Large-scale emergency supplier selection considering limited rational behaviors of decision makers and ranking robustness
Abstract
Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model.
Keyword : emergency supplier selection, large-scale alternatives, multi-criterion decision-making, regret theory, disappointment theory, τ-balanced clustering method, ROCOSD method
How to Cite
Li, X., Liao, H., Baušys, R., & Zavadskas, E. K. (2024). Large-scale emergency supplier selection considering limited rational behaviors of decision makers and ranking robustness. Technological and Economic Development of Economy, 30(4), 1037–1063. https://doi.org/10.3846/tede.2024.21569
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alvarez, P. A., Ishizaka, A., & Martínez. L. (2021). Multiple-criteria decision-making sorting methods: A survey. Expert Systems with Applications, 183, Article 115368. https://doi.org/10.1016/j.eswa.2021.115368
Azadi, M., Mirhedayatian, S. M., & Saen, R. F. (2013). A new fuzzy goal directed benchmarking for supplier selection. International Journal of Services and Operations Management, 14(3), 321–335. https://doi.org/10.1504/IJSOM.2013.052093
Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research, 30, 961–981. https://doi.org/10.1287/opre.30.5.961
Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research, 33(1), 1–27. https://doi.org/10.1287/opre.33.1.1
Brans, J. P., & Vincke, P. (1985). Note-a preference ranking organisation method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6), 647–656. https://doi.org/10.1287/mnsc.31.6.647
Chao, X., Kou, G., Peng, Y., & Viedma, E. H. (2021). Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. European Journal of Operational Research, 288(1), 271–293. https://doi.org/10.1016/j.ejor.2020.05.047
Danielson, M., & Ekenberg, L. (2017). A robustness study of state-of-the-art surrogate weights for MCDM. Group Decision Negotiation, 26(4), 677–691. https://doi.org/10.1007/s10726-016-9494-6
De Smet, Y., & Guzman, L. M. (2004). Towards multicriteria clustering: An extension of the k-means algorithm. European Journal of Operational Research, 158(2), 390–398. https://doi.org/10.1016/j.ejor.2003.06.012
Dialoulaki, D., Mavtotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Du, Z. J., Luo, H. Y., Lin, X. D., & Yu, S. M. (2020). A trust-similarity analysis-based clustering method for large-scale group decision-making under a social network. Information Fusion, 63, 13–29. https://doi.org/10.1016/j.inffus.2020.05.004
Efremova, R., & Lotov, A. (2009). A framework for participatory decision support using pareto frontier visualization, goal identification and arbitration. European Journal of Operational Research, 199(2), 459–467. https://doi.org/10.1016/j.ejor.2008.10.034
Fei, L. G., Feng, Y. Q., & Wang, H. L. (2021). Modeling heterogeneous multi-attribute emergency decision-making with Dempster-Shafer theory. Computers & Industrial Engineering, 161, Article 107633. https://doi.org/10.1016/j.cie.2021.107633
Fernández, E., Figueira, J. R., Navarro, J., & Roy, B. (2017). ELECTRE TRI-nB: A new multiple criteria ordinal classification method. European Journal of Operational Research, 263(1), 214–224. https://doi.org/10.1016/j.ejor.2017.04.048
Franti, P., Brown, G., Loog, M., Escolano, F., & Pelillo, M. (2014). Balanced K-means for clustering. Structural, Syntactic, and Statistical Pattern Recognition, 8621, 32–41. https://doi.org/10.1007/978-3-662-44415-3
Gonzalez, T. F. (1984). Clustering to minimize the maximum inter-cluster distance. Theoretical Computer Science, 38(2–3), 293–306. https://doi.org/10.1016/0304-3975(85)90224-5
Hu, S. L., & Dong, Z. S. (2019). Supplier selection and pre-positioning strategy in humanitarian relief. Omega, 83, 287–298. https://doi.org/10.1016/j.omega.2018.10.011
Jessop, A. (2004). Sensitivity and robustness in selection problems. Computers Operational Research, 31(4), 607–622. https://doi.org/10.1016/S0305-0548(03)00017-0
Kraude, R., Narayanan, S., & Talluri, S. (2023). Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis. European Journal of Operational Research, 303(3), 1168–1182. https://doi.org/10.1016/j.ejor.2022.03.016
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics, 52(1), 7–21. https://doi.org/10.1002/nav.20053
Laciana, C. E., & Weber, E. U. (2008). Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment. Journal of Risk and Uncertainty, 36(1), 1–17. https://doi.org/10.1007/s11166-007-9027-4
