedma, E. (2015). Fuzzy sets and fuzzy logic in multicriteria decision making. The 50th anniversary of prof. Lotfi Zadeh᾽s theory: introduction. Technological & Economic Development of Economy, 21(5), 677-683. https://doi.org/10.3846/20294913.2015.1084956
Hwang, C. L., & Yoon, K. (1995). Multiple attribute decision making. Berlin, Heidelberg: Springer.
Jian, X., Li, Y., Jiang, Z., & Li, L. (2012). The research on technologic economics comprehensive appraisal of wastewater treatment in the paper making factory with intuitionistic fuzzy information. Advances in Information Sciences & Service Sciences, 4(4), 293-299. https://doi.org/10.4156/aiss.vol4.issue4.35
Kahyaoğlu-Koračin, J., Bassett, S. D., Mouat, D. A., & Gertler, A. W. (2009). Application of a scenario-based modeling system to evaluate the air quality impacts of future growth. Atmospheric Environment, 43(5), 1021-1028. https://doi.org/10.1016/j.atmosenv.2008.04.004
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. https://doi.org/10.2307/1914185
Karamouz, M., Rasoulnia, E., Zahmatkesh, Z., Olyaei, M. A., & Baghvand, A. (2016). Uncertainty-based flood resiliency evaluation of wastewater treatment plants. Journal of Hydroinformatics, 18(6), 990-1106. https://doi.org/10.2166/hydro.2016.084
Karsak, E. E., Sozer, S., & Alptekin, S. E. (2003). Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & Industrial Engineering, 44(1), 171-190. https://doi.org/10.1016/S0360-8352(02)00191-2
Khemiri, R., Elbedouimaktouf, K., Grabot, B., & Zouari, B. (2017). A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. International Journal of Production Research, 55(18), 5305-5329. https://doi.org/10.1080/00207543.2017.1308575
Khodadadi, M. R., Zolfani, S. H., Yazdani, M., & Zavadskas, E. K. (2017). A hybrid madm analysis in evaluating process of chemical wastewater purification regarding to advance oxidation processes. Journal of Environmental Engineering & Landscape Management, 25(3), 277-288.
Li, D. F., & Nan, J. X. (2013). Extension of the TOPSIS for multiattribute group decision making under Atanassov IFS Environments. International Journal of Fuzzy System Applications, 1(4), 47-61. https://doi.org/10.4018/ijfsa.2011100104
Liu, H. C., You, J. X., Lu, C., & Chen, Y. Z. (2015). Evaluating health-care waste treatment technologies using a hybrid multi-criteria decision-making model. Renewable & Sustainable Energy Reviews, 41, 932-942. https://doi.org/10.1016/j.rser.2014.08.061
Liu, Y., & Tay, J. H. (2004). State of the art of bio granulation technology for wastewater treatment. Biotechnology Advances, 22(7), 533-563. https://doi.org/10.1016/j.biotechadv.2004.05.001
Madadian, E., Amiri, L., & Abdoli, M. A. (2013). Application of analytic hierarchy process and multicriteria decision analysis on waste management: A case study in Iran. Environmental Progress & Sustainable Energy, 32(3), 810-817. https://doi.org/10.1002/ep.11695
Mardani, A., Jusoh, A., Zavadskas, E. K., Cavallaro, F., & Khalifah, Z. (2015). Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability, 7, 13947-13984. https://doi.org/10.3390/su71013947
Marin, L., Valls, A., Isern, D., Moreno, A., & Merigó, J. M. (2014). Induced unbalanced linguistic ordered weighted average and its application in multi-person decision making. The Scientific World Journal, 2014, ID 642165, 1-19. https://doi.org/10.1155/2014/642165
Maryam, M., Mohd Bakri, I., Ali, T., Latifah, A. M., Normala, H., & Jamal, G. D. (2017). Optimal selection of Iron and Steel wastewater treatment technology using integrated multicriteria decision-making techniques and fuzzy logic. Process Safety and Environmental Protection, 107, 54-68. https://doi.org/10.1016/j.psep.2017.01.016
