Share:


A criterion utility conversion technique for probabilistic linguistic multiple criteria analysis in emergency management

Abstract

In multiple criteria decision making (MCDM), the even swaps method uses the relationships of criteria to make trade-offs but the burdens of experts are heavy; the linear programming technique for multidimensional analysis of preference (LINMAP) method cannot deal with the inter-dependencies among criteria but the cognitive burdens of experts are low. Taking the advantages of both these methods, this study proposes a criterion utility conversion (CUC) technique to solve probabilistic linguistic MCDM problems given that the probabilistic linguistic term set (PLTS) can reflect the psychology of experts when making evaluations. The utility conversion process is first proposed based on the marginal utilities of criteria. Then, the criterion preference ratios of experts are refined from the utility conversion process. Based on the criterion preference ratios and the operations of PLTSs, the adjusted probabilistic linguistic expected values of alternatives are calculated. The consistency and inconsistency indexes of alternatives and criteria are defined to set up the linear programming used to work out the criterion preference ratios. An illustration about the selection of emergency logistics supplier is given to validate the proposed method. The comparative analysis indicates the low cognitive burden, high stability, and strong applicability of the proposed method.


First published online 05 July 2021

Keyword : multiple criteria analysis, criterion utility conversion, probabilistic linguistic term set, emergency logistics supplier selection

How to Cite
Qin, R., Liao, H., & Jiang, L. (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
Published in Issue
Aug 31, 2021
Abstract Views
595
PDF Downloads
542
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adhami, A. Y., & Ahmad, F. (2020). Interactive Pythagorean-hesitant fuzzy computational algorithm for multiobjective transportation problem under uncertainty. International Journal of Management Science and Engineering Management, 15(4), 288−297. https://doi.org/10.1080/17509653.2020.1783381

Altun, K., & Dereli, T. (2014). Even easier multi-issue negotiation through modified Even-Swaps considering practically dominated alternatives. Computers & Industrial Engineering, 76, 307−317. https://doi.org/10.1016/j.cie.2014.08.015

Ben Abdelaziz, F., Lang, P., & Nadeau, R. (1999). Dominance and efficiency in multicriteria decision under uncertainty. Theory and Decision, 47(3), 191−211. https://doi.org/10.1023/A:1005102326115

Chen, S. X., Wang, J. Q., & Wang, T. L. (2019). Cloud-based ERP system selection based on extended probabilistic linguistic MULTIMOORA method and Choquet integral operator. Computational & Applied Mathematics, 38(2), 88. https://doi.org/10.1007/s40314-019-0839-z

Chen, T. Y. (2019). Multiple criteria group decision making using a parametric linear programming technique for multidimensional analysis of preference under uncertainty of Pythagorean fuzziness. IEEE Access, 7, 174108−174128. https://doi.org/10.1109/ACCESS.2019.2957161

Dereli, T., & Altun, K. (2012). Modified Even-Swaps: A novel, clear, rational and an easy-to-use mechanism for multi-issue negotiation. Computers & Industrial Engineering, 63(4), 1013−1029. https://doi.org/10.1016/j.cie.2012.06.013

Elahi, G., & Yu, E. (2012). Comparing alternatives for analyzing requirements trade-offs − In the absence of numerical data. Information and Software Technology, 54(6), 517−530. https://doi.org/10.1016/j.infsof.2011.10.007

Fang, R., Liao, H. C., Yang, J. B., & Xu, D. L. (2021). Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty. Journal of the Operations Research Society, 72(1), 130−144. https://doi.org/10.1080/01605682.2019.1654415

Gomes, L. F. A. M., Rangel, L. A. D., & Fernandes, P. P. (2012). Decision aiding in plastic surgery: a multicriteria analysis. Pesquisa Operacional, 32(2), 371−387. https://doi.org/10.1590/S0101-74382012005000011

Haghighi, M. H., Mousavi, S. M., & Mohagheghi, V. (2019). A new soft computing model based on linear assignment and linear programming technique for multidimensional analysis of preference with interval type-2 fuzzy sets. Applied Soft Computing, 77, 780−796. https://doi.org/10.1016/j.asoc.2019.01.048

Hamidzadeh, Z., Sattari, S., Soltanieh, M., & Vatani, A. (2020). Development of a multi-objective decision-making model to recover flare gases in a multi flare gases zone. Energy, 203, 117815. https://doi.org/10.1016/j.energy.2020.117815

