《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (8): 2369-2377.DOI: 10.11772/j.issn.1001-9081.2021050872
所属专题: 人工智能
收稿日期:
2021-05-28
修回日期:
2021-09-18
接受日期:
2021-09-22
发布日期:
2021-09-18
出版日期:
2022-08-10
通讯作者:
楚俊峰
作者简介:
刘议聪(1997—),女,河南驻马店人,硕士研究生,主要研究方向:群体决策、社会网络分析;基金资助:
Yicong LIU1, Junfeng CHU1,2(), Yanyan WANG3, Yingming WANG1,2
Received:
2021-05-28
Revised:
2021-09-18
Accepted:
2021-09-22
Online:
2021-09-18
Published:
2022-08-10
Contact:
Junfeng CHU
About author:
LIU Yicong, born in 1997, M. S. candidate. Her research interests include group decision-making, social network analysis.Supported by:
摘要:
针对在群体决策中如何利用专家之间的社会关系和决策专家的有限理性的问题,提出一种信任网络下的TODIM群体决策方法。首先,根据专家讨论次数,在每一次讨论中,每个专家会根据信任接受程度参考信任者的决策矩阵,并通过信息交互和协商修改决策矩阵;其次,当达到设定的专家讨论次数时,计算最终的群体决策矩阵;最后,分别运用信任网络下的TODIM群体决策方法和TODIM群体决策方法计算各方案排序。对所得结果进行对比分析,并对专家讨论次数和信任接受程度进行灵敏度分析。案例分析结果表明,信任网络下的TODIM群体决策方法能充分结合信任网络,保证了决策过程中的多阶段信息交互和反馈过程,并在对比分析和灵敏度分析上优于对比方法。
中图分类号:
刘议聪, 楚俊峰, 王燕燕, 王应明. 信任网络下的TODIM群体决策方法[J]. 计算机应用, 2022, 42(8): 2369-2377.
Yicong LIU, Junfeng CHU, Yanyan WANG, Yingming WANG. TODIM group decision-making method under trust network[J]. Journal of Computer Applications, 2022, 42(8): 2369-2377.
0.00 | 0.32 | 0.18 | 0.26 | 0.26 | 0.26 | |
-3.16 | 0.00 | -2.58 | -1.83 | -1.83 | -1.83 | |
-1.83 | 0.26 | 0.00 | 0.18 | 0.18 | 0.18 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 |
表1 N=0时,信任者e2针对属性c1时方案Ai对Ak的优势度
Tab. 1 Dominance degree of Ai over Ak when trustee e2 targets c1 with N=0
0.00 | 0.32 | 0.18 | 0.26 | 0.26 | 0.26 | |
-3.16 | 0.00 | -2.58 | -1.83 | -1.83 | -1.83 | |
-1.83 | 0.26 | 0.00 | 0.18 | 0.18 | 0.18 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 | |
-2.58 | 0.18 | -1.83 | 0.00 | 0.00 | 0.00 |
0.00 | 0.00 | 0.18 | 0.26 | 0.26 | -1.83 | |
0.00 | 0.00 | 0.18 | 0.26 | 0.26 | -1.83 | |
-1.83 | -1.83 | 0.00 | 0.18 | 0.18 | -2.58 | |
-2.58 | -2.58 | -1.83 | 0.00 | 0.00 | -3.16 | |
-2.58 | -2.58 | -1.83 | 0.00 | 0.00 | -3.16 | |
0.18 | 0.18 | 0.26 | 0.32 | 0.32 | 0.00 |
表2 N=0时,专家e3针对属性c1时方案Ai对Ak的优势度
Tab. 2 Dominance degree of Ai over Ak when expert e3 targets c1 with N=0
0.00 | 0.00 | 0.18 | 0.26 | 0.26 | -1.83 | |
0.00 | 0.00 | 0.18 | 0.26 | 0.26 | -1.83 | |
-1.83 | -1.83 | 0.00 | 0.18 | 0.18 | -2.58 | |
-2.58 | -2.58 | -1.83 | 0.00 | 0.00 | -3.16 | |
-2.58 | -2.58 | -1.83 | 0.00 | 0.00 | -3.16 | |
0.18 | 0.18 | 0.26 | 0.32 | 0.32 | 0.00 |
0.00 | -3.39 | -4.19 | -5.76 | -5.46 | -5.13 | |
-4.51 | 0.00 | -3.82 | -7.44 | -5.11 | -7.61 | |