Mehlawat, M. K., & Grover, N. (2018). Intuitionistic fuzzy multicriteria group decision making with an application to critical path selection. Annals of Operations Research, 269(1-2), 505-520. https://doi.org/10.1007/s10479-017-2477-4
Molinos-Senante, M., Gómez, T., Garrido-Baserba, M., Caballero, R., & Sala-Garrido, R. (2014). Assessing the sustainability of small wastewater treatment systems: A composite indicator approach. Science of the Total Environment, 497-498, 607-617. https://doi.org/10.1016/j.scitotenv.2014.08.026
Ren, J., & Liang, H. (2017). Multi-criteria group decision-making based sustainability measurement of wastewater treatment processes. Environmental Impact Assessment Review, 65, 91-99. https://doi.org/10.1016/j.eiar.2017.04.008
Ren, Z., Xu, Z., & Wang, H. (2017). Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method. Information Sciences, 388-389, 1-16. https://doi.org/10.1016/j.ins.2017.01.024
Rodríguez, A., Ortega, F., & Concepción, R. (2017). An intuitionistic method for the selection of a risk management approach to information technology projects. Information Sciences, 375, 202-218. https://doi.org/10.1016/j.ins.2016.09.053
Stenchly, K., Dao, J., Lompo, D. J., & Buerkert, A. (2017). Effects of waste water irrigation on soil properties and soil fauna of spinach fields in a West African urban vegetable production system. Environmental Pollution, 222, 58-63. https://doi.org/10.1016/j.envpol.2017.01.006
Tamura, H. (2005). Behavioral models for complex decision analysis. European Journal of Operational Research, 166(3), 655-665. https://doi.org/10.1016/j.ejor.2004.03.038
Tsai, J. C. C., Chen, V. C. P., Beck, M. B., & Chen, J. (2004). Stochastic dynamic programming formulation for a wastewater treatment decision-making framework. Annals of Operations Research, 132(1-4), 207-221. https://doi.org/10.1023/B:ANOR.0000045283.86576.62
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk & Uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574
Wibowo, S., & Grandhi, S. (2015). Application of the fuzzy approach for the selection of wastewater treatment technologies. In IEEE 10th Conference on Industrial Electronics and Applications (pp. 843-848). IEEE. https://doi.org/10.1109/ICIEA.2015.7334228
Wiedemann, P. M., & Femers, S. (1993). Public participation in waste management decision making: Analysis and management of conflicts. Journal of Hazardous Materials, 33(3), 355-368. https://doi.org/10.1016/0304-3894(93)85085-S
Xiao, Y., Yi, S., & Tang, Z. (2017). Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Science of the Total Environment, 599-600, 1034-1046. https://doi.org/10.1016/j.scitotenv.2017.04.218
Xing, Z., Xiong, W., & Liu, H. (2017). A Euclidean approach for ranking Atanassov intuitionistic fuzzy values. IEEE Transactions on Fuzzy Systems, 99, 1-1. https://doi.org/10.1109/TFUZZ.2017.2666219
Ye, J. (2017). Intuitionistic fuzzy hybrid arithmetic and geometric aggregation operators for the decision-making of mechanical design schemes. Applied Intelligence, 2, 1-9. https://doi.org/10.1007/s10489-017-0930-3
Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & MD Nor, K. (2016). Development of topsis method to solve complicated decision-making problems: an overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645-682. https://doi.org/10.1142/S0219622016300019
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological & Economic Development of Economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037
Zeng, S., Merigó, J. M., Palacios-Marqués, D., Jin, H., & Gu, F. (2016). Intuitionistic fuzzy induced ordered weighted averaging distance operator and its application to decision making. Journal of Intelligent & Fuzzy Systems, 32(1), 1-12.
