Decision-making approach by employing grey incidence analysis and group negotiation

doi:10.21629/JSEE.2019.04.13

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (4): 760-769.doi: 10.21629/JSEE.2019.04.13

• Systems Engineering • Previous Articles Next Articles

Decision-making approach by employing grey incidence analysis and group negotiation

Yong LIU*(), Jiao LI(), Renshi ZHANG()

School of Business, Jiangnan University, Wuxi 214122, China

Received:2018-03-30 Online:2019-08-01 Published:2019-09-01
Contact: Yong LIU E-mail:clly1985528@163.com;930229529@qq.com;6180903007@stu.jiangnan.edu.cn
About author:LIU Yong was born in 1985. He received his B.S., M.S., and Ph.D. degrees in mechanical engineering, and system engineering and management science and engineering from Nanjing University of Aeronautics and Astronautics. Currently, he is an associate professor in Jiangnan University. His research interests include conflict analysis, soft computing, and grey system. E-mail:clly1985528@163.com|LI Jiao was born in 1994. She received her B.S. degree in logistics management from Yangtze University. Currently, she is a graduate student in School of Business, Jiangnan University. Her research interests include conflict coordination, incentive mechanism of supply chain channel and grey system. E-mail:930229529@qq.com|ZHANG Renshi was born in 1998. Currently, she is a senior student in School of Business, Jiangnan University. Her research interests include uncertainly prediction and decision making. E-mail:6180903007@stu.jiangnan.edu.cn
Supported by:
the National Natural Science Foundation of China(71503103);the Humanities and Social Sciences of Education Ministry(17YJC640233);the Jiangsu Province University Philosophy and Social Sciences for Key Research Program(2017ZDIXM034);the Soft Science Foundation of Jiangsu Province(BR2018005);the Natural Science Foundation of Jiangsu Province(BK20150157);the Fundamental Research Funds for the Central Universities(2019JDZD06);the Key Soft Science Foundation of Wuxi(KX-18-B01);This work was supported by the National Natural Science Foundation of China (71503103), the Humanities and Social Sciences of Education Ministry (17YJC640233), the Jiangsu Province University Philosophy and Social Sciences for Key Research Program (2017ZDIXM034), the Soft Science Foundation of Jiangsu Province (BR2018005), the Natural Science Foundation of Jiangsu Province (BK20150157), the Fundamental Research Funds for the Central Universities (2019JDZD06) and the Key Soft Science Foundation of Wuxi (KX-18-B01)

Abstract

Abstract:

For the problems of the consistency ranking of the group decision-making scheme, from the view of group negotiation and system coordination, the grey incidence analysis and Nash bargaining model are used to establish a consistency group decision-making method. First, the concepts of the consensus partial decision-making program and the consensus overall ideal decision-making program are defined, and then a multi-object optimization model is constructed based on the satisfaction maximization of group negotiation and deviation minimization of system coordination to determine the consensus partial decision-making program and the consensus overall ideal decision-making program. Moreover, the grey incidence analysis is exploited to measure the close degrees between them. Finally, a real case of the online product evaluation verifies the validity and rationality of the proposed model.

Key words: group decision-making, group negotiation, system coordination, grey incidence analysis

Yong LIU, Jiao LI, Renshi ZHANG. Decision-making approach by employing grey incidence analysis and group negotiation[J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 760-769.

