A Z-number and MABAC method based on reliability analysis and evaluation of product design concept

Tan, Yuhang; Chen, Zhe; Wang, Baoping; Ma, Qing; Wei, Jianing

doi:10.17531/ein/178304

Artykuł - szczegóły

Tytuł artykułu

A Z-number and MABAC method based on reliability analysis and evaluation of product design concept

Autorzy

Tan Yuhang , Chen Zhe , Wang Baoping , Ma Qing , Wei Jianing

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.17531/ein/178304

Warianty tytułu

Języki publikacji

Abstrakty

Modular design is a significant method for complicated product development. In the context of modular design, involving users in concept assessment boosts a product's appeal but also introduces decision uncertainty and unreliability. As a solution, this paper proposed a hybrid method by integrating expert consensus modeling, attribute weighting, Z-number, and the Multi-Attribute Border Approximation Area Comparison (MABAC) method. Initially, a consensus model is established using consistency theory to determine expert weights, and attribute priorities are determined through the entropy weighting method. Subsequently, the Z-number-based MABAC method ranks the alternatives, determiningthe optimal solution among them. Using an automated outdoor cleaning vehicle as an example, the proposed method is compared to other techniques. The sensitivity analysis and the comparisons show that the proposed method improves the reliability and objective of the decision-making process.

Słowa kluczowe

product design concept evaluation Z-number MABAC reliability modular design

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2024

Tom

Vol. 26, no. 1

Strony

art. no. 178304

Opis fizyczny

Bibliogr. 62 poz., rys., tab. wykr.