-4.51 | -2.84 | 0.00 | -6.08 | -3.97 | -5.65 | |
-4.14 | -0.05 | -2.09 | 0.00 | -2.36 | -2.54 | |
-3.87 | -2.26 | -2.23 | -3.75 | 0.00 | -5.22 | |
-3.21 | -0.83 | -4.10 | -1.92 | -3.41 | 0.00 |
表3 N=0时,信任者e2针对方案Ai对Ak的综合优势度
Tab. 3 Comprehensive dominance degree of trustee e2 to Ai over Ak with N=0
0.00 | -3.39 | -4.19 | -5.76 | -5.46 | -5.13 | |
-4.51 | 0.00 | -3.82 | -7.44 | -5.11 | -7.61 | |
-4.51 | -2.84 | 0.00 | -6.08 | -3.97 | -5.65 | |
-4.14 | -0.05 | -2.09 | 0.00 | -2.36 | -2.54 | |
-3.87 | -2.26 | -2.23 | -3.75 | 0.00 | -5.22 | |
-3.21 | -0.83 | -4.10 | -1.92 | -3.41 | 0.00 |
0.00 | 0.82 | -3.93 | -6.93 | -7.42 | -7.28 | |
-3.87 | 0.00 | -6.82 | -8.39 | -8.96 | -9.81 | |
-1.55 | 1.05 | 0.00 | -4.91 | -6.33 | -6.60 | |
-1.19 | -0.88 | -2.02 | 0.00 | -2.33 | -5.27 | |
-1.23 | -0.91 | -3.97 | -3.79 | 0.00 | -4.79 | |
1.01 | 1.53 | -1.43 | -4.94 | -4.33 | 0.00 |
表4 N=1时,专家e3针对方案Ai对Ak的原始综合优势度
Tab. 4 Initial comprehensive dominance degree of expert e3 to Ai over Ak with N=1
0.00 | 0.82 | -3.93 | -6.93 | -7.42 | -7.28 | |
-3.87 | 0.00 | -6.82 | -8.39 | -8.96 | -9.81 | |
-1.55 | 1.05 | 0.00 | -4.91 | -6.33 | -6.60 | |
-1.19 | -0.88 | -2.02 | 0.00 | -2.33 | -5.27 | |
-1.23 | -0.91 | -3.97 | -3.79 | 0.00 | -4.79 | |
1.01 | 1.53 | -1.43 | -4.94 | -4.33 | 0.00 |
0.00 | 0.40 | -3.96 | -6.81 | -7.22 | -7.06 | |
-3.94 | 0.00 | -6.52 | -8.29 | -8.58 | -9.59 | |
-1.84 | 0.66 | 0.00 | -5.03 | -6.10 | -6.50 | |
-1.48 | -0.79 | -2.02 | 0.00 | -2.33 | -4.99 | |
-1.50 | -1.04 | -3.79 | -3.78 | 0.00 | -4.83 | |
0.59 | 1.30 | -1.70 | -4.64 | -4.23 | 0.00 |
表5 N=1时,基于α的专家e3针对方案Ai对Ak的综合优势度
Tab. 5 Comprehensive dominance degree of αbased expert e3 to Ai to Ak with N=1
0.00 | 0.40 | -3.96 | -6.81 | -7.22 | -7.06 | |
-3.94 | 0.00 | -6.52 | -8.29 | -8.58 | -9.59 | |
-1.84 | 0.66 | 0.00 | -5.03 | -6.10 | -6.50 | |
-1.48 | -0.79 | -2.02 | 0.00 | -2.33 | -4.99 | |
-1.50 | -1.04 | -3.79 | -3.78 | 0.00 | -4.83 | |
0.59 | 1.30 | -1.70 | -4.64 | -4.23 | 0.00 |
0.74 | 1.00 | 0.45 | 0.13 | 0.14 | 0.00 |
表6 N=1时,专家e3对方案的总体优势度
Tab. 6 Overall dominance degree of expert e3 to alternatives with N=1
0.74 | 1.00 | 0.45 | 0.13 | 0.14 | 0.00 |
1.00 | 0.73 | 0.94 | 0.61 | 0.03 | 0.00 |
表7 N=5时,Xˉ*5的总体优势度
Tab. 7 Overall dominance degree of Xˉ*5 with N=5
1.00 | 0.73 | 0.94 | 0.61 | 0.03 | 0.00 |
0.83 | 1.00 | 0.72 | 0.20 | 0.41 | 0.00 |
表8 X*0的总体优势度
Tab. 8 Overall dominance degree of X*0
0.83 | 1.00 | 0.72 | 0.20 | 0.41 | 0.00 |
0.80 | 1.00 | 0.66 | 0.00 | 0.25 |
表9 采用TODIM群体决策方法的总体优势度