Li, H. L., Yang, J. Q., & Xiang, Z. Q. (2022). A fuzzy linguistic multi-criteria decision-making approach to assess emergency suppliers. Sustainability, 14(20), Article 13114. https://doi.org/10.3390/su142013114
Li, X. F., Liao, H. C., & Wen, Z. (2021). A consensus model to manage the non-cooperative behaviors of individuals in uncertain group decision making problems during the COVID-19 outbreak. Applied Soft Computing, 99, Article 106879. https://doi.org/10.1016/j.asoc.2020.106879
Li, X. L., Chen, M. L., & Wang, Q. (2020). Adaptive consistency propagation method for graph clustering. IEEE Transaction on Knowledge and Data Engineering, 32(4), 797–802. https://doi.org/10.1109/TKDE.2019.2936195
Liao, H. C., Zhang, Z. Y., Xu, Z. S., & Banaitis, A. (2020). A heterogeneous regret-theory-based method with Choquet integral to multiattribute reverse auction. IEEE Transactions on Engineering Management, 69(5), 2248–2259. https://doi.org/10.1109/TEM.2020.3004501
Lin, Y. M., Tang, H. B., Li, Y., Fang, C. X., Xu, Z. J., Zhou, Y., & Zhou, A. Y. (2022). Generating clusters of similar sizes by constrained balanced clustering. Applied Intelligence, 52, 5273–5289. https://doi.org/10.1007/s10489-021-02682-y
Liu, L., Zhu, Q. Y, Yang, D., & Liu, S. (2023a). Extended multicriteria group decision making with a novel aggregation operator for emergency material supplier selection. Entropy, 25(4), Article 702. https://doi.org/10.3390/e25040702
Liu, P. D., Wang, X. Y., Teng, F., Li, Y. W., & Wang, F. B. (2022a). Distance education quality evaluation based on multigranularity probabilistic linguistic term sets and disappointment theory. Information Sciences, 605, 159–181. https://doi.org/10.1016/j.ins.2022.05.034
Liu, P. D, Zhang, K., Wang, P., & Wang, F. B. (2022b). A clustering-and maximum consensus-based model for social network large-scale group decision making with linguistic distribution. Information Sciences, 602, 269–297. https://doi.org/10.1016/j.ins.2022.04.038
Liu, S., He, X. J., Chan, F. T. S., & Wang, Z. Y. (2022c). An extended multi-criteria group decision-making method with psychological factors and bidirectional influence relation for emergency medical supplier selection. Expert Systems with Applications, 202, Article 117414. https://doi.org/10.1016/j.eswa.2022.117414
Liu, Y. D., Li, X., & Zheng, Z. Q. (2023b). Smart natural disaster relief: Assisting victims with artificial intelligence in lending. Information Systems Research. https://doi.org/10.1287/isre.2023.1230
Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal, 92(368), 961–981. https://doi.org/10.2307/2232669
Madhooshiarzanagh, P., & Abi-Zeid, I. (2021). A disaggregation approach for indirect preference elicitation in Electre TRI-nC: Application and validation. Journal of Multi-Criteria Decision Analysis, 28(3–4), 144–159. https://doi.org/10.1002/mcda.1730
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
Ozer, M. (2005). Fuzzy c-means clustering and Internet portals: A case study. European Journal of Operational Research, 164(3), 696–714. https://doi.org/10.1016/j.ejor.2003.11.015
Pala, O. (2023). A new objective weighting method based on robustness of ranking with standard deviation and correlation: The ROCOSD method. Information Sciences, 636, Article 118930. https://doi.org/10.1016/j.ins.2023.04.009
Peng, H. G., Shen, K. W., He, S. S., Zhang, H. Y., & Wang, J. Q. (2019). Investment risk evaluation for new energy resources: An integrated decision support model based on regret theory and ELECTRE III. Energy Conversion and Management, 183, 332–348. https://doi.org/10.1016/j.enconman.2019.01.015
Peng, X. D., & Huang, H. H. (2020). Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695–724. https://doi.org/10.3846/tede.2020.11920
Qin, J. D., Liu, X. W., & Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626–638. https://doi.org/10.1016/j.ejor.2016.09.059
Qin, R., Liao, H. C., & Jiang, L. S. (2021). A criterion utility conversion technique for probabilistic linguistic multiple criteria analysis in emergency management. Technological and Economic Development of Economy, 27(5), 1207–1226. https://doi.org/10.3846/tede.2021.15051
Ruan, J., Wan, Y., & Ma, Y. (2023). Two-sided matching decision method of electricity sales package based on disappointment theory. Applied Sciences, 13(17), Article 9683. https://doi.org/10.3390/app13179683
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672. https://doi.org/10.1016/j.cie.2018.10.001
Su, H., & Meng, L. (2017). Emergency procurement suppliers selection based on entropy weighted TOPSIS method. In International Conference on Strategic Management (pp. 496–502).
Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227–1249. https://doi.org/10.3846/tede.2021.15058
van Dijk, W. W., & Zeelenberg, M. (2002). Investigating the appraisal patterns of regret and disappointment. Motivation and Emotion, 26(4), 321–331. https://doi.org/10.1023/A:1022823221146
Von Neumann, J., & Morgenstern, O. (1944). The theory of games and economic behavior (pp. 86–92). Princeton University Press.
Wallenius, J., Dyer, J. S., Fishburn, P. C., Steuer, R. E., Zionts, S., & Deb, K. (2008). Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead. Management Science, 54(7), 1336–1349. https://doi.org/10.1287/mnsc.1070.0838
Wan, Q. F., Xu, X. H., Chen, X. H, & Zhuang, J. (2020). A two-stage optimization model for large-scale group decision-making in disaster management: minimizing group conflict and maximizing individual satisfaction. Group Decision and Negotiation, 29, 901–921. https://doi.org/10.1007/s10726-020-09684-0
Wang, H. D., Pan, X. H., Yan, J., Yao, J. L., & He. S. F. (2020). A projection-based RT method for multi-attribute decision making under interval type-2 fuzzy sets environment. Information Sciences, 512, 108–122. https://doi.org/10.1016/j.ins.2019.09.041
Wang, J. J., Ma, X. L, Xu, Z. S, & Zhan, J. M. (2022). Regret theory-based three-way decision model in hesitant fuzzy environments and its application to medical decision. IEEE Transactions on Fuzzy Systems, 30(12), 5361–5375. https://doi.org/10.1109/TFUZZ.2022.3176686
Wang, L. E., Liu, H. C., & Quan, M. Y. (2016). Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Computers & Industrial Engineering, 102, 175–185. https://doi.org/10.1016/j.cie.2016.11.003
Wang, X. D., & Cai, J. F. (2017). A group decision-making model based on distance-based VIKOR with incomplete heterogeneous information and its application to emergency supplier selection. Kybernetes, 46(3), 501–529. https://doi.org/10.1108/K-06-2016-0132
Xu, X. H., Yin, X., & Chen, X. H. (2019). A large-group emergency risk decision method based on data mining of public attribute preferences. Knowledge Based Systems, 163, 495–509. https://doi.org/10.1016/j.knosys.2018.09.010
Xue, W. T., Xu, Z. S., & Lu, W. H. (2023). A probabilistic linguistic thermodynamic method based on the water-filling algorithm and regret theory for emergency decision making. Economic Research-Ekonomska Istraživanja, 36(1), Article 2076141. https://doi.org/10.1080/1331677X.2022.2076141
Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research, 205(1), 202–204. https://doi.org/10.1016/j.ejor.2010.01.019
Yin, J. L., Guo, J., Ji, T. M., Cai, J. R., Xiao, L., & Dong, Z. (2019). An extended TODIM method for project manager’s competency evaluation. Journal of Civil Engineering and Management, 25(7), 673–686. https://doi.org/10.3846/jcem.2019.10521
Yu, L., & Lai, K. K. (2011). A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decision Support Systems, 51, 307–315. https://doi.org/10.1016/j.dss.2010.11.024
Zhan, J. M., Deng, J., Xu, Z. S., & Martinez, L. (2023). A three-way decision methodology with regret theory via triangular fuzzy numbers in incomplete multiscale decision information systems. IEEE Transaction on Fuzzy Systems, 31(8), 2773–2787. https://doi.org/10.1109/TFUZZ.2023.3237646