Hammond, J. S., Keeney, R. L., & Raiffa, H. (1993). Even Swaps: a rational method for making tradeoffs. Harvard Business Review, 76(2), 137−138. https://www.researchgate.net/publication/13119982

Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International Journal of Management Science and Engineering Management, 15(2), 79−88. https://doi.org/10.1080/17509653.2019.1633964

Kajanus, M., Ahola, J., Kurttila, M., & Pesonen, M. (2001). Application of even swaps for strategy selection in a rural enterprise. Management Decision, 39(5), 394−402. https://doi.org/10.1108/00251740110395688

Kashef, M., Safari, H., Maleki, M., & Cruz-Machado, V. (2018). Solving MCDM problems based on combination of PACMAN and LINMAP. Journal of Multi-Criteria Decision Analysis, 25(5−6), 169−176. https://doi.org/10.1002/mcda.1650

Lahtinen, T. J., & Hämäläinen, R. P. (2016). Path dependence and biases in the even swaps decision analysis method. European Operations Research Perspectives, 249(3), 890–898. https://doi.org/10.1016/j.ejor.2015.09.056

Lahtinen, T. J., Hämäläinen, R. P., & Jenytin, C. (2020). On preference elicitation processes which mitigate the accumulation of biases in multi-criteria decision analysis. European Journal of Operational Research, 282(1), 201−210. https://doi.org/10.1016/j.ejor.2019.09.004

Lei, F., Wei, G. W., Wu, J., Wei, C., & Guo, Y. F. (2020). QUALIFLEX method for MAGDM with probabilistic uncertain linguistic information and its application to green supplier selection. Journal of Intelligent & Fuzzy Systems, 39(5), 6819−6831. https://doi.org/10.3233/JIFS-191737

Li, D. F. (2008). Extension of the LINMAP for multiattribute decision making under Atanassov’s intuitionistic fuzzy environment. Fuzzy Optimization and Decision Making, 7(1), 17−34. https://doi.org/10.1007/s10700-007-9022-x

Li, D. F., & Wan, S. P. (2014). Fuzzy heterogeneous multiattribute decision making method for outsourcing provider selection. Expert Systems with Applications, 41(6), 3047−3059. https://doi.org/10.1016/j.eswa.2013.10.036

Li, H. L., & Ma, L. C. (2008). Visualizing decision process on spheres based on the even swap concept. Decision Support Systems, 45(2), 354−367. https://doi.org/10.1016/j.dss.2008.01.004

Liao, H. C., Jiang, L. S., Lev, B., & Fujita, H. (2019a). Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Applied Soft Computing, 80, 450−464. https://doi.org/10.1016/j.asoc.2019.04.018

Liao, H. C., Jiang, L. S., Xu, Z. S., Xu, J. P., & Herrera, F. (2017). A linear programming method for multiple criteria decision making with probabilistic linguistic information. Information Science, 415, 341−355. https://doi.org/10.1016/j.ins.2017.06.035

Liao, H. C., Mi, X. M., & Xu, Z. S. (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, Z. Q., Liao, H. C., Gou, X. J., Xu, Z. S., & Zavadskas, E. K. (2019b). A hesitant fuzzy linguistic choquet integral-based MULTIMOORA method for multiple criteria decision making and its application in talent selection. Economic Computation and Economic Cybernetics Studies and Research, 53(2), 113−130. https://doi.org/10.24818/18423264/53.2.19.07

Lin, M. W., Chen, Z. Y., Liao, H. C., & Xu, Z. S. (2019). ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing. Nonlinear Dynamics, 96(3), 2125–2143. https://doi.org/10.1007/s11071-019-04910-0

Lu, X. Y., Dong, J. Y., & Wan, S. P. (2020). A novel three-phase LINMAP method for hybrid multi-criteria group decision making with dual hesitant fuzzy truth degrees. IEEE Access, 8, 112462−112483. https://doi.org/10.1109/ACCESS.2020.3001941

Mehrabadi, Z. K., & Boyaghchi, F. A. (2019). Thermodynamic, economic and environmental impact studies on various distillation units integrated with gasification-based multi-generation system: comparative study and optimization. Journal of Cleaner Production, 241, 118333. https://doi.org/10.1016/j.jclepro.2019.118333

Mi, X. M., Liao, H. C., Wu, X. L., & Xu, Z. S. (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