Twórcy

autor

Tan Yuhang

1269042051@qq.com

Shandong Jiaotong University, Jinan 250357, China

autor

Chen Zhe

czsdjtu@outlook.com

Shandong Jiaotong University, Jinan 250357, China

https://orcid.org/0000-0002-4413-1606

autor

Wang Baoping

wangbaoping@sdjtu.edu.cn

Shandong Jiaotong University, Jinan 250357, China

autor

Ma Qing

3533298128@qq.com

Shandong Jiaotong University, Jinan 250357, China

autor

Wei Jianing

954988323@qq.com

Shandong Jiaotong University, Jinan 250357, China

Bibliografia

1. Rogalewicz M, Kujawińska A, Feledziak A. Ensuring the reliability and reduction of quality control costs by minimizing process variability. Maintenance & Reliability/Eksploatacja i Niezawodnosc. 2023;25(2).https://doi.org/10.17531/ein/162626
2. Summers GJ, Scherpereel CM. Flawed decision models and flexibility in product development. Journal of Engineering and Technology Management. 2023;67:101728.
3. Li X-Q, Song L-K, Choy Y-S, Bai G-C. Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning. Philosophical Transactions of the Royal Society A. 2023;381(2260):20220384.
4. Chen B, Chen Y, Zhou H, Bai X, Li B, Guo X. A Bi-level gaming programming for regional integrated energy system considering the users’ reliability incentive. Reliability Engineering & System Safety. 2023;229:108839.
5. Li X-Q, Song L-K, Choy Y-S, Bai G-C. Multivariate ensembles-based hierarchical linkage strategy for system reliability evaluation of aeroengine cooling blades. Aerospace Science and Technology. 2023;138:108325.
6. Mashal I. Evaluation and assessment of smart grid reliability using fuzzy multi-criteria decision-making. Applications of fuzzy logic in planning and operation of smart grids: Springer; 2021. p. 67-104.https://doi.org/10.1007/978-3-030-64627-1_3
7. Lima MB, Kubota FI. A modular product design framework for the home appliance industry. The International Journal of AdvancedManufacturing Technology. 2022;120(3):2311-30.https://doi.org/10.1007/s00170-022-08896-9
8. Ma Q, Sun H, Chen Z, Tan Y. A novel MCDM approach for design concept evaluation based on interval-valued picture fuzzy sets. PLOS ONE. 2023;18(11):e0294596.
9. Darko AP, Antwi CO, Asamoah KO, Opoku-Mensah E, Ren J. A probabilistic reliable linguistic PROBID method for selecting electronic mental health platforms considering users’ bounded rationality. Engineering Applications of Artificial Intelligence. 2023;125:106716.
10. Salas P, Venkataraman S. Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure. Structural and Multidisciplinary Optimization. 2009;37:541-55.https://doi.org/10.1007/s00158-008-0259-y
11. Chen Z, Zhong P, Liu M, Sun H, Shang K. A novel hybrid approach for product concept evaluation based on rough numbers, shannon entropy and TOPSIS-PSI. Journal of Intelligent & Fuzzy Systems 2021;40:12087-99.https://doi.org/10.3233/JIFS-210184
12. Kimita K, Shimomura Y, Arai T. Evaluation of customer satisfaction for PSS design. Journal of Manufacturing Technology Management. 2009;20(5):654-73.https://doi.org/10.1108/17410380910961046
13. Kwok CP, Tang YM. A fuzzy MCDM approach to support customer-centric innovation in virtual reality (VR) metaverse headset design. Advanced Engineering Informatics. 2023;56:101910.
14. Mirtalaie MA, Hussain OK, Chang E, Hussain FK. A decision support framework for identifying novel ideas in new product development from cross-domain analysis. Information Systems. 2017;69:59-80.https://doi.org/10.1016/j.is.2017.04.003
15. Cristiano JJ, Liker JK, White CCI. Key factors in the successful application of quality function deployment (QFD). IEEE Transactions on Engineering Management. 2001;48(1):81-95.https://doi.org/10.1109/17.913168
16. Chen Z, Zhong P, Liu M, Ma Q, Si G. A novel integrated MADM method for design concept evaluation. Scientific Reports. 2022;12(1):15885.https://doi.org/10.1038/s41598-022-20044-7
17. Qi J, Hu J, Peng Y. New design concept evaluation method involving customer preferences based on rough distance to redefined ideal solution. Computers & Industrial Engineering. 2020;147:106677.
18. Zoghi M, Rostami G, Khoshand A, Motalleb F. Material selection in design for deconstruction using Kano model, fuzzy-AHP and TOPSIS methodology. Waste Management & Research. 2021;40(4):410-9.https://doi.org/10.1177/0734242X211013904