Zhang, H. Y., Wei, G. W., & Chen, X. D. (2022). SF-GRA method based on cumulative prospect theory for multiple attribute group decision making and its application to emergency supplies supplier selection. Engineering Applications of Artificial Intelligence, 110, Article 104679. https://doi.org/10.1016/j.engappai.2022.104679
Zhao, Q., Ju, Y. B., Martinez, L., Pedrycz, W., Dong, P. W., & Wang, A. H. (2022). SMAA-Bicapacity-Choquet-Regret model for heterogeneous linguistic MCDM with interactive criteria with bipolar scale and 2-tuple aspirations. IEEE Transaction on Fuzzy Systems, 30(10), 4384–4398. https://doi.org/10.1109/TFUZZ.2022.3149401
Zhou, S. N., Ji, X., & Xu, X. H. (2020). A hierarchical selection algorithm for multiple attributes decision making with large-scale alternatives. Information Sciences, 521, 195–208. https://doi.org/10.1016/j.ins.2020.02.030
Zhou, Y. J., Zhou, M., Liu, X. B., Cheng, B. Y., & Herrera-Viedma, E. (2022). Consensus reaching mechanism with parallel dynamic feedback strategy for large-scale group decision making under social network analysis. Computers & Industrial Engineering, 174, Article 108818. https://doi.org/10.1016/j.cie.2022.108818
Zhou, Y. Y., Wang, S., Chen, Y., & Zheng, C. L. (2023). Statistics-based method for large-scale group decision-making with incomplete linguistic distribution fuzzy information: Incorporating reliability and entropy. Information Fusion, 99, Article 101894. https://doi.org/10.1016/j.inffus.2023.101894
Zuo, W. J., Li, D. F., & Yu, G. F. (2020). A general multi-attribute multi-scale decision making method based on dynamic LINMAP for property perceived service quality evaluation. Technological and Economic Development of Economy, 26(5), 1052–1073. https://doi.org/10.3846/tede.2020.12726
Azadi, M., Mirhedayatian, S. M., & Saen, R. F. (2013). A new fuzzy goal directed benchmarking for supplier selection. International Journal of Services and Operations Management, 14(3), 321–335. https://doi.org/10.1504/IJSOM.2013.052093
Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research, 30, 961–981. https://doi.org/10.1287/opre.30.5.961
Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research, 33(1), 1–27. https://doi.org/10.1287/opre.33.1.1
Brans, J. P., & Vincke, P. (1985). Note-a preference ranking organisation method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6), 647–656. https://doi.org/10.1287/mnsc.31.6.647
Chao, X., Kou, G., Peng, Y., & Viedma, E. H. (2021). Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. European Journal of Operational Research, 288(1), 271–293. https://doi.org/10.1016/j.ejor.2020.05.047
Danielson, M., & Ekenberg, L. (2017). A robustness study of state-of-the-art surrogate weights for MCDM. Group Decision Negotiation, 26(4), 677–691. https://doi.org/10.1007/s10726-016-9494-6
De Smet, Y., & Guzman, L. M. (2004). Towards multicriteria clustering: An extension of the k-means algorithm. European Journal of Operational Research, 158(2), 390–398. https://doi.org/10.1016/j.ejor.2003.06.012
Dialoulaki, D., Mavtotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Du, Z. J., Luo, H. Y., Lin, X. D., & Yu, S. M. (2020). A trust-similarity analysis-based clustering method for large-scale group decision-making under a social network. Information Fusion, 63, 13–29. https://doi.org/10.1016/j.inffus.2020.05.004
Efremova, R., & Lotov, A. (2009). A framework for participatory decision support using pareto frontier visualization, goal identification and arbitration. European Journal of Operational Research, 199(2), 459–467. https://doi.org/10.1016/j.ejor.2008.10.034