Milutinovic, G., Ahonen-Jonnarth, U., & Seipel, S. (2018). GISwaps: a new method for decision making in continuous choice models based on even swaps. International Journal of Decision Support Systems Technology, 10(3), 57−78. https://doi.org/10.4018/IJDSST.2018070104

Mustajoki, J., & Hämäläinen, R. P. (2005). A preference programming approach to make the even swaps method even easier. Decision Analysis, 2(2), 110−123. https://doi.org/10.1287/deca.1050.0043

Mustajoki, J., & Hämäläinen, R. P. (2007). Smart-Swaps — A decision support system for multicriteria decision analysis with the even swaps method. Decision Support Systems, 44(1), 313−325. https://doi.org/10.1016/j.dss.2007.04.004

Pang, Q., Wang, H., & Xu, Z. S. (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

Rodríguez, R. M., Martıńez, 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

Shaikh, A., Singh, A., Ghose, D., & Shabbiruddin. (2020). Analysis and selection of optimum material to improvise braking system in automobiles using integrated Fuzzy-COPRAS methodology. International Journal of Management Science and Engineering Management, 15(4), 265−273. https://doi.org/10.1080/17509653.2020.1772895

Sheu, J. B. (2007a). Challenges of emergency logistics management. Transportation Research Part ELogistics and Transportation Review, 43(6), 655−659. https://doi.org/10.1016/j.tre.2007.01.001

Sheu, J. B. (2007b). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E-Logistics and Transportation Review, 43(6), 687−709. https://doi.org/10.1016/j.tre.2006.04.004

Srinivasan, V., & Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preference. Psychometrica, 38, 337−342. https://doi.org/10.1007/BF02291658

Wang, J.-C., & Chen, T.-Y. (2020). A novel Pythagorean fuzzy LINMAP-based compromising approach for multiple criteria group decision-making with preference over alternatives. International Journal of Computational Intelligence Systems, 13(1), 444−463. https://doi.org/10.2991/ijcis.d.200408.001

Wang, J.-Q., Wu, J.-T., Wang, J. H., Zhang, H.-Y., & Chen, X.-H. (2014). Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Information Sciences, 288, 55−72. https://doi.org/10.1016/j.ins.2014.07.034

Wang, L., & Xu, J. Z. (2016). Emergency logistics distribution optimization model and algorithm in disaster chain. Logistics Technology, 35(8), 115−118.

Wei, G. W., Lei, F., Lin, R., Wang, R., Wei, Y., Wu, J., & Wei, C. (2020). Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations. Economic Research-Ekonomska Istraživanja, 33(1), 828−846. https://doi.org/10.1080/1331677X.2020.1734851

Wen, Z., Liao, H. C., Ren, R. X., Bai, C. G., Zavadskas, E. K., Antucheviciene, J., & Al-Barakati, A. (2019). Cold chain logistics management of medicine with an integrated multi-criteria decisionmaking method. International Journal of Environmental Research and Public Health, 16(23), 4843. https://doi.org/10.3390/ijerph16234843

Wu, X. L., & Liao, H. C. (2019). A consensus-based probabilistic linguistic gained and lost dominance score method. European Journal of Operational Research, 272(3), 1017−1027. https://doi.org/10.1016/j.ejor.2018.07.044

Wu, X. L., Liao, H. C., Xu, Z. S., 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), 3688−2702. https://doi.org/10.1109/TFUZZ.2018.2843330

Yao, Q. Z. (2019). Multi-objective optimization design of spur gear based on NSGA-II and decision making. Advances in Mechanical Engineering, 11(3), 1−8. https://doi.org/10.1177/1687814018824936

Zadeh, L. A. (1975). The concept of a linguistic variable and its applications to approximate reasoningPart I. Information Sciences, 8, 199−249. https://doi.org/10.1016/0020-0255(75)90036-5

Zhang, X. L., & Xing, X. M. (2017). Probabilistic linguistic VIKOR method to evaluate green supply chain initiatives. Sustainability, 9(7), 1231. https://doi.org/10.3390/su9071231

Zuo, W. J., Li, D. F., Yu, G. F., & Zhang, L. P. (2019). A large group decision-making method and its application to the evaluation of property perceived service quality. Journal of Intelligent & Fuzzy Systems, 37(1), 1513−1527. https://doi.org/10.3233/JIFS-182934