Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
Abstract
New energy vehicles can improve the environmental pollution and thus benefit people’s healthy life. As a core component of new energy vehicles, batteries play a crucial role in the performance of new energy vehicles. There are many factors to be considered when selecting the battery for a new energy vehicle, so it can be regarded as a MCDM problem. This study builds a useful model by combining the PLTS with the UTASTAR method. Firstly, to represent the uncertain and fuzzy information of experts, we use the PLTSs to accurately express the linguistic information of experts. Given that the weights of criteria are often different and there are some preferences for criteria among experts, we use the BWM to determine the weights of criteria, which can deal with hesitant information and make the result suitable for experts’ preferences. The method proposed in this study can sort all alternatives based on a small amount of data. To show its applicability, we implement the method in the selection of new energy vehicle battery suppliers. Comparative analysis and discussions are made to verify the effectiveness of the method.
First published online 10 May 2021
Keyword : new energy vehicle, battery supplier development, best-worst method, probabilistic linguistic term set, UTASTAR
How to Cite
Liao, H., Liu, Z., Banaitis, A., Zavadskas, E. K., & Zhou, X. (2022). Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method. Transport, 37(2), 121–136. https://doi.org/10.3846/transport.2021.14710
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Asadabadi, M. R.; Chang, E.; Zwikael, O.; Saberi, M.; Sharpe, K. 2020. Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method, Fuzzy Sets and Systems 383: 127–145. https://doi.org/10.1016/j.fss.2019.06.017
Athawale, V. M.; Kumar, R.; Chakraborty, S. 2011. Decision making for material selection using the UTA method, The International Journal of Advanced Manufacturing Technology 57(1–4): 11. https://doi.org/10.1007/s00170-011-3293-7
Beiragh, R. G.; Alizadeh, R.; Kaleibari, S. S.; Cavallaro, F.; Hashemkhani Zolfani, S.; Bausys, R.; Mardani, A. 2020. An integrated multi-criteria decision making model for sustainability performance assessment for insurance companies, Sustainability 12(3): 789. https://doi.org/10.3390/su12030789
Carreno, M.; Ge, Y.-E.; Borthwick, S. 2014. Could green taxation measures help incentivise future Chinese car drivers to purchase low emission vehicles?, Transport 29(3): 260–268. https://doi.org/10.3846/16484142.2014.913261
Chen, K.; Zhao, F.; Hao, H.; Liu, Z. 2019. Selection of lithiumion battery technologies for electric vehicles under China’s new energy vehicle credit regulation, Energy Procedia 158: 3038–3044. https://doi.org/10.1016/j.egypro.2019.01.987
Demesouka, O. E.; Anagnostopoulos, K. P.; Siskos, E. 2019. Spatial multicriteria decision support for robust land-use suitability: the case of landfill site selection in Northeastern Greece, European Journal of Operational Research 272(2): 574–586. https://doi.org/10.1016/j.ejor.2018.07.005
Diouf, B.; Pode, R. 2015. Potential of lithium-ion batteries in renewable energy, Renewable Energy 76: 375–380. https://doi.org/10.1016/j.renene.2014.11.058
Gong, H.; Wang, M. Q.; Wang, H. 2013. New energy vehicles in China: policies, demonstration, and progress, Mitigation and Adaptation Strategies for Global Change 18(2): 207–228. https://doi.org/10.1007/s11027-012-9358-6
Grigoroudis, E.; Zopounidis, C. 2012. Developing an employee evaluation management system: the case of a healthcare organization, Operational Research 12(1): 83–106. https://doi.org/10.1007/s12351-011-0103-9
Gruca, A.; Sikora, M. 2013. Rule based functional description of genes – estimation of the multicriteria rule interestingness measure by the UTA method, Biocybernetics and Biomedical Engineering 33(4): 222–234. https://doi.org/10.1016/j.bbe.2013.09.005
Haider, H.; Singh, P.; Ali, W.; Tesfamariam, S.; Sadiq, R. 2015. Sustainability evaluation of surface water quality management options in developing countries: multicriteria analysis using fuzzy UTASTAR method, Water Resources Management 29(8): 2987–3013. https://doi.org/10.1007/s11269-015-0982-2