Fei, L. G., Feng, Y. Q., & Wang, H. L. (2021). Modeling heterogeneous multi-attribute emergency decision-making with Dempster-Shafer theory. Computers & Industrial Engineering, 161, Article 107633. https://doi.org/10.1016/j.cie.2021.107633
Fernández, E., Figueira, J. R., Navarro, J., & Roy, B. (2017). ELECTRE TRI-nB: A new multiple criteria ordinal classification method. European Journal of Operational Research, 263(1), 214–224. https://doi.org/10.1016/j.ejor.2017.04.048
Franti, P., Brown, G., Loog, M., Escolano, F., & Pelillo, M. (2014). Balanced K-means for clustering. Structural, Syntactic, and Statistical Pattern Recognition, 8621, 32–41. https://doi.org/10.1007/978-3-662-44415-3
Gonzalez, T. F. (1984). Clustering to minimize the maximum inter-cluster distance. Theoretical Computer Science, 38(2–3), 293–306. https://doi.org/10.1016/0304-3975(85)90224-5
Hu, S. L., & Dong, Z. S. (2019). Supplier selection and pre-positioning strategy in humanitarian relief. Omega, 83, 287–298. https://doi.org/10.1016/j.omega.2018.10.011
Jessop, A. (2004). Sensitivity and robustness in selection problems. Computers Operational Research, 31(4), 607–622. https://doi.org/10.1016/S0305-0548(03)00017-0
Kraude, R., Narayanan, S., & Talluri, S. (2023). Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis. European Journal of Operational Research, 303(3), 1168–1182. https://doi.org/10.1016/j.ejor.2022.03.016
Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics, 52(1), 7–21. https://doi.org/10.1002/nav.20053
Laciana, C. E., & Weber, E. U. (2008). Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment. Journal of Risk and Uncertainty, 36(1), 1–17. https://doi.org/10.1007/s11166-007-9027-4
Li, H. L., Yang, J. Q., & Xiang, Z. Q. (2022). A fuzzy linguistic multi-criteria decision-making approach to assess emergency suppliers. Sustainability, 14(20), Article 13114. https://doi.org/10.3390/su142013114
Li, X. F., Liao, H. C., & Wen, Z. (2021). A consensus model to manage the non-cooperative behaviors of individuals in uncertain group decision making problems during the COVID-19 outbreak. Applied Soft Computing, 99, Article 106879. https://doi.org/10.1016/j.asoc.2020.106879
Li, X. L., Chen, M. L., & Wang, Q. (2020). Adaptive consistency propagation method for graph clustering. IEEE Transaction on Knowledge and Data Engineering, 32(4), 797–802. https://doi.org/10.1109/TKDE.2019.2936195
Liao, H. C., Zhang, Z. Y., Xu, Z. S., & Banaitis, A. (2020). A heterogeneous regret-theory-based method with Choquet integral to multiattribute reverse auction. IEEE Transactions on Engineering Management, 69(5), 2248–2259. https://doi.org/10.1109/TEM.2020.3004501
Lin, Y. M., Tang, H. B., Li, Y., Fang, C. X., Xu, Z. J., Zhou, Y., & Zhou, A. Y. (2022). Generating clusters of similar sizes by constrained balanced clustering. Applied Intelligence, 52, 5273–5289. https://doi.org/10.1007/s10489-021-02682-y
Liu, L., Zhu, Q. Y, Yang, D., & Liu, S. (2023a). Extended multicriteria group decision making with a novel aggregation operator for emergency material supplier selection. Entropy, 25(4), Article 702. https://doi.org/10.3390/e25040702
Liu, P. D., Wang, X. Y., Teng, F., Li, Y. W., & Wang, F. B. (2022a). Distance education quality evaluation based on multigranularity probabilistic linguistic term sets and disappointment theory. Information Sciences, 605, 159–181. https://doi.org/10.1016/j.ins.2022.05.034