19. Zadeh LA. Fuzzy sets. Information and Control. 1965;8(3):338-53.https://doi.org/10.1016/S0019-9958(65)90241-X
20. Alshraideh M, Ababneh S, Gunay EE, Al-Araidah O. A fuzzy-TOPSIS model for maintenance outsourcing considering the quality of submitted tender documents. Eksploatacja i Niezawodność. 2021;23(3).https://doi.org/10.17531/ein.2021.3.5
21. Li J, DuAn R. Dynamic diagnostic strategy based on reliability analysis and distance-based VIKOR with heterogeneous information. Eksploatacja i Niezawodność. 2018;20(4).https://doi.org/10.17531/ein.2018.4.12
22. Jiang J, Ren M, Wang J. Interval number multi-attribute decision-making method based on TOPSIS. Alexandria Engineering Journal. 2022;61(7):5059-64.https://doi.org/10.1016/j.aej.2021.09.031
23. Zhu G-N, Hu J, Ren H. A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Applied Soft Computing. 2020;91:106228.
24. Zhou M, Zheng Y-Q, Chen Y-W, Cheng B-Y, Herrera-Viedma E, Wu J. A large-scale group consensus reaching approach considering self-confidence with two-tuple linguistic trust/distrust relationship and its application in life cycle sustainability assessment. Information Fusion. 2023;94:181-99.https://doi.org/10.1016/j.inffus.2023.01.026
25. Liu S, Yu W, Chan FTS, Niu B. A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. International Journal of Intelligent Systems. 2021;36(2):1015-52.https://doi.org/10.1002/int.22329
26. Singh S, Ganie AH. On a new picture fuzzy correlation coefficient with its applications to pattern recognition and identification of an investment sector. Computational and Applied Mathematics. 2021;41(1):8.https://doi.org/10.1007/s40314-021-01699-w
27. Jiang Z, Wei G, Guo Y. Picture fuzzy MABAC method based on prospect theory for multiple attribute group decision making and its application to suppliers selection. Journal of Intelligent & Fuzzy Systems. 2022;42:3405-15.https://doi.org/10.3233/JIFS-211359
28. Thurston DL, Carnahan JV. Fuzzy Ratings and Utility Analysis in Preliminary Design Evaluation of Multiple Attributes. Journal of Mechanical Design. 1992;114(4):648-58.https://doi.org/10.1115/1.2917056
29. Alekperov R, editor Using Fuzzy Z -Numbers When Processing Flexible Queries. Intelligent and Fuzzy Systems; 2022 2022//; Cham: Springer International Publishing.https://doi.org/10.1007/978-3-031-09173-5_52
30. Shidpour H, Da Cunha C, Bernard A. Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory. Expert Systems with Applications. 2016;64:633-44.https://doi.org/10.1016/j.eswa.2016.08.022
31. Geng X, Chu X, Zhang Z. A new integrated design concept evaluation approach based on vague sets. Expert Systems with Applications. 2010;37(9):6629-38.https://doi.org/10.1016/j.eswa.2010.03.058
32. Jing L, Yao J, Gao F, Li J, Peng X, Jiang S. A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution. Advanced Engineering Informatics. 2021;48:101284.
33. Jabeen K, Ullah K, Akram M, Haleemzai I. Interval Valued Picture Fuzzy Aczel–Alsina Aggregation Operators and Their Application by Using the Multiattribute Decision Making Problem. Journal of Mathematics. 2023;2023:1707867.
34. Xu W, Shang X, Wang J, Xu Y. Multi-Attribute Decision-Making Based on Interval-Valued q-Rung Dual Hesitant Uncertain Linguistic Sets. IEEE Access. 2020;8:26792-813.https://doi.org/10.1109/ACCESS.2020.2968381
35. Chen Z, Zhong P, Liu M, Ma Q, Si G. An integrated expert weight determination method for design concept evaluation. Scientific Reports. 2022;12(1):6358.https://doi.org/10.1038/s41598-022-10333-6
36. Zadeh LA. A Note on Z-numbers. Information Sciences. 2011;181(14):2923-32.https://doi.org/10.1016/j.ins.2011.02.022
37. Banerjee R, Pal SK. Z*-numbers: Augmented Z-numbers for machine-subjectivity representation. Information Sciences. 2015;323:143-78.https://doi.org/10.1016/j.ins.2015.06.026
38. Allahviranloo T, Ezadi S. Z-Advanced numbers processes. Information Sciences. 2019;480:130-43.https://doi.org/10.1016/j.ins.2018.12.012
39. Wu M-C, Mao J-J, Yao A-T, Wu T. The novel entropy measurements of Z +-numbers and their application on multi-attribute decision making problem. Journal of Intelligent & Fuzzy Systems. 2021;40:131-48.https://doi.org/10.3233/JIFS-190300
40. Shen K-W, Wang J-Q, Wang T-L. The arithmetic of multidimensional Z-number. Journal of Intelligent & Fuzzy Systems. 2019;36:1647-61.https://doi.org/10.3233/JIFS-18927