Tab. 9 Overall dominance degree of TODIM group decision-making method
0.80 | 1.00 | 0.66 | 0.00 | 0.25 |
0.85 | 0.46 | 1.00 | 0.05 | 0.00 |
表10 采用信任网络下交互式TODIM群体决策方法的总体优势度
Tab. 10 Overall dominance degree of interactive TODIM group decision-making under trust network
0.85 | 0.46 | 1.00 | 0.05 | 0.00 |
1 | HOCHBAUM D S, LEVIN A. Methodologies and algorithms for group-rankings decision[J]. Management Science, 2006, 52(9): 1394-1408. 10.1287/mnsc.1060.0540 |
2 | WALLENIUS J, DYER J S, FISHBURN P C, et al. Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead[J]. Management Science, 2008, 54(7): 1336-1349. 10.1287/mnsc.1070.0838 |
3 | MERIGÓ J M, XU Y J, ZENG S Z. Group decision making with distance measures and probabilistic information[J]. Knowledge-Based Systems, 2013, 40: 81-87. 10.1016/j.knosys.2012.11.014 |
4 | BAUCELLS M, SARIN R K. Group decisions with multiple criteria[J]. Management Science, 2003, 49(8): 1105-1118. 10.1287/mnsc.49.8.1105.16400 |
5 | HERRERA-VIEDMA E, ALONSO S, CHICLANA F, et al. A consensus model for group decision making with incomplete fuzzy preference relations[J]. IEEE Transactions on Fuzzy Systems, 2007, 15(5): 863-877. 10.1109/tfuzz.2006.889952 |
6 | 袁宇翔,孙静春.多粒度语言信息的交互式多属性群决策方法及应用[J].运筹与管理, 2019, 28(6): 25-32. |
YUAN Y X, SUN J C. Interactive multi-attribute group decision making with multi- granularity linguistic information and its application[J]. Operations Research and Management Science, 2019, 28(6): 25-32. | |
7 | 杨雷,杨洋.决策要素动态变化的群体决策偏好演化过程[J].系统工程理论与实践, 2014, 34(9): 2302-2311. 10.12011/1000-6788(2014)9-2302 |
YANG L, YANG Y. The preference evolution process of group decision-making with dynamic decision factors[J]. Systems Engineering — Theory and Practice, 2014, 34(9): 2302-2311. 10.12011/1000-6788(2014)9-2302 | |
8 | 孟波,王浣尘,付微.一种交互式多目标满意群决策方法[J].系统工程与电子技术, 1993(9): 27-32. 10.3321/j.issn:1001-506X.1993.09.005 |
MENG B, WANG H C, FU W. An interactive group multiobjective satisfying decision making method[J]. Systems Engineering and Electronics, 1993(9): 27-32. 10.3321/j.issn:1001-506X.1993.09.005 | |
9 | 徐泽水.基于残缺互补判断矩阵的交互式群决策方法[J].控制与决策, 2005, 20(8): 913-916. 10.3321/j.issn:1001-0920.2005.08.015 |
XU Z S. Interactive approach based on incomplete complementary judgement matrices to group decision making[J]. Control and Decision, 2005, 20(8): 913-916. 10.3321/j.issn:1001-0920.2005.08.015 | |
10 | 杜娟,霍佳震.交互式多属性群决策评价方法研究[J].中国管理科学, 2016, 24(11): 120-128. |
