Reliability allocation is one of the most important factors to consider when determining the reliability and competitiveness of a product. The feasibility-of-objectives (FOO) technique has become the current standard for assessing reliability designs for military mechanical–electrical systems. However, the FOO method has several drawbacks: For instance, it requires that the value of reliability allocation factors is single linguistic variables, and it does not consider the ordered weight of reliability allocation factors, but simply multiplies the ISPE (Complexity (I), State of Art (S), Performance Time (P), and Environment (E)) values one by one. This can lead to erroneous results. To address these issues, this paper combines the fuzzy allocation method with the maximum entropy ordered weighted averaging method (ME-OWA) to achieve a flexible allocation of system reliability. To verify the effectiveness of the proposed method, the CNC machine tool is taken as an example. The FOO method and the fuzzy allocation method and the proposed method were used to assign reliability to the eight subsystems of a CNC machine tool, and the results were compared to draw conclusions: The proposed method is more flexible and accurate for reliability allocation.
Advisory. Group of Reliability of Electronic Equipment (AGREE) (1957). Reliability of military electronic equipment. Office of the Assistant Secretary of Defense Research and Engineering, Washington, DC.
2.
W.H.Alven, (1964), Reliability engineering: Prepared by ARINC research corporation. Englewood Cliff, NJ: Prentice Hall, Inc.
3.
V.J.Bracha, The methods of reliability engineering, Machine Design (1964), 70–76.
4.
E.D.Karmiol, Reliability apportionment. Preliminary Report EIAM-5, Task II, General Electric, Schenectady, NY, (1965), pp. 10–22.
5.
R.T.Anderson, (1976), Reliability design handbook. Chicago: ITT Research Institute.
6.
Department of Defense of USA (1988), MIL-HDBK-338B, Electronic design reliability handbook (pp. 6/13–6/16).
7.
K.Smedley, Reliability analysis for LEB ring magnet power system in SSC, IEEE Transactions on Nuclear Science39(4) (1992), 1170–1174.
8.
R.R.Yager, On ordered weighted averaging aggregation operators in multi criteria decision making, IEEE Transactions on Systems Man and Cybernetics18(1) (1988), 183–190.
9.
R.Fuller and P.Majlender, An analytic approach for obtaining maximal entropy OWA operator weights, Fuzzy Sets and Systems124(1) (2001), 53–57.
10.
C.H.Cheng and J.R.Chang, MCDM aggregation model using situational MEOWA and ME-OWGA operators, Fuzziness and Knowledge-Based Systems14(4) (2006), 421–443.
11.
K.H.Chang, C.H.Cheng and Y.C.Chang, Reliability assessment of an aircraft propulsion system using IFS and OWA tree, Engineering Optimization40(10) (2008), 907–921.
12.
V.Torra, Hesitant fuzzy sets, Int J Intell Syst25(2010), 529–539.
13.
R.M.Rodriguez, L.Martinez and F.Herrera, Hesitant fuzzy linguistic term sets for decision making, IEEE Trans Fuzzy Syst20 (2012), 109–119.
14.
I.Beg and T.Rashid, TOPSIS for hesitant fuzzy linguistic term sets, Int J Intell Syst28 (2013), 1162–1171.
15.
K.H.Chang, Enhanced assessment of a supplier selection problem by integration of soft sets and hesitant fuzzy linguistic term set, Proc Inst Mech Eng B: J Eng Manuf229 (2015), 1635–1644.
16.
H.B.Liu and R.M.Rodriguez, A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multi criteria decision making, Inf Sci258 (2014), 220–238.
17.
L.Wang, M.Ni and L.Zhu, Correlation measures of dual hesitant fuzzy sets, J Appl Math2013 (2013) 12 (Article ID 593739).
18.
C.P.Wei, N.Zhao and X.J.Tang, Operators and comparisons of hesitant fuzzy linguistic term sets, IEEE Trans Fuzzy Syst22 (2014), 575–585.
19.
V.Ebrahimipour, S.M.Asadzadeh and A.Aadeh, An emotional learning-based fuzzy inference system for improvement of system reliability evaluation in redundancy allocation problem, Int J Adv Manuf Technol66 (2013), 1657–1672.