41. Banerjee R, Pal SK, Pal JK. A Decade of the Z-Numbers. IEEE Transactions on Fuzzy Systems. 2022;30(8):2800-12.https://doi.org/10.1109/TFUZZ.2021.3094657
42. Aliev RA, Guirimov BG, Huseynov OH, Aliyev RR. Z-relation equation-based decision making. Expert Systems with Applications. 2021;184:115387.
43. Li Y, Rao C, Goh M, Xiao X. Novel multi-attribute decision-making method based on Z-number grey relational degree. Soft Computing. 2022;26(24):13333-47.https://doi.org/10.1007/s00500-022-07487-w
44. Kang B, Wei D, Li Y, Deng Y. A method of converting Z-number to classical fuzzy number. Journal of Information and Computational Science. 2012;9:703-9.
45. Kang B, Deng Y, Sadiq R. Total utility of Z-number. Applied Intelligence. 2018;48(3):703-29.https://doi.org/10.1007/s10489-017-1001-5
46. Huang G, Xiao L, Pedrycz W, Pamucar D, Zhang G, Martínez L. Design alternative assessment and selection: A novel Z-cloud rough number-based BWM-MABAC model. Information Sciences. 2022;603:149-89.https://doi.org/10.1016/j.ins.2022.04.040
47. Qi J, Hu J, Huang H, Peng Y. New customer-oriented design concept evaluation by using improved Z-number-based multi-criteria decision-making method. Advanced Engineering Informatics. 2022;53:101683.
48. Zhu G-N. Design concept evaluation considering information reliability, uncertainty, and subjectivity: An integrated rough-Z-number-enhanced MCGDM methodology. Advanced Engineering Informatics. 2022;54:101796.
49. Liu Q, Chen J, Wang W, Qin Q. Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method. Applied Sciences [Internet]. 2021; 11(16).https://doi.org/10.3390/app11167400
50. Aydoğan S, Günay EE, Akay D, Okudan Kremer GE. Concept design evaluation by using Z-axiomatic design. Computers in Industry. 2020;122:103278.
51. Torkayesh AE, Tirkolaee EB, Bahrini A, Pamucar D, Khakbaz A. A Systematic Literature Review of MABAC Method and Applications:An Outlook for Sustainability and Circularity. Informatica. 2023;34:415-48.https://doi.org/10.15388/23-INFOR511
52. Mandal U, Seikh MR. Interval-valued spherical fuzzy MABAC method based on Dombi aggregation operators with unknown attribute weights to select plastic waste management process. Applied Soft Computing. 2023;145:110516.
53. Salman M, Chauhan R, Singh T, Prabakaran R, Kim SC. Experimental investigation and optimization of dimple-roughened impinging jet solar air collector using a novel AHP-MABAC approach. Environmental Science and Pollution Research. 2023;30(13):36259-75. https://doi.org/10.1007/s11356-022-24765-0
54. Wang T, Wu X, Garg H, Liu Q, Chen G. A prospect theory-based MABAC algorithm with novel similarity measures and interactional operations for picture fuzzy sets and its applications. Engineering Applications of Artificial Intelligence. 2023;126:106787.
55. Giachetti RE, Young RE. Analysis of the error in the standard approximation used for multiplication of triangular and trapezoidal fuzzy numbers and the development of a new approximation. Fuzzy Sets and Systems. 1997;91(1):1-13.https://doi.org/10.1016/S0165-0114(96)00118-2
56. Halidini D, Xhafaj E, Xhafaj A, Halidini E. Ranking the Most Important Attributes of using Google Classroom in online Teaching for Albanian Universities: A Fuzzy AHP Method with Triangular Fuzzy Numbers and Trapezoidal Fuzzy Numbers. 2021.https://doi.org/10.25046/aj060134
57. Dombi J, Jónás T. Ranking trapezoidal fuzzy numbers using a parametric relation pair. Fuzzy Sets and Systems. 2020;399:20-43.https://doi.org/10.1016/j.fss.2020.04.014
58. Anand MC, Bharatraj J. Theory of Triangular Fuzzy Number2017.
59. Banerjee S, Kumar T. Arithmetic Operations on Generalized Trapezoidal Fuzzy Number and its Applications. Turkish Journal of Fuzzy Systems. 2012;3:1309-190.
60. Hu Z, Lin J. An integrated multicriteria group decision making methodology for property concealment risk assessment under Z-number environment. Expert Systems with Applications. 2022;205:117369.
61. Wang S, Li Z, He C, Liu D, Zou G. An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm. Symmetry [Internet]. 2022; 14(1).https://doi.org/10.3390/sym14010048
62. Sun J, Chai N, Pi G, Zhang Z, Fan B. Modularization of Product Service System Based on Functional Requirement. Procedia CIRP.2017;64:301-5.https://doi.org/10.1016/j.procir.2017.03.038

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Bibliografia

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