Figures/Tables 2

References 47

1	LIU S F, YANG Y J, LIN Y. Grey data analysis:method, models and applications. New York: Springer-Verlag, 2016.
2	YAN H B, MA T J. A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. European Journal of Operational Research, 2015, 241 (3): 815- 829.
3	SAFARZADEH S, KHANSEFID S, BARZOKI M R. A group multi-criteria decision-making based on best-worst method. Computers&Industrial Engineering, 2018, 126 (2): 111- 121.
4	YAO O Y, WITOLD P. A new model for intuitionistic fuzzy multi-attributes decision making. European Journal of Operational Research, 2016, 249 (2): 677- 682.
5	WANG Z J. A note on "A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making". European Journal of Operational Research, 2015, 247 (3): 867- 871.
6	WAN S P, LI S Q, DONG J Y. A three-phase method for Pythagorean fuzzy multi-attribute group decision making and application to haze management. Computers&Industrial Engineering, 2018, 123 (9): 348- 363.
7	XU Y J, WEN X W, ZHANG W C. A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Computers&Industrial Engineering, 2018, 116 (2): 113- 129.
8	WAN S P, WANG F, DONG J Y. Additive consistent intervalvalued Atanassov intuitionistic fuzzy preference relation and likelihood comparison algorithm based group decision making. European Journal of Operational Research, 2017, 263 (2): 571- 582.
9	QIN J D, LIU X W, PEDRYCZ W. An extended TODIM multicriteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 2017, 258 (2): 626- 638.
10	WU Q, LIN W H, ZHOU L G, et al. Enhancing multiple attribute group decision making flexibility based on information fusion technique and hesitant Pythagorean fuzzy sets. Computers&Industrial Engineering, 2019, 127 (1): 954- 970.
11	KUMAR S, JOSHI D. Fuzzy ideal based computational approach for group decision making problems. Fuzzy Information and Engineering, 2017, 9 (2): 247- 258. doi: 10.1016/j.fiae.2017.06.008
12	DEEPA J, KUMAR S. Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multicriteria group decision making. European Journal of Operational Research, 2016, 248 (1): 183- 191.
13	BRÄUNING M, HÜLLERMEIER E, KELLER T, et al. Lexicographic preferences for predictive modeling of human decision making:a new machine learning method with an application in accounting. European Journal of Operational Research, 2017, 258 (1): 295- 306.
14	SUN B Z, MA W M, CHEN X T. Variable precision multigranulation rough fuzzy set approach to multiple attribute group decision-making based on λ-similarity relation. Computers&Industrial Engineering, 2018, 126 (12): 345- 355.
15	LIU J P, LIAO X W, YANG J B. A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty. European Journal of Operational Research, 2015, 246 (3): 858- 873.
16	MA L C. A new group ranking approach for ordinal preferences based on group maximum consensus sequences. European Journal of Operational Research, 2016, 251 (1): 171- 181.
17	DONG Q X, COOPER O. A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making. European Journal of Operational Research, 2016, 250 (2): 521- 530.
18	WU J, SUN Q, FUJITA H, et al. An attitudinal consensus degree to control the feedback mechanism in group decision making with different adjustment cost. Knowledge-Based Systems, 2019, 164 (1): 265- 273.
19	ZHANG H J, DONG Y C, CHICLANA F, et al. Consensus efficiency in group decision making:a comprehensive comparative study and its optimal design. European Journal of Operational Research, 2019, 275 (2): 580- 598.
20	DONG Y C, ZHA Q B, ZHANG H J, et al. Consensus reaching in social network group decision making:research paradigms and challenges. Knowledge-Based Systems, 2018, 162 (12): 3- 13.
21	CHENG L C, CHEN Y L, CHIANG Y C. Identifying conflict patterns to reach a consensus-a novel group decision approach. European Journal of Operational Research, 2016, 254 (2): 622- 631.
22	PÉREZ I J, CABRERIZO F J, ALONSO S, et al. On dynamic consensus processes in group decision making problems. Information Sciences, 2018, 459 (8): 20- 35.
23	LIU J P, LIAO X W, HUANG W, et al. A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples. European Journal of Operational Research, 2018, 265 (2): 598- 620.
24	SMRITHI R V, HENRY M. Group decision-making in software architecture:a study on industrial practices. Information and Software Technology, 2018, 101, 51- 63. doi: 10.1016/j.infsof.2018.04.009
25	MANISH AGGARWAL. Hesitant information sets and application in group decision making. Applied Soft Computing Journal, 2019, 75 (2): 120- 129.
26	ABTEEN I M, MOJAN M, ASHKAN H, et al. Hybrid hierarchical fuzzy group decision-making based on information axioms and BWM:prototype design selection. Computers&Industrial Engineering, 2019, 127 (1): 788- 804.
27	LIU Y, FAN Z P, YOU T H, et al. Large group decision-making (LGDM) with the participators from multiple subgroups of stakeholders:a method considering both the collective evaluation and the fairness of the alternative. Computers&Industrial Engineering, 2018, 122 (8): 262- 272.
28	FU C, CHANG W J, XUE M, et al. Multiple criteria group decision making with belief distributions and distributed preference relations. European Journal of Operational Research, 2019, 273 (2): 623- 633.
29	KIMURA K, SAWADAB H, KATAYAMA J. Outcome evaluations in group decision making using authority rule:an electrophysiological study. Neuro Psychologia, 2018, 119, 271- 279.
30	MERIGÓ J M, MARQUÉS D P, ZENG S Z. Subjective and objective information in linguistic multi-criteria group decision making. European Journal of Operational Research, 2016, 248 (2): 522- 531.
31	GAO Q, CHEN X, LI Z Z. The influence of the shared-display configuration on group decision making. International Journal of Industrial Ergonomics, 2016, 51 (2): 59- 67.
32	RAJESH R, RAVI V. Supplier selection in resilient supply chains:a grey relational analysis approach. Journal of Cleaner Production, 2015, 86 (1): 343- 359.
33	WANG P, ZHUA Z Q, WANG Y H. A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Information Sciences, 2016, 345 (6): 27- 45.
34	ZHU J J, ZHANG S T, CHEN Y, et al. A hierarchical clustering approach based on three-dimensional gray relational analysis for clustering a large group of decision makers with double information. Group Decision and Negotiation, 2016, 25 (2): 325- 354. doi: 10.1007/s10726-015-9444-8
35	HASHEMI A H, KARIMI A, TAVANA M. An integrated green supplier selection approach with analytic network process and improved grey relational analysis. International Journal of Production Economics, 2015, 159 (1): 178- 191.
36	TANG H X. A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster-Shafer theory of evidence. Applied Soft Computing, 2015, 31 (6): 317- 325.
37	LI P, LIU J, LIU S F, et al. Grey target method for intuitionistic fuzzy decision making based on grey incidence analysis. The Journal of Grey System, 2016, 28 (4): 96- 109.
38	WANG H H, ZHU J J, FANG Z G. Aggregation of multi-stage linguistic evaluation information based on grey incidence degree. Control and Decision, 2013, 28 (1): 109- 114.
39	YAN S L, LIU S F, LIU J F, et al. Dynamic grey target decision making method with grey numbers based on existing state and future development trend of alternatives. Journal of Intelligent&Fuzzy Systems, 2015, 28 (5): 2159- 2168.
40	LIU Y, FORREST J, LIU S F. Dynamic grey incidence group decision making methodology based on interval two-tuple linguistic information processing and application. Systems Engineering and Electronics, 2013, 35 (9): 1915- 1922.
41	LIU H C, LI P, YOU J X, et al. A novel approach for FMEA:combination of interval 2-tuple linguistic variables and gray relational analysis. Quality and Reliability Engineering International, 2015, 31 (5): 761- 772. doi: 10.1002/qre.1633
42	WANG H H, ZHU J J, FANG Z G. Group aggregation method on large-scale linguistic evaluation information based on grey cluster. Control and Decision, 2012, 27 (2): 271- 280.
43	MANZARDO A, REN J Z, MAZZI A, et al. A grey-based group decision-making methodology for the selection of hydrogen technologies in life cycle sustainability perspective. International Journal of Hydrogen Energy, 2012, 37 (23): 17663- 17670. doi: 10.1016/j.ijhydene.2012.08.137
44	WINSTON W L. Operations research application and algorithms. Beijing: Tsinghua University Press, 2006.
45	SAMVEDIA A, JAINA V, CHAN F T S. An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. International Journal of Production Research, 2012, 50 (12): 3211- 3221. doi: 10.1080/00207543.2011.560906
46	SUN G D, GUAN X, YI X, et al. Grey relational analysis between hesitant fuzzy sets with applications to pattern recognition. Expert Systems with Applications, 2018, 92 (3): 521- 532.
47	WU Z, XU J. A concise consensus support model for group decision making with reciprocal preference relations based on deviation measures. Fuzzy Sets&Systems, 2012, 206 (11): 58- 73.