Hruška, R.; Průša, P.; Babić, D. 2014. The use of AHP method for selection of supplier, Transport 29(2): 195–203. https://doi.org/10.3846/16484142.2014.930928
Ishizaka, A.; Resce, G. 2021. Best-worst PROMETHEE method for evaluating school performance in the OECD’s PISA project, Socio-Economic Planning Sciences 73: 100799. https://doi.org/10.1016/j.seps.2020.100799
Jacquet-Lagreze, E.; Siskos, J. 1982. Assessing a set of additive utility functions for multicriteria decision-making, the UTA method, European Journal of Operational Research 10(2): 151–164. https://doi.org/10.1016/0377-2217(82)90155-2
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Kaleibari, S. S.; Beiragh, R. G.; Alizadeh, R.; Solimanpur, M. 2016. A framework for performance evaluation of energy supply chain by a compatible network data envelopment analysis model, Scientia Iranica: Transactions E: Industrial Engineering 23(4): 1904–1917. https://doi.org/10.24200/sci.2016.3936
Li, J.; Ku, Y.; Liu, C.; Zhou, Y. 2020. Dual credit policy: promoting new energy vehicles with battery recycling in a competitive environment?, Journal of Cleaner Production 243: 118456. https://doi.org/10.1016/j.jclepro.2019.118456
Li, L.; Jing, X. 2019. Analysis of tax policy for promoting the development of China’s new energy vehicles industry, Advances in Social Science, Education and Humanities Research 328: 315–320. https://doi.org/10.2991/ichssd-19.2019.63
Liao, H.; Mi, X.; Xu, Z. 2020. A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions, Fuzzy Optimization and Decision Making 19(1): 81–134. https://doi.org/10.1007/s10700-019-09309-5
Liao, H.; Xu, Z.; Herrera-Viedma, E.; Herrera, F. 2018. Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey, International Journal of Fuzzy Systems 20(7): 2084–2110. https://doi.org/10.1007/s40815-017-0432-9
Liu, Y.; Kokko, A. 2013. Who does what in China’s new energy vehicle industry?, Energy Policy 57: 21–29. https://doi.org/10.1016/j.enpol.2012.05.046
Makui, A.; Momeni, M. 2012. Using CSW weight’s in UTASTAR method, Decision Science Letters 1: 39–46. https://doi.org/10.5267/j.dsl.2012.06.001
Mi, X.; Liao, H.; Wu, X.; Xu, Z. 2020. Probabilistic linguistic information fusion: a survey on aggregation operators in terms of principles, definitions, classifications, applications, and challenges, International Journal of Intelligent Systems 35(3): 529–556. https://doi.org/10.1002/int.22216
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. https://doi.org/10.1016/j.omega.2019.01.009
Nian, V.; Chou, S. K.; Su, B.; Bauly, J. 2014. Life cycle analysis on carbon emissions from power generation – the nuclear energy example, Applied Energy 118: 68–82. https://doi.org/10.1016/j.apenergy.2013.12.015
Nikas, A.; Doukas, H.; Siskos, E.; Psarras, J. 2018. International cooperation for clean electricity: a UTASTAR application in energy policy, in N. Matsatsinis, E. Grigoroudis (Eds.). Preference Disaggregation in Multiple Criteria Decision Analysis. Multiple Criteria Decision Making, 163–186. https://doi.org/10.1007/978-3-319-90599-0_8
Pang, Q.; Wang, H.; Xu, Z. 2016. Probabilistic linguistic term sets in multi-attribute group decision making, Information Sciences 369: 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Papapostolou, A.; Karakosta, C.; Nikas, A.; Psarras, J. 2017. Exploring opportunities and risks for RES-E deployment under cooperation mechanisms between EU and Western Balkans: a multi-criteria assessment, Renewable and Sustainable Energy Reviews 80: 519–530. https://doi.org/10.1016/j.rser.2017.05.190
Patiniotakis, I.; Apostolou, D.; Mentzas, G. 2011. Fuzzy UTASTAR: a method for discovering utility functions from fuzzy data, Expert Systems with Applications 38(12): 15463–15474. https://doi.org/10.1016/j.eswa.2011.06.014
Rezaei, J. 2015. Best-worst multi-criteria decision-making method, Omega 53: 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Saaty, T. L. 1977. A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology 15(3): 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Sims, R. E. H.; Rogner, H.-H.; Gregory, K. 2003. Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation, Energy Policy 31(13): 1315–1326. https://doi.org/10.1016/S0301-4215(02)00192-1
Siskos, Y.; Yannacopoulos, D. 1985. UTASTAR: an ordinal regression method for building additive value functions, Investigação Operacional 5(1): 39–53.
