A Reliable Approach in Solving Multi-Attribute Decision-Making Problems through Fuzzy Rule-Base System and Z-numbers
الموضوعات : Fuzzy Optimization and Modeling JournalSaeed Bahrami 1 , Mahmonir Bayanati 2 , Mohammad Reza Nasiri Janagha 3 , Saman Malekian 4 , Milad Abolghasemian 5 , Adel Pourghader Chobar 6
1 - Department of Educational Science, Farhangian University, Tehran, Iran
2 - Faculty of Technology and Industrial Management, Health and Industry Research Centre, West Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
4 - Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
5 - Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
6 - Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Group decision-making, Linguistic Variables, Multi-attribute decision-making, Z-number, fuzzy rule-based,
ملخص المقالة :
Multi-Attribute Decision Making (MADM) process is the most well-known branch of decision making and it is one of the most important tasks that have received a lot of attention in many areas. In solving MADM issues, the parameters of decision-making are often faced with problems, such as imprecise, vague, uncertain, or incomplete information which lead to inaccurate decision-making. To cope with these problems, the researchers apply fuzzy set theory as the best-developed approach. Among different fuzzy methods, the fuzzy rule-based system (FRBS) due to its flexibility, simplicity, and experts' knowledge modeling is an adequate technique for solving MADM problems. The main objective of this study is to apply experts' opinions by Z-numbers in MADM issues to enhance the accuracy of the decision-making process. The fundamental issue in solving MADM problems is that inadequate information in the experts' opinions leads to some degree of uncertainty in decisions. Indeed, in FRBS research to ranking, the reliability level (Z-numbers) in experts' opinions within the decision-making process has not been taken into account. Whereas, the Z-numbers play a key role in the decision-making process to reach more precise decisions affecting the final ranking results. In the proposed approach (Z-FRBS), by considering experts' opinions in the form of Z-numbers to deal with inadequate information and modeling experts' knowledge through FRBS, the process of making a decision is performed without using conventional techniques which resulted in a more accurate solving MADM problems. The effectiveness and validity of the proposed method was approved with an illustrative example, sensitivity analysis, and comparison with three other validated method.
1. Abolghasemian, M., Pourghader Chobar, A., AliBakhshi, M., Fakhr, A., & Moradi Pirbalouti, S. (2021). Delay scheduling based on discrete-event simulation for construction projects. Iranian Journal of Operations Research, 12(1), 49-63.
2. Adamopoulos, G. I., & Pappis, C. P. (1996). A fuzzy-linguistic approach to a multi-criteria sequencing problem. European Journal of Operational Research, 92(3), 628-636.
3. Aliev, R. A., & Zeinalova, L. M. (2014). Decision making under Z-information. Human-centric decision-making models for social sciences, 233-252.
4. Almadi, A. I., Al Mamlook, R. E., Almarhabi, Y., Ullah, I., Jamal, A., & Bandara, N. (2022). A fuzzy-logic approach based on driver decision-making behavior modeling and simulation. Sustainability, 14(14), 8874.
5. Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied soft computing, 12(6), 1668-1677.
6. Azadeh, A., & Kokabi, R. (2016). Z-number DEA: A new possibilistic DEA in the context of Z-numbers. Advanced engineering informatics, 30(3), 604-617.
7. Azadeh, A., Saberi, M., Atashbar, N. Z., Chang, E., & Pazhoheshfar, P. (2013, July). Z-AHP: A Z-number extension of fuzzy analytical hierarchy process. In 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) (pp. 141-147). IEEE.
8. Biswas, T. K., Abbasi, A., & Chakrabortty, R. K. (2022). A two-stage VIKOR assisted multi-operator differential evolution approach for Influence Maximization in social networks. Expert Systems with Applications, 192, 116342.