Zeng, S., Mu, Z., & Baležentis, T. (2017). A novel aggregation method for pythagorean fuzzy multiple attribute group decision making. International Journal of Intelligent Systems, 33(3), 573-585. https://doi.org/10.1002/int.21953
Zhang, S., Wei, Z., Liu, W., Yao, L., Suo, W., Xing, J., Huang, B., Jin, D., & Wang, J. (2015). Indicators for environment health risk assessment in the Jiangsu province of china. International Journal of Environmental Research & Public Health, 12(9), 11012-11024. https://doi.org/10.3390/ijerph120911012
Zolfani, S. H., Maknoon, R., & Zavadskas, E. K. (2016). An introduction to prospective multiple attribute decision making (PMADM). Technological & Economic Development of Economy, 22(2), 309-326. https://doi.org/10.3846/20294913.2016.1150363
Arshad, A., Iqbal, J., Siddiq, M., Ali, M., Ali, A., Shabbir, H., Usama, B. N., & ShahbazSaleem, M. (2017). Solar light triggered catalytic performance of graphene-CuO nanocomposite for waste water treatment. Ceramics International, 43(13), 10654-10660. https://doi.org/10.1016/j.ceramint.2017.03.165
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets & Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
Barber, W. P., & Stuckey, D. C. (1999). The use of the anaerobic baffled reactor (ABR) for wastewater treatment: a review. Water Research, 33(7), 1559-1578. https://doi.org/10.1016/S0043-1354(98)00371-6
Chhipi-Shrestha, G., Hewage, K., & Sadiq, R. (2017). Fit-forpurpose wastewater treatment: Conceptualization to development of decision support tool (I). Science of the Total Environment, 607-608, 600-612. https://doi.org/10.1016/j.scitotenv.2017.06.269
Choudhury, P., Uday, U. S. P., Mahata, N., Tiwari, O., Ray, R., Bandyopadhyay, T., & Bhunia, B. (2017). Performance improvement of microbial fuel cells for waste water treatment along with value addition: A review on past achievements and recent perspectives. Renewable & Sustainable Energy Reviews, 79, 372-389. https://doi.org/10.1016/j.rser.2017.05.098
Dursun, M. (2016). A fuzzy approach for the assessment of wastewater treatment alternatives. Engineering Letters, 24(2), 231-236.
Fawcett, H. (1993). High-level dollars: low-level sense: a critique of present policy for the management of long-lived radioactive waste and discussion of an alternative approach, by A. Makhijani and Scott Saleska, A Report of the Institute for Energy and Environmental Reseal. Journal of Hazardous Materials, 33(3), 330-343. https://doi.org/10.1016/0304-3894(93)85087-U
Fisher, B. (2003). Fuzzy environmental decision-making: applications to air pollution. Atmospheric Environment, 37(14), 1865-1877. https://doi.org/10.1016/S1352-2310(03)00028-1
Gilcreas, F. W. (1966). Standard methods for the examination of water and waste water. American Journal of Public Health & the Nations Health, 56(3), 387-388. https://doi.org/10.2105/AJPH.56.3.387
Ghosh, P., Roy, T. K., & Majumder, C. (2016). Optimization of industrial wastewater treatment using intuitionistic fuzzy goal geometric programming problem. Fuzzy Information & Engineering, 8(3), 329-343. https://doi.org/10.1016/j.fiae.2016.09.002
Hadipour, A., Rajaee, T., Hadipour, V., & Seidirad, S. 2016. Multi-criteria decision-making model for wastewater reuse application: a case study from iran. Desalination & Water Treatment, 57(30), 13857-13864. https://doi.org/10.1080/19443994.2015.1060905
Hashemi, H., & Mousavi, S. M. (2013). A compromise ratio method with an application to water resources management: an intuitionistic fuzzy set. Water Resources Management, 27(7), 2029-2051. https://doi.org/10.1007/s11269-013-0271-x
Herrera-Viedma, E. (2015). Fuzzy sets and fuzzy logic in multicriteria decision making. The 50th anniversary of prof. Lotfi Zadeh᾽s theory: introduction. Technological & Economic Development of Economy, 21(5), 677-683. https://doi.org/10.3846/20294913.2015.1084956
Hwang, C. L., & Yoon, K. (1995). Multiple attribute decision making. Berlin, Heidelberg: Springer.