20.
H.Garg and S.P.Sharma, Multi-objective reliability-redundancy allocation problem using particle swarm optimization, Comput Ind Eng64 (2013), 247–255.
21.
V.Sriramdas, S.K.Chaturvedi and H.Gargama, Fuzzy arithmetic based reliability allocation approach during early design and development, Expert Syst Appl41 (2014), 3444–3449.
22.
F.Wang, X.Li and X.Chen, Hesitant fuzzy soft set and its applications in multi criteria decision making, J Appl Math2014 (2014) 10 (Article ID643785).
23.
J.Q.Wang, J.Wang, Q.H.Chen, H.Y.Zhang and X.H.Chen, An outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets, Inf Sci280 (2014), 338–351.
24.
M.O’Hagan, Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In Proceedings of 22nd annual IEEE Asilomar conference on signals, systems, computers, Pacific Grove, CA, Piscataway (1988), (pp. 681–689). NJ: IEEE.
25.
Y.M.Wang, J.B.Yang, D.L.Xu and K.S.Chin, On the centroids of fuzzy numbers, Fuzzy Sets Syst157 (2006), 919–926.
26.
Q.Cheng, B.B.Qi, Z.F.Liu, C.X.Zhang, D.Y. andXue, An accuracy degradation analysis of ball screw mechanism considering time-varying motion and loading working conditions, Mechanism and Machine Theory134 (2019), 1–23.
27.
L.Wang, J.J.Peng and J.Q.Wang, A multi-criteria decision-making framework for risk ranking of energy performance contracting project under picture fuzzy environment, Journal of Cleaner Production191 (2018), 105–118.
28.
S.Q.Zhang, H.Gao, G.W.Wei, Y.Wei and C.Wei, Evaluation Based on Distance from Average Solution Method for Multiple Criteria Group Decision Making under Picture 2-Tuple Linguistic Environment, Mathematics7(3) (2019), 243.
29.
R.Wang, J.Wang, H.Gao and G.W.WEI, Methods for MADM with Picture Fuzzy Muirhead Mean Operators and Their Application for Evaluating the Financial Investment Risk, Symmetry11(1) (2019), 6.
30.
G.W.Wei, C.Wei, J.Wang, H.Gao and Y.Wei, Some q-rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization, International Journal of Intelligent Systems34(1) (2019), 50–81.
31.
G.W.Wei and Z.P.Zhang, Some Single-Valued Neutrosophic Bonferroni Power Aggregation Operators in Multiple Attribute Decision Making, Journal of Ambient Intelligence and Humanized Computing10(3) (2019), 863–882.
32.
S.M.Peng, Study on enterprise risk management assessment based on picture fuzzy multiple attribute decision-making method, Journal of Intelligent & Fuzzy Systems33 (2017), 3451–3458.
33.
Z.S.Chen, L.Martinez, k.-S.Chin, Two-stage aggregation paradigm for HFLTS possibility distributions: A hierarchical clustering perspective, Expert Systems with applications104 (2018), 43–66.
34.
W.Y.Yu, Z.Zhang, Q.Y.Zhong and L.Sun, Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets, Computers & Industrial Engineering114 (2017), 316–328.
35.
Z.Zhang, X.Y.Kou and Q.X.Dong, Additive consistency analysis and improvement for hesitant fuzzy preference relations, Expert Systems with applications98 (2018), 118–128.
36.
Z.Zhang and C.H.Guo, Deriving priority weights from intuitionistic multiplicative preference relations under group decision-making settings, Journal of the Operational Research Society68(12) (2017), 1582–1599.
37.
Z.Zhang, C.H.Guo and L.Martinez, IEEE Transactions on Systems Man Cybernetics-Systems, 47(11) (2018), 3063–3076.
38.
Z.Zhang, X.Y.Kou, W.Y.Yu and C.H.Guo, On priority weights and consistency for incomplete hesitant fuzzy preference relations, Knowledge-Based Systems143 (2018), 115–126.