Sola, A. V. H.; Mota, C. M. D. M. 2012. A multi-attribute decision model for portfolio selection aiming to replace technologies in industrial motor systems, Energy Conversion and Management 57: 97–106. https://doi.org/10.1016/j.enconman.2011.12.013
Stević, Ž.; Vasiljević, M.; Puška, A.; Tanackov, I.; Junevičius, R.; Vesković, S. 2019. Evaluation of suppliers under uncertainty: a multiphase approach based on fuzzy AHP and fuzzy EDAS, Transport 34(1): 52–66. https://doi.org/10.3846/transport.2019.7275
Touni, Z.; Makui, A.; Mohammadi, E. 2019. A MCDM-based approach using UTA-STRAR method to discover behavioral aspects in stock selection problem, International Journal of Industrial Engineering & Production Research 30(1): 93–103. https://doi.org/10.22068/ijiepr.30.1.93
Van de Kaa, G.; Rezaei, J.; Taebi, B.; Van de Poel, I.; Kizhakenath, A. 2020. How to weigh values in value sensitive design: a best worst method approach for the case of smart metering, Science and Engineering Ethics 26(1): 475–494. https://doi.org/10.1007/s11948-019-00105-3
Walter, B.; Pietrzak, B. 2005. Multi-criteria detection of bad smells in code with UTA method, Lecture Notes in Computer Science 3556: 154–161. https://doi.org/10.1007/11499053_18
Whittington, H. W. 2002. Electricity generation: options for reduction in carbon emissions, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 360(1797): 1653–1668. https://doi.org/10.1098/rsta.2002.1025
Wu, X.; Liao, H.; Xu, Z.; Hafezalkotob, A.; Herrera, F. 2018. Probabilistic linguistic MULTIMOORA: a multicriteria decision making method based on the probabilistic linguistic expectation function and the improved Borda rule, IEEE Transactions on Fuzzy Systems 26(6): 3688–3702. https://doi.org/10.1109/TFUZZ.2018.2843330
Xiong, W.-T.; Cheng, J. 2016. A weighted UTASTAR method for the multiple criteria decision making with interval numbers, Advances in Economics, Business and Management Research 10: 191–195. https://doi.org/10.2991/msmi-16.2016.46
Yuan, X.; Liu, X.; Zuo, J. 2015. The development of new energy vehicles for a sustainable future: a review, Renewable and Sustainable Energy Reviews 42: 298–305. https://doi.org/10.1016/j.rser.2014.10.016
Yuan, H.; Jiang, Y. 2019. Research on key influencing factors of China’s new energy automobile industry development, Advances in Economics, Business and Management Research 91: 676–681. https://doi.org/10.2991/edmi-19.2019.117
Zadeh, L. A. 1965. Fuzzy sets, Information and 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 – I, Information Sciences 8(3): 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zadeh, L. A. 2012. Computing with Words. Springer. 142 p. https://doi.org/10.1007/978-3-642-27473-2
Zhang, C.; Luo, L.; Liao, H.; Mardani, A.; Streimikiene, D.; Al-Barakati, A. 2020. A priority-based intuitionistic multiplicative UTASTAR method and its application in low-carbon tourism destination selection, Applied Soft Computing 88: 106026. https://doi.org/10.1016/j.asoc.2019.106026
Alizadeh, R.; Majidpour, M.; Maknoon, R.; Salimi, J. 2015. Iranian energy and climate policies adaptation to the Kyoto protocol, International Journal of Environmental Research 9(3): 853–864. https://doi.org/10.22059/ijer.2015.972
Alizadeh, R.; Soltanisehat, L.; Lund, P. D.; Zamanisabzi, H. 2020. Improving renewable energy policy planning and decisionmaking through a hybrid MCDM method, Energy Policy 137: 111174. https://doi.org/10.1016/j.enpol.2019.111174