Jian, X., Li, Y., Jiang, Z., & Li, L. (2012). The research on technologic economics comprehensive appraisal of wastewater treatment in the paper making factory with intuitionistic fuzzy information. Advances in Information Sciences & Service Sciences, 4(4), 293-299. https://doi.org/10.4156/aiss.vol4.issue4.35
Kahyaoğlu-Koračin, J., Bassett, S. D., Mouat, D. A., & Gertler, A. W. (2009). Application of a scenario-based modeling system to evaluate the air quality impacts of future growth. Atmospheric Environment, 43(5), 1021-1028. https://doi.org/10.1016/j.atmosenv.2008.04.004
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. https://doi.org/10.2307/1914185
Karamouz, M., Rasoulnia, E., Zahmatkesh, Z., Olyaei, M. A., & Baghvand, A. (2016). Uncertainty-based flood resiliency evaluation of wastewater treatment plants. Journal of Hydroinformatics, 18(6), 990-1106. https://doi.org/10.2166/hydro.2016.084
Karsak, E. E., Sozer, S., & Alptekin, S. E. (2003). Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & Industrial Engineering, 44(1), 171-190. https://doi.org/10.1016/S0360-8352(02)00191-2
Khemiri, R., Elbedouimaktouf, K., Grabot, B., & Zouari, B. (2017). A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. International Journal of Production Research, 55(18), 5305-5329. https://doi.org/10.1080/00207543.2017.1308575
Khodadadi, M. R., Zolfani, S. H., Yazdani, M., & Zavadskas, E. K. (2017). A hybrid madm analysis in evaluating process of chemical wastewater purification regarding to advance oxidation processes. Journal of Environmental Engineering & Landscape Management, 25(3), 277-288.
Li, D. F., & Nan, J. X. (2013). Extension of the TOPSIS for multiattribute group decision making under Atanassov IFS Environments. International Journal of Fuzzy System Applications, 1(4), 47-61. https://doi.org/10.4018/ijfsa.2011100104
Liu, H. C., You, J. X., Lu, C., & Chen, Y. Z. (2015). Evaluating health-care waste treatment technologies using a hybrid multi-criteria decision-making model. Renewable & Sustainable Energy Reviews, 41, 932-942. https://doi.org/10.1016/j.rser.2014.08.061
Liu, Y., & Tay, J. H. (2004). State of the art of bio granulation technology for wastewater treatment. Biotechnology Advances, 22(7), 533-563. https://doi.org/10.1016/j.biotechadv.2004.05.001
Madadian, E., Amiri, L., & Abdoli, M. A. (2013). Application of analytic hierarchy process and multicriteria decision analysis on waste management: A case study in Iran. Environmental Progress & Sustainable Energy, 32(3), 810-817. https://doi.org/10.1002/ep.11695
Mardani, A., Jusoh, A., Zavadskas, E. K., Cavallaro, F., & Khalifah, Z. (2015). Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability, 7, 13947-13984. https://doi.org/10.3390/su71013947
Marin, L., Valls, A., Isern, D., Moreno, A., & Merigó, J. M. (2014). Induced unbalanced linguistic ordered weighted average and its application in multi-person decision making. The Scientific World Journal, 2014, ID 642165, 1-19. https://doi.org/10.1155/2014/642165
Maryam, M., Mohd Bakri, I., Ali, T., Latifah, A. M., Normala, H., & Jamal, G. D. (2017). Optimal selection of Iron and Steel wastewater treatment technology using integrated multicriteria decision-making techniques and fuzzy logic. Process Safety and Environmental Protection, 107, 54-68. https://doi.org/10.1016/j.psep.2017.01.016
Mehlawat, M. K., & Grover, N. (2018). Intuitionistic fuzzy multicriteria group decision making with an application to critical path selection. Annals of Operations Research, 269(1-2), 505-520. https://doi.org/10.1007/s10479-017-2477-4
Molinos-Senante, M., Gómez, T., Garrido-Baserba, M., Caballero, R., & Sala-Garrido, R. (2014). Assessing the sustainability of small wastewater treatment systems: A composite indicator approach. Science of the Total Environment, 497-498, 607-617. https://doi.org/10.1016/j.scitotenv.2014.08.026