DU J, HUO J Z. A study on the interactive evaluation in multiple attribute group decision making[J]. Chinese Journal of Management Science, 2016, 24(11): 120-128. | |
11 | YANG S J H, CHEN I Y L. A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network[J]. International Journal of Human-Computer Studies, 2008, 66(1): 36-50. 10.1016/j.ijhcs.2007.08.005 |
12 | WU J, CHICLANA F, FUJITA H, et al. A visual interaction consensus model for social network group decision making with trust propagation[J]. Knowledge-Based Systems, 2017, 122: 39-50. 10.1016/j.knosys.2017.01.031 |
13 | WU J, CHANG J L, CAO Q W, et al. A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust[J]. Computers and Industrial Engineering, 2019, 127: 853-864. 10.1016/j.cie.2018.11.020 |
14 | LIU Y J, LIANG C Y, CHICLANA F, et al. A trust induced recommendation mechanism for reaching consensus in group decision making[J]. Knowledge-Based Systems, 2017, 119: 221-231. 10.1016/j.knosys.2016.12.014 |
15 | VICTOR P, CORNELIS C, DE COCK M, et al. Gradual trust and distrust in recommender systems[J]. Fuzzy Sets and Systems, 2009, 160(10): 1367-1382. 10.1016/j.fss.2008.11.014 |
16 | GOMES L F A M, LIMA M M P P. TODIM: basic and application to multicriteria ranking of projects with environmental impacts[J]. Foundations of Computing and Decision Sciences, 1991, 16(3/4): 113-127. |
17 | 樊治平,陈发动,张晓.考虑决策者心理行为的区间数多属性决策方法[J].东北大学学报(自然科学版), 2011, 32(1): 136-139. |
FAN Z P, CHEN F D, ZHANG X. Method for interval multiple attribute decision-making considering decision-maker's psychological behavior[J]. Journal of Northeastern University (Natural Science), 2011, 32(1): 136-139. | |
18 | 刘议聪,楚俊峰,王燕燕.基于信任关系的TODIM群体多属性决策方法[J].计算机工程与应用, 2022, 58(3): 187-194. 10.3778/j.issn.1002-8331.2008-0159 |
LIU Y C, CHU J F, WANG Y Y. TODIM group multi-attribute decision-making method based on trust relationship[J]. Computer Engineering and Applications, 2022, 58(3): 187-194. 10.3778/j.issn.1002-8331.2008-0159 | |
19 | WU Q, LIU X W, QIN J D, et al. A linguistic distribution behavioral multi-criteria group decision making model integrating extended generalized TODIM and quantum decision theory[J]. Applied Soft Computing, 2021, 98: No.106757. 10.1016/j.asoc.2020.106757 |
20 | 楚俊峰.考虑社会网络的模糊群决策方法及其商务推荐应用[D].南京:东南大学, 2017: 27-56. |
CHU J F. Fuzzy group decision making method considering social network and application in business recommendation[D]. Nanjing: Southeast University, 2017: 27-56. |
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