9. Brainy, J. R. V. J., Suganthi, K. D. N., Narayanamoorthy, S., Ilakiya, U., Innab, N., Alshammari, A., Jeon, J. (2023). A perspective study for the assessment of field robots in agriculture: An enhanced fuzzy MADM approach. Computers and Electronics in Agriculture, 214, 108296.
10. Carrera, D. A., & Mayorga, R. V. (2008). Supply chain management: a modular fuzzy inference system approach in supplier selection for new product development. Journal of Intelligent Manufacturing, 19, 1-12.
11. Chae, S. T., Chung, E. S., & Jiang, J. (2022). Robust siting of permeable pavement in highly urbanized watersheds considering climate change using a combination of fuzzy-TOPSIS and the VIKOR method. Water Resources Management, 36(3), 951-969.
12. Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
13. Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
14. Dong, P., Zhang, T., Ju, Y., & Wang, A. (2020). A novel multi-attribute decision-making framework based on Z-RIM: an illustrative example of cloud service selection. Soft Computing, 24, 18233-18247.
15. Ghadimi, P., Dargi, A., & Heavey, C. (2017). Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry. Computers & Industrial Engineering, 105, 12-27.
16. Güneri, A. F., Ertay, T., & Yücel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(12), 14907-14917.
17. Hatamzad, M., Polanco Pinerez, G., & Casselgren, J. (2022). Addressing uncertainty by designing an intelligent fuzzy system to help decision support systems for winter road maintenance. Safety, 8(1), 14.
18. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1997). A rational consensus model in group decision making using linguistic assessments. Fuzzy sets and systems, 88(1), 31-49.
19. Hoseinzada, G. A. (2022, August). Z-Decision Making for the Selection of IT Engineers. In International Conference on Theory and Applications of Fuzzy Systems and Soft Computing (pp. 226-233). Cham: Springer Nature Switzerland.
20. Hosseini, S., Ahmadi Choukolaei, H., Ghasemi, P., Dardaei-beiragh, H., Sherafatianfini, S., & Pourghader Chobar, A. (2022). Evaluating the Performance of Emergency Centers during Coronavirus Epidemic Using Multi‐Criteria Decision‐Making Methods (Case Study: Sari City). Discrete Dynamics in Nature and Society, 2022(1), 6074579.
21. Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., & Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: a real case study in COVID-19 conditions. Journal of applied research on industrial engineering, 8(Special Issue), 1-10.
22. Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2013). A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Applied Soft Computing, 13(10), 4133-4147.
23. Kang, B., Deng, Y., & Sadiq, R. (2018). Total utility of Z-number. Applied Intelligence, 48, 703-729.
24. Kang, D. Wei, Y. Li, and Y. Deng, (2012).“A Method of Converting Z-number to Classical Fuzzy Number,” Journal of Information and Computational Science, vol. 9, no. 3, pp. 703–709, 2012.
25. Kang, D. Wei, Y. Li, and Y. Deng, (2012).“Decision making using Z-numbers under uncertain environment,” Computational Information Systems, vol. 8, no. 7, pp. 2807–2814.
26. Kumar, D., Singh, J., & Singh, O. P. (2013). A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Modelling, 58(11-12), 1679-1695.
27. Mahmoudi, A., Sadi-Nezhad, S., & Makui, A. (2016). A hybrid fuzzy-intelligent system for group multi-attribute decision making. International Journal of Fuzzy Systems, 18, 1117-1130.
28. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
29. Nuriyev, M. (2020). Z-numbers based hybrid MCDM approach for energy resources ranking and selection. International Journal of Energy Economics and Policy, 10(6), 22-30.
30. Peng, H. G., Wang, X. K., Wang, T. L., & Wang, J. Q. (2019). Multi-criteria game model based on the pairwise comparisons of strategies with Z-numbers. Applied Soft Computing, 74, 451-465.