Literature	Ranking result
[18]	$x_1 \succ x_5 \succ x_3 \succ x_2 \succ x_6 \succ x_4 $
[45]	$x_1 \succ x_5 \succ x_2 \succ x_3 \succ x_4 \succ x_6 $
[46]	$x_1 \succ x_5 \succ x_3 \succ x_2 \succ x_4 \succ x_6 $
[47]	$x_1 \succ x_5 \succ x_2 \succ x_3 \succ x_6 \succ x_4 $
Proposed model	$x_1 \succ x_5 \succ x_2 \succ x_3 \succ x_6 \succ x_4 $

[1]	Weixue DIAO, Yong LIU. Two-level consensus modeling with utility and cost constraints [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 716-726.
[2]	Jin Dai, Yi Yan, and Yuhong He. Grey incidence clustering method based on multidimensional dynamic time warping distance [J]. Systems Engineering and Electronics, 2017, 28(5): 946-954.
[3]	Bingsheng Liu, Sijia Guo, Kaijing Yan, Ling Li, and Xueqing Wang. Double weight determination method for experts of complex multi-attribute large-group decision-making in interval-valued intuitionistic fuzzy environment [J]. Systems Engineering and Electronics, 2017, 28(1): 88-.
[4]	Ke Zhang, Yintao Zhang, and Pinpin Qu. Comprehensive multivariate grey incidence degree based on principal component analysis [J]. Journal of Systems Engineering and Electronics, 2014, 25(5): 840-847.
[5]	Tao Geng, An Zhang, and Guangshan Lu. Consensus intuitionistic fuzzy group decision-making method for aircraft cockpit display and control system evaluation [J]. Journal of Systems Engineering and Electronics, 2013, 24(4): 634-641.
[6]	Xuan Zhou, Fengming Zhang, Xiaobin Hui, and Kewu Li. Group decision-making method based on entropy and experts cluster analysis [J]. Journal of Systems Engineering and Electronics, 2011, 22(3): 468-472.
[7]	Wu Li, Guanqi Guo, Chaoyuan Yue, and Yong Zhao. Dynamic programming methodology for multi-criteria group decision-making under ordinal preferences [J]. Journal of Systems Engineering and Electronics, 2010, 21(6): 975-980.
[8]	Shengbao Yao and Wan’an Cui. Method for multi-attribute group decision-making based on the