Ren, J., & Liang, H. (2017). Multi-criteria group decision-making based sustainability measurement of wastewater treatment processes. Environmental Impact Assessment Review, 65, 91-99. https://doi.org/10.1016/j.eiar.2017.04.008
Ren, Z., Xu, Z., & Wang, H. (2017). Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method. Information Sciences, 388-389, 1-16. https://doi.org/10.1016/j.ins.2017.01.024
Rodríguez, A., Ortega, F., & Concepción, R. (2017). An intuitionistic method for the selection of a risk management approach to information technology projects. Information Sciences, 375, 202-218. https://doi.org/10.1016/j.ins.2016.09.053
Stenchly, K., Dao, J., Lompo, D. J., & Buerkert, A. (2017). Effects of waste water irrigation on soil properties and soil fauna of spinach fields in a West African urban vegetable production system. Environmental Pollution, 222, 58-63. https://doi.org/10.1016/j.envpol.2017.01.006
Tamura, H. (2005). Behavioral models for complex decision analysis. European Journal of Operational Research, 166(3), 655-665. https://doi.org/10.1016/j.ejor.2004.03.038
Tsai, J. C. C., Chen, V. C. P., Beck, M. B., & Chen, J. (2004). Stochastic dynamic programming formulation for a wastewater treatment decision-making framework. Annals of Operations Research, 132(1-4), 207-221. https://doi.org/10.1023/B:ANOR.0000045283.86576.62
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk & Uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574
Wibowo, S., & Grandhi, S. (2015). Application of the fuzzy approach for the selection of wastewater treatment technologies. In IEEE 10th Conference on Industrial Electronics and Applications (pp. 843-848). IEEE. https://doi.org/10.1109/ICIEA.2015.7334228
Wiedemann, P. M., & Femers, S. (1993). Public participation in waste management decision making: Analysis and management of conflicts. Journal of Hazardous Materials, 33(3), 355-368. https://doi.org/10.1016/0304-3894(93)85085-S
Xiao, Y., Yi, S., & Tang, Z. (2017). Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Science of the Total Environment, 599-600, 1034-1046. https://doi.org/10.1016/j.scitotenv.2017.04.218
Xing, Z., Xiong, W., & Liu, H. (2017). A Euclidean approach for ranking Atanassov intuitionistic fuzzy values. IEEE Transactions on Fuzzy Systems, 99, 1-1. https://doi.org/10.1109/TFUZZ.2017.2666219
Ye, J. (2017). Intuitionistic fuzzy hybrid arithmetic and geometric aggregation operators for the decision-making of mechanical design schemes. Applied Intelligence, 2, 1-9. https://doi.org/10.1007/s10489-017-0930-3
Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & MD Nor, K. (2016). Development of topsis method to solve complicated decision-making problems: an overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645-682. https://doi.org/10.1142/S0219622016300019
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological & Economic Development of Economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037
Zeng, S., Merigó, J. M., Palacios-Marqués, D., Jin, H., & Gu, F. (2016). Intuitionistic fuzzy induced ordered weighted averaging distance operator and its application to decision making. Journal of Intelligent & Fuzzy Systems, 32(1), 1-12.
Zeng, S., Mu, Z., & Baležentis, T. (2017). A novel aggregation method for pythagorean fuzzy multiple attribute group decision making. International Journal of Intelligent Systems, 33(3), 573-585. https://doi.org/10.1002/int.21953
Zhang, S., Wei, Z., Liu, W., Yao, L., Suo, W., Xing, J., Huang, B., Jin, D., & Wang, J. (2015). Indicators for environment health risk assessment in the Jiangsu province of china. International Journal of Environmental Research & Public Health, 12(9), 11012-11024. https://doi.org/10.3390/ijerph120911012
Zolfani, S. H., Maknoon, R., & Zavadskas, E. K. (2016). An introduction to prospective multiple attribute decision making (PMADM). Technological & Economic Development of Economy, 22(2), 309-326. https://doi.org/10.3846/20294913.2016.1150363