31. Pourghader Chobar, A., Sabk Ara, M., Moradi Pirbalouti, S., Khadem, M., & Bahrami, S. (2022). A multi-objective location-routing problem model for multi-device relief logistics under uncertainty using meta-heuristic algorithm. Journal of Applied Research on Industrial Engineering, 9(3), 354-373.
32. Qiao, D., Shen, K. W., Wang, J. Q., & Wang, T. L. (2020). Multi-criteria PROMETHEE method based on possibility degree with Z-numbers under uncertain linguistic environment. Journal of Ambient Intelligence and humanized computing, 11, 2187-2201.
33. Rahmaty, M., Daneshvar, A., Salahi, F., Ebrahimi, M., & Chobar, A. P. (2022). Customer churn modeling via the grey wolf optimizer and ensemble neural networks. Discrete Dynamics in Nature and Society, 2022(1), 9390768.
34. Rao, C., Gao, M., Wen, J., & Goh, M. (2022). Multi-attribute group decision making method with dual comprehensive clouds under information environment of dual uncertain Z-numbers. Information Sciences, 602, 106-127.
35. Rezaei, J., & Ortt, R. (2013). Supplier segmentation using fuzzy logic. Industrial Marketing Management, 42(4), 507-517.
36. Sari, K. (2017). Modeling of a fuzzy expert system for choosing an appropriate supply chain collaboration strategy. Intelligent Automation & Soft Computing, 1-8.
37. Shen, K. W., Wang, X. K., Qiao, D., & Wang, J. Q. (2019). Extended Z-MABAC method based on regret theory and directed distance for regional circular economy development program selection with Z-information. IEEE Transactions on Fuzzy Systems, 28(8), 1851-1863.
38. Sotoudeh-Anvari, A., & Sadi-Nezhad, S. (2015). A new approach based on the level of reliability of information to determine the relative weights of criteria in fuzzy TOPSIS. International Journal of Applied Decision Sciences, 8(2), 164-178.
39. Tavana, M., & Hatami-Marbini, A. (2011). A group AHP-TOPSIS framework for human spaceflight mission planning at NASA. Expert Systems with Applications, 38(11), 13588-13603.
40. Touti, E., & Chobar, A. P. (2020). Utilization of AHP and MCDM integrated methods in urban project management (A case study for eslamshahr-tehran). International journal of industrial engineering and operational research, 2(1), 16-27.
41. Wang, F., & Mao, J. (2019). Approach to multicriteria group decision making with Z‐numbers based on TOPSIS and Power aggregation operators. Mathematical problems in Engineering, 2019(1), 3014387.
42. Wang, H. (2015). Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making. International Journal of Computational Intelligence Systems, 8(1), 14-33.
43. Yaakob, A. M., & Gegov, A. (2015, August). Fuzzy rule based approach with z-numbers for selection of alternatives using TOPSIS. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
44. Yuan, X., Liebelt, M. J., Shi, P., & Phillips, B. J. (2022). Cognitive decisions based on a rule-based fuzzy system. Information Sciences, 600, 323-341.
45. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.
46. Zadeh, L. A. (2011). A note on Z-numbers. Information sciences, 181(14), 2923-2932.
47. Zahran, B., Ayyoub, B., Abu-Ain, W., Hadi, W., & Al-Hawary, S. (2023). A fuzzy based model for rainfall prediction. International Journal of Data and Network Science, 7(1), 97-106.
48. Zanon, L. G., Arantes, R. F. M., Calache, L. D. D. R., & Carpinetti, L. C. R. (2020). A decision making model based on fuzzy inference to predict the impact of SCOR® indicators on customer perceived value. International Journal of Production Economics, 223, 107520.
49. Zeinalova, L. M. (2014). Expected utility based decision making under Z-information. Intelligent Automation & Soft Computing, 20(3), 419-431.
50. Zhou, X., & Li, Q. (2014). Generalized hesitant fuzzy prioritized einstein aggregation operators and their application in group decision making. International Journal of Fuzzy Systems, 16(3), 303-316.