Liu, P. D, Zhang, K., Wang, P., & Wang, F. B. (2022b). A clustering-and maximum consensus-based model for social network large-scale group decision making with linguistic distribution. Information Sciences, 602, 269–297. https://doi.org/10.1016/j.ins.2022.04.038
Liu, S., He, X. J., Chan, F. T. S., & Wang, Z. Y. (2022c). An extended multi-criteria group decision-making method with psychological factors and bidirectional influence relation for emergency medical supplier selection. Expert Systems with Applications, 202, Article 117414. https://doi.org/10.1016/j.eswa.2022.117414
Liu, Y. D., Li, X., & Zheng, Z. Q. (2023b). Smart natural disaster relief: Assisting victims with artificial intelligence in lending. Information Systems Research. https://doi.org/10.1287/isre.2023.1230
Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal, 92(368), 961–981. https://doi.org/10.2307/2232669
Madhooshiarzanagh, P., & Abi-Zeid, I. (2021). A disaggregation approach for indirect preference elicitation in Electre TRI-nC: Application and validation. Journal of Multi-Criteria Decision Analysis, 28(3–4), 144–159. https://doi.org/10.1002/mcda.1730
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
Ozer, M. (2005). Fuzzy c-means clustering and Internet portals: A case study. European Journal of Operational Research, 164(3), 696–714. https://doi.org/10.1016/j.ejor.2003.11.015
Pala, O. (2023). A new objective weighting method based on robustness of ranking with standard deviation and correlation: The ROCOSD method. Information Sciences, 636, Article 118930. https://doi.org/10.1016/j.ins.2023.04.009
Peng, H. G., Shen, K. W., He, S. S., Zhang, H. Y., & Wang, J. Q. (2019). Investment risk evaluation for new energy resources: An integrated decision support model based on regret theory and ELECTRE III. Energy Conversion and Management, 183, 332–348. https://doi.org/10.1016/j.enconman.2019.01.015
Peng, X. D., & Huang, H. H. (2020). Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695–724. https://doi.org/10.3846/tede.2020.11920
Qin, J. D., Liu, X. W., & Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626–638. https://doi.org/10.1016/j.ejor.2016.09.059
Qin, R., Liao, H. C., & Jiang, L. S. (2021). A criterion utility conversion technique for probabilistic linguistic multiple criteria analysis in emergency management. Technological and Economic Development of Economy, 27(5), 1207–1226. https://doi.org/10.3846/tede.2021.15051
Ruan, J., Wan, Y., & Ma, Y. (2023). Two-sided matching decision method of electricity sales package based on disappointment theory. Applied Sciences, 13(17), Article 9683. https://doi.org/10.3390/app13179683
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672. https://doi.org/10.1016/j.cie.2018.10.001
Su, H., & Meng, L. (2017). Emergency procurement suppliers selection based on entropy weighted TOPSIS method. In International Conference on Strategic Management (pp. 496–502).
Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227–1249. https://doi.org/10.3846/tede.2021.15058
van Dijk, W. W., & Zeelenberg, M. (2002). Investigating the appraisal patterns of regret and disappointment. Motivation and Emotion, 26(4), 321–331. https://doi.org/10.1023/A:1022823221146
Von Neumann, J., & Morgenstern, O. (1944). The theory of games and economic behavior (pp. 86–92). Princeton University Press.
