A Multi-attribute Decision-making Method for Interval Rough Number Information System Considering Distribution Types
DOI:
https://doi.org/10.15837/ijccc.2024.4.6633Keywords:
interval rough numbers, dominance degree, uniform distribution, exponential distribution, normal distribution, dynamic weightsAbstract
This paper proposes a novel multi-attribute decision-making (MADM) method for interval rough numbers (IRNs) considering different distribution types, namely uniform, exponential, and normal distributions. Upper and lower approximate interval dominance degrees are defined and aggregated using dynamic weights to obtain pairwise comparisons of IRNs. The properties of dominance are verified, and an attribute weight determination method based on the dominance balance degree is introduced. The proposed MADM method is data-driven and does not rely on the subjective preferences of decision-makers. Case analysis demonstrates the effectiveness and rationality of the proposed method, revealing that the distribution type of IRNs significantly impacts decision results, potentially leading to reversed ranking outcomes. The proposed method offers a comprehensive framework for handling MADM problems with IRNs under different distributions.
References
Yu, B.; Hu, Y.; Kang, Y. (2023). A novel variable precision rough set attribute reduction algorithm based on local attribute significance, International Journal of Approximate Reasoning, 157, 88- 104, 2023. https://doi.org/10.1016/j.ijar.2023.03.002
Barbara, W.; Angelika, A.L. (2021). Application of the rough set theory to the analysis of food safety in fish processing, Procedia Computer Science, 192, 3342-3350, 2021. https://doi.org/10.1016/j.procs.2021.09.107
Zeng, W.J.; Qin, F.; Li L. (2021). Research on Deduplication Mining Algorithm for Emergency Data Based on Rough Set Attributes Based on Information Entropy, Computing Technology & Automation, 40(04), 64-68, 2021.
Suchanek, P.; Bucki, R; Postrozny, J. (2023). Modelling and Simulation of a Decision-Making Process Supporting Business System Logistics, Int. Journal of Simulation Modelling, 22(4), 655- 666, 2023. https://doi.org/10.2507/IJSIMM22-4-665
Zhang. Y.M.; Song, Y.F.; Meng, X.; Liu, Z.G. (2023). Optimizing Supply Chain Efficiency with Fuzzy CRITIC-EDAS, Int. Journal of Simulation Modelling, 22(4), 723-733, 2023. https://doi.org/10.2507/IJSIMM22-4-CO19
Bellamn, R.E; Zadel, L.A. (1970). Decision making in a fuzzy environment, Management Science, 17B(4), 141-164, 1970. https://doi.org/10.1287/mnsc.17.4.B141
Pawlak, Z. (1982). Rough sets, International Journal of Computer and Science, 11(5), 341-356, 1982. https://doi.org/10.1007/BF01001956
Li, Q. (2023). Green Supply Chain Optimization with Fuzzy MCDM for Economic Growth, Int. Journal of Simulation Modelling, 22(4), 690-700, 2023. https://doi.org/10.2507/IJSIMM22-4-CO16
Witthayapraphakorn, A.; Jaijit, S. (2023). Using Simulation to Determine the Reorder Point under Uncertainty of a Retail Store, Int. Journal of Simulation Modelling, 22(2), 199-210, 2023. https://doi.org/10.2507/IJSIMM22-2-630
Fu, H., Du, Y., Ding, Q. Fu, M. (2023). Performance Evaluation of Port Enterprise Resource Integration Based on Fuzzy Comprehensive Evaluation Method, Tehnički vjesnik, 30(4), 1185- 1192, 2023. https://doi.org/10.17559/TV-20230130000291
Liu, B.D. (2002). Theory and practice of uncertain programming, Heidelberg, Physica-Verlag, Anchor, 2002. https://doi.org/10.1007/978-3-7908-1781-2
Cheng, L.H.; He Y.Y; Zhang Y. (2019). Rough set model of interval rough number under λ- similarity relation, Computer Engineering and Applications. 55(21), 46-51+59, 2019.
Weng, S.Z; Lv, Y.J. (2015). Ranking method and application of interval rough number, Journal of Nanjing University(Natural Science), 51(04), 818-825, 2015.