Asadabadi, M. R.; Chang, E.; Zwikael, O.; Saberi, M.; Sharpe, K. 2020. Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method, Fuzzy Sets and Systems 383: 127–145. https://doi.org/10.1016/j.fss.2019.06.017
Athawale, V. M.; Kumar, R.; Chakraborty, S. 2011. Decision making for material selection using the UTA method, The International Journal of Advanced Manufacturing Technology 57(1–4): 11. https://doi.org/10.1007/s00170-011-3293-7
Beiragh, R. G.; Alizadeh, R.; Kaleibari, S. S.; Cavallaro, F.; Hashemkhani Zolfani, S.; Bausys, R.; Mardani, A. 2020. An integrated multi-criteria decision making model for sustainability performance assessment for insurance companies, Sustainability 12(3): 789. https://doi.org/10.3390/su12030789
Carreno, M.; Ge, Y.-E.; Borthwick, S. 2014. Could green taxation measures help incentivise future Chinese car drivers to purchase low emission vehicles?, Transport 29(3): 260–268. https://doi.org/10.3846/16484142.2014.913261
Chen, K.; Zhao, F.; Hao, H.; Liu, Z. 2019. Selection of lithiumion battery technologies for electric vehicles under China’s new energy vehicle credit regulation, Energy Procedia 158: 3038–3044. https://doi.org/10.1016/j.egypro.2019.01.987
Demesouka, O. E.; Anagnostopoulos, K. P.; Siskos, E. 2019. Spatial multicriteria decision support for robust land-use suitability: the case of landfill site selection in Northeastern Greece, European Journal of Operational Research 272(2): 574–586. https://doi.org/10.1016/j.ejor.2018.07.005
Diouf, B.; Pode, R. 2015. Potential of lithium-ion batteries in renewable energy, Renewable Energy 76: 375–380. https://doi.org/10.1016/j.renene.2014.11.058
Gong, H.; Wang, M. Q.; Wang, H. 2013. New energy vehicles in China: policies, demonstration, and progress, Mitigation and Adaptation Strategies for Global Change 18(2): 207–228. https://doi.org/10.1007/s11027-012-9358-6
Grigoroudis, E.; Zopounidis, C. 2012. Developing an employee evaluation management system: the case of a healthcare organization, Operational Research 12(1): 83–106. https://doi.org/10.1007/s12351-011-0103-9
Gruca, A.; Sikora, M. 2013. Rule based functional description of genes – estimation of the multicriteria rule interestingness measure by the UTA method, Biocybernetics and Biomedical Engineering 33(4): 222–234. https://doi.org/10.1016/j.bbe.2013.09.005
Haider, H.; Singh, P.; Ali, W.; Tesfamariam, S.; Sadiq, R. 2015. Sustainability evaluation of surface water quality management options in developing countries: multicriteria analysis using fuzzy UTASTAR method, Water Resources Management 29(8): 2987–3013. https://doi.org/10.1007/s11269-015-0982-2
Hruška, R.; Průša, P.; Babić, D. 2014. The use of AHP method for selection of supplier, Transport 29(2): 195–203. https://doi.org/10.3846/16484142.2014.930928
Ishizaka, A.; Resce, G. 2021. Best-worst PROMETHEE method for evaluating school performance in the OECD’s PISA project, Socio-Economic Planning Sciences 73: 100799. https://doi.org/10.1016/j.seps.2020.100799
Jacquet-Lagreze, E.; Siskos, J. 1982. Assessing a set of additive utility functions for multicriteria decision-making, the UTA method, European Journal of Operational Research 10(2): 151–164. https://doi.org/10.1016/0377-2217(82)90155-2
Jayant, A.; Chandan, A. K.; Singh, S. 2019. Sustainable supplier selection for battery manufacturing industry: a MOORA and WASPAS based approach, Journal of Physics: Conference Series 1240: 012015. https://doi.org/10.1088/1742-6596/1240/1/012015
Kaleibari, S. S.; Beiragh, R. G.; Alizadeh, R.; Solimanpur, M. 2016. A framework for performance evaluation of energy supply chain by a compatible network data envelopment analysis model, Scientia Iranica: Transactions E: Industrial Engineering 23(4): 1904–1917. https://doi.org/10.24200/sci.2016.3936
