Ranking range based approach to MADM under incomplete context and its application in venture investment evaluation
Abstract
In real-world Multiple Attribute Decision Making (MADM) problem, the attribute weights information may be unknown or partially known. Several approaches have been suggested to address this kind of incomplete MADM problem. However, these approaches depend on the determination of attribute weights, and setting different attribute weight vectors may result in different ranking positions of alternatives. To deal with this issue, this paper develops a novel MADM approach: the ranking range based MADM approach. In the novel MADM approach, the minimum and maximum ranking positions of every alternative are generated using several optimization models, and the average ranking position of every alternative is produced applying the Monte Carlo simulation method. Then, the minimum, maximum and average ranking positions of the alternative are integrated into a new ranking position of the alternative. This novel approach is capable of dealing with venture investment evaluation problems. However, in the venture investment evaluation process, decision makers will present different risk attitudes. To deal with this issue, two ranking range based MADM approaches with risk attitudes are further designed. A case study and a simulation experiment are presented to show the validity of the proposal.
First published online 12 July 2019
Keyword : multiple attribute decision making, ranking range, incomplete attribute weights, venture investment evaluation, risk attitudes
How to Cite
Liu, Y., Zhang, H., Wu, Y., & Dong, Y. (2019). Ranking range based approach to MADM under incomplete context and its application in venture investment evaluation. Technological and Economic Development of Economy, 25(5), 877-899. https://doi.org/10.3846/tede.2019.10296
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives. New York: John Wiley&Sons.
Li, C. C., Dong, Y. C., & Herrera, F. (2019). A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups. IEEE Transactions on Fuzzy Systems, 27(2), 221-233. https://doi.org/10.1109/TFUZZ.2018.2857720
Li, C. C., Rodríguez, R. M., Martínez, L., Dong, Y. C., & Herrera, F. (2018). Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowledge-Based Systems, 145, 156-165. https://doi.org/10.1016/j.knosys.2018.01.011
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Liu, W. Q., Dong, Y. C., Chiclana, F., Cabrerizo, F. J., & Herrera-Viedma, E. (2017). Group decisionmaking based on heterogeneous preference relations with self-confidence. Fuzzy Optimization and Decision Making, 16(4), 429-447. https://doi.org/10.1007/s10700-016-9254-8
Liu, Y. T., Dong, Y. C., Liang, H. M., Chiclana, F., & Herrera-Viedma, E. (2018). Multiple attribute strategic weight manipulation with minimum cost in a group decision making context with interval attribute weights information. IEEE Transactions on Systems, Man, and Cybernetics: Systems (in Press). https://doi.org/10.1109/TSMC.2018.2874942
López, J. C. L., Carrillo, P. A. Á., Chavira, D. A. G., & Noriega, J. J. S. (2017). A web-based group decision support system for multicriteria ranking problems. Operational Research, 17(2), 499-534. https://doi.org/10.1007/s12351-016-0234-0
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Morente-Molinera, J. A., Kou, G., Pang, C., Cabrerizo, J., & Herrera-Viedma, E. (2019). An automatic procedure to create fuzzy ontologies from users’ opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods. Information Sciences, 476, 222-238. https://doi.org/10.1016/j.ins.2018.10.022
Nanda, R., & Rhodes-Kropf, M. (2016). Financing risk and innovation, Management Science, 63(4), 901-918. https://doi.org/10.1287/mnsc.2015.2350
Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Belgrade: University of Belgrade.
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
Pérez, I. J., Cabrerizo, F. J., Alonso, S., & Herrera-Viedma, E. (2014). A new consensus model for group decision making problems with non homogeneous experts. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 494-498. https://doi.org/10.1109/TSMC.2013.2259155
Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., & Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Science, 459, 20-35. https://doi.org/10.1016/j.ins.2018.05.017
Roy, B., & Bertier, B. (1972). La metode ELECTRE II. In Sixieme Conference internationale de rechearche operationelle. Dublin.
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Singh, A., Gupta, A., & Mehra, A. (2017). Energy planning problems with interval-valued 2-tuple linguistic information. Operational Research, 17(3), 821-848. https://doi.org/10.1007/s12351-016-0245-x
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