Weng, S.Z; Zhu, J.; Wang, C. (2021). Rough set model of dominance relation under interval rough number order information system, Fuzzy system and mathematics, 35(03), 133-144, 2021.
Zhang, W.Y; Yang, Y.; Liu, J. (2020). Multi-attribute decision-making method and application based on interval rough number, Computer application research, 37(10), 2990-2995+3019, 2020.
Xia, X.D; Lv, Y.J. (2016). TOPSIS method of interval rough number MAD based on distribution parameters, Journal of Guangxi University (Natural Science Edition), 41(05), 1603-1609, 2016.
Cheng, L.H; Zhang, Y.; He, Y. (2020). Rough set models of interval rough number information system, Journal of Intelligent and Fuzzy Systems, 40(1), 1-12, 2020. https://doi.org/10.3233/JIFS-191096
He, Y.Y; Zhang, Y.; Cheng, L.H. (2020). Interval rough number covering rough set model, Fuzzy system and mathematics, 34(03), 79-88, 2020 .
Lv, Y.J; Cheng, L.H; Zhang, Y,. (2021). Coverage classification, redundancy and attribute reduction of interval rough number information system, Control and decision-making, 36(03), 677-685, 2021.
Weng, S.Z; Lv, Y.J; Cao, Z.Q. (2022). Multi-attribute decision-making method of interval coarse fuzzy number based on distance measure, Fuzzy system and mathematics, 36(03), 131-144, 2022.
Xia, X.D; Lv, Y.J. (2017). A multi-attribute decision-making method for interval rough numbers with parameters, Computer Engineering and Applications. 53(05), 255-259, 2017.
Zeng, X.L; Xie, F.P. (2017). Multi-attribute decision-making method of interval rough number based on contact number, Computer Engineering and Applications. 53(03), 54-57+86, 2017.
Xu, K. (2023). Research on decision-making method of rough number in normal distribution interval, Chengdu, Sichuan Normal University, 2023.
Weng, S.Z; Lv, Y.J. (2020). Comparative simulation of interval rough number ranking and its application in the field of logistics, Journal of Hainan Tropical Ocean University, 27(02), 56-64, 2020.
Slowinski, R.; Vanderpooten, D. (2000). A generalized definition of rough approximations based on similarity, IEEE Transactions on Knowledge and Data Engineering, 12(2), 331-336, 2000. https://doi.org/10.1109/69.842271
Dragan, P.; Milan, M.; Radojko, O. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers : Hybrid DEMATEL -ANP-MAIRCA model, Expert Systems with Applications, 88, 58, 2017. https://doi.org/10.1016/j.eswa.2017.06.037
Wang, Y.L.; Zheng, X.Y.; Yin, X.M.; Cai, J.R. (2022). Simulation of Financing Decisions with Behavioural Preferences and Yield Uncertainty, Int. Journal of Simulation Modelling, 21(4), 675- 683, 2022. https://doi.org/10.2507/IJSIMM21-4-CO16
Evera, A.V.; Cîrnu, C.E.; Radulescu, C.Z. (2022). A Multi-Attribute approach for cyber threat intelligence product and services selection, Studies in Informatics and Control, 31(1), 13-23, 2022. https://doi.org/10.24846/v31i1y202202
Xu, Z.; Chen, J.J.; Ye, J.; Zhang, J. (2021). Automotive Parts Purchasing Using the Fuzzy MOMIP Model of Reliability Objective with Uncertain Weights, Studies in Informatics and Control, 30(2), 5-20, 2021. https://doi.org/10.24846/v30i2y202101
Shi, D.L.; Zhang, B.B.; Li, Y.A. (2020). Multi-Objective Flexible Job-Shop Scheduling Model Based on Fuzzy Theory and Immune Genetic Algorithm, Int. Journal of Simulation Modelling, 19(1), 123-133, 2020. https://doi.org/10.2507/IJSIMM19-1-CO1
Chen, J. (2023). A Novel Model for Language Training Assessment Based on Data Mining and Bayesian Network, Tehnički vjesnik, 30(3), 771-778, 2023. https://doi.org/10.17559/TV-20220814112137
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 weng shizhou, Liu Hongmei hongmei
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