Li, J.; Ku, Y.; Liu, C.; Zhou, Y. 2020. Dual credit policy: promoting new energy vehicles with battery recycling in a competitive environment?, Journal of Cleaner Production 243: 118456. https://doi.org/10.1016/j.jclepro.2019.118456
Li, L.; Jing, X. 2019. Analysis of tax policy for promoting the development of China’s new energy vehicles industry, Advances in Social Science, Education and Humanities Research 328: 315–320. https://doi.org/10.2991/ichssd-19.2019.63
Liao, H.; Mi, X.; Xu, Z. 2020. A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions, Fuzzy Optimization and Decision Making 19(1): 81–134. https://doi.org/10.1007/s10700-019-09309-5
Liao, H.; Xu, Z.; Herrera-Viedma, E.; Herrera, F. 2018. Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey, International Journal of Fuzzy Systems 20(7): 2084–2110. https://doi.org/10.1007/s40815-017-0432-9
Liu, Y.; Kokko, A. 2013. Who does what in China’s new energy vehicle industry?, Energy Policy 57: 21–29. https://doi.org/10.1016/j.enpol.2012.05.046
Makui, A.; Momeni, M. 2012. Using CSW weight’s in UTASTAR method, Decision Science Letters 1: 39–46. https://doi.org/10.5267/j.dsl.2012.06.001
Mi, X.; Liao, H.; Wu, X.; Xu, Z. 2020. Probabilistic linguistic information fusion: a survey on aggregation operators in terms of principles, definitions, classifications, applications, and challenges, International Journal of Intelligent Systems 35(3): 529–556. https://doi.org/10.1002/int.22216
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. https://doi.org/10.1016/j.omega.2019.01.009
Nian, V.; Chou, S. K.; Su, B.; Bauly, J. 2014. Life cycle analysis on carbon emissions from power generation – the nuclear energy example, Applied Energy 118: 68–82. https://doi.org/10.1016/j.apenergy.2013.12.015
Nikas, A.; Doukas, H.; Siskos, E.; Psarras, J. 2018. International cooperation for clean electricity: a UTASTAR application in energy policy, in N. Matsatsinis, E. Grigoroudis (Eds.). Preference Disaggregation in Multiple Criteria Decision Analysis. Multiple Criteria Decision Making, 163–186. https://doi.org/10.1007/978-3-319-90599-0_8
Pang, Q.; Wang, H.; Xu, Z. 2016. Probabilistic linguistic term sets in multi-attribute group decision making, Information Sciences 369: 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Papapostolou, A.; Karakosta, C.; Nikas, A.; Psarras, J. 2017. Exploring opportunities and risks for RES-E deployment under cooperation mechanisms between EU and Western Balkans: a multi-criteria assessment, Renewable and Sustainable Energy Reviews 80: 519–530. https://doi.org/10.1016/j.rser.2017.05.190
Patiniotakis, I.; Apostolou, D.; Mentzas, G. 2011. Fuzzy UTASTAR: a method for discovering utility functions from fuzzy data, Expert Systems with Applications 38(12): 15463–15474. https://doi.org/10.1016/j.eswa.2011.06.014
Rezaei, J. 2015. Best-worst multi-criteria decision-making method, Omega 53: 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Saaty, T. L. 1977. A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology 15(3): 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Sims, R. E. H.; Rogner, H.-H.; Gregory, K. 2003. Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation, Energy Policy 31(13): 1315–1326. https://doi.org/10.1016/S0301-4215(02)00192-1
Siskos, Y.; Yannacopoulos, D. 1985. UTASTAR: an ordinal regression method for building additive value functions, Investigação Operacional 5(1): 39–53.