Wallenius, J., Dyer, J. S., Fishburn, P. C., Steuer, R. E., Zionts, S., & Deb, K. (2008). Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead. Management Science, 54(7), 1336–1349. https://doi.org/10.1287/mnsc.1070.0838
Wan, Q. F., Xu, X. H., Chen, X. H, & Zhuang, J. (2020). A two-stage optimization model for large-scale group decision-making in disaster management: minimizing group conflict and maximizing individual satisfaction. Group Decision and Negotiation, 29, 901–921. https://doi.org/10.1007/s10726-020-09684-0
Wang, H. D., Pan, X. H., Yan, J., Yao, J. L., & He. S. F. (2020). A projection-based RT method for multi-attribute decision making under interval type-2 fuzzy sets environment. Information Sciences, 512, 108–122. https://doi.org/10.1016/j.ins.2019.09.041
Wang, J. J., Ma, X. L, Xu, Z. S, & Zhan, J. M. (2022). Regret theory-based three-way decision model in hesitant fuzzy environments and its application to medical decision. IEEE Transactions on Fuzzy Systems, 30(12), 5361–5375. https://doi.org/10.1109/TFUZZ.2022.3176686
Wang, L. E., Liu, H. C., & Quan, M. Y. (2016). Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Computers & Industrial Engineering, 102, 175–185. https://doi.org/10.1016/j.cie.2016.11.003
Wang, X. D., & Cai, J. F. (2017). A group decision-making model based on distance-based VIKOR with incomplete heterogeneous information and its application to emergency supplier selection. Kybernetes, 46(3), 501–529. https://doi.org/10.1108/K-06-2016-0132
Xu, X. H., Yin, X., & Chen, X. H. (2019). A large-group emergency risk decision method based on data mining of public attribute preferences. Knowledge Based Systems, 163, 495–509. https://doi.org/10.1016/j.knosys.2018.09.010
Xue, W. T., Xu, Z. S., & Lu, W. H. (2023). A probabilistic linguistic thermodynamic method based on the water-filling algorithm and regret theory for emergency decision making. Economic Research-Ekonomska Istraživanja, 36(1), Article 2076141. https://doi.org/10.1080/1331677X.2022.2076141
Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research, 205(1), 202–204. https://doi.org/10.1016/j.ejor.2010.01.019
Yin, J. L., Guo, J., Ji, T. M., Cai, J. R., Xiao, L., & Dong, Z. (2019). An extended TODIM method for project manager’s competency evaluation. Journal of Civil Engineering and Management, 25(7), 673–686. https://doi.org/10.3846/jcem.2019.10521
Yu, L., & Lai, K. K. (2011). A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decision Support Systems, 51, 307–315. https://doi.org/10.1016/j.dss.2010.11.024
Zhan, J. M., Deng, J., Xu, Z. S., & Martinez, L. (2023). A three-way decision methodology with regret theory via triangular fuzzy numbers in incomplete multiscale decision information systems. IEEE Transaction on Fuzzy Systems, 31(8), 2773–2787. https://doi.org/10.1109/TFUZZ.2023.3237646
Zhang, H. Y., Wei, G. W., & Chen, X. D. (2022). SF-GRA method based on cumulative prospect theory for multiple attribute group decision making and its application to emergency supplies supplier selection. Engineering Applications of Artificial Intelligence, 110, Article 104679. https://doi.org/10.1016/j.engappai.2022.104679
Zhao, Q., Ju, Y. B., Martinez, L., Pedrycz, W., Dong, P. W., & Wang, A. H. (2022). SMAA-Bicapacity-Choquet-Regret model for heterogeneous linguistic MCDM with interactive criteria with bipolar scale and 2-tuple aspirations. IEEE Transaction on Fuzzy Systems, 30(10), 4384–4398. https://doi.org/10.1109/TFUZZ.2022.3149401
Zhou, S. N., Ji, X., & Xu, X. H. (2020). A hierarchical selection algorithm for multiple attributes decision making with large-scale alternatives. Information Sciences, 521, 195–208. https://doi.org/10.1016/j.ins.2020.02.030
Zhou, Y. J., Zhou, M., Liu, X. B., Cheng, B. Y., & Herrera-Viedma, E. (2022). Consensus reaching mechanism with parallel dynamic feedback strategy for large-scale group decision making under social network analysis. Computers & Industrial Engineering, 174, Article 108818. https://doi.org/10.1016/j.cie.2022.108818
Zhou, Y. Y., Wang, S., Chen, Y., & Zheng, C. L. (2023). Statistics-based method for large-scale group decision-making with incomplete linguistic distribution fuzzy information: Incorporating reliability and entropy. Information Fusion, 99, Article 101894. https://doi.org/10.1016/j.inffus.2023.101894
Zuo, W. J., Li, D. F., & Yu, G. F. (2020). A general multi-attribute multi-scale decision making method based on dynamic LINMAP for property perceived service quality evaluation. Technological and Economic Development of Economy, 26(5), 1052–1073. https://doi.org/10.3846/tede.2020.12726