Sola, A. V. H.; Mota, C. M. D. M. 2012. A multi-attribute decision model for portfolio selection aiming to replace technologies in industrial motor systems, Energy Conversion and Management 57: 97–106. https://doi.org/10.1016/j.enconman.2011.12.013
Stević, Ž.; Vasiljević, M.; Puška, A.; Tanackov, I.; Junevičius, R.; Vesković, S. 2019. Evaluation of suppliers under uncertainty: a multiphase approach based on fuzzy AHP and fuzzy EDAS, Transport 34(1): 52–66. https://doi.org/10.3846/transport.2019.7275
Touni, Z.; Makui, A.; Mohammadi, E. 2019. A MCDM-based approach using UTA-STRAR method to discover behavioral aspects in stock selection problem, International Journal of Industrial Engineering & Production Research 30(1): 93–103. https://doi.org/10.22068/ijiepr.30.1.93
Van de Kaa, G.; Rezaei, J.; Taebi, B.; Van de Poel, I.; Kizhakenath, A. 2020. How to weigh values in value sensitive design: a best worst method approach for the case of smart metering, Science and Engineering Ethics 26(1): 475–494. https://doi.org/10.1007/s11948-019-00105-3
Walter, B.; Pietrzak, B. 2005. Multi-criteria detection of bad smells in code with UTA method, Lecture Notes in Computer Science 3556: 154–161. https://doi.org/10.1007/11499053_18
Whittington, H. W. 2002. Electricity generation: options for reduction in carbon emissions, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 360(1797): 1653–1668. https://doi.org/10.1098/rsta.2002.1025
Wu, X.; Liao, H.; Xu, Z.; Hafezalkotob, A.; Herrera, F. 2018. Probabilistic linguistic MULTIMOORA: a multicriteria decision making method based on the probabilistic linguistic expectation function and the improved Borda rule, IEEE Transactions on Fuzzy Systems 26(6): 3688–3702. https://doi.org/10.1109/TFUZZ.2018.2843330
Xiong, W.-T.; Cheng, J. 2016. A weighted UTASTAR method for the multiple criteria decision making with interval numbers, Advances in Economics, Business and Management Research 10: 191–195. https://doi.org/10.2991/msmi-16.2016.46
Yuan, X.; Liu, X.; Zuo, J. 2015. The development of new energy vehicles for a sustainable future: a review, Renewable and Sustainable Energy Reviews 42: 298–305. https://doi.org/10.1016/j.rser.2014.10.016
Yuan, H.; Jiang, Y. 2019. Research on key influencing factors of China’s new energy automobile industry development, Advances in Economics, Business and Management Research 91: 676–681. https://doi.org/10.2991/edmi-19.2019.117
Zadeh, L. A. 1965. Fuzzy sets, Information and 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 – I, Information Sciences 8(3): 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zadeh, L. A. 2012. Computing with Words. Springer. 142 p. https://doi.org/10.1007/978-3-642-27473-2
Zhang, C.; Luo, L.; Liao, H.; Mardani, A.; Streimikiene, D.; Al-Barakati, A. 2020. A priority-based intuitionistic multiplicative UTASTAR method and its application in low-carbon tourism destination selection, Applied Soft Computing 88: 106026. https://doi.org/10.1016/j.asoc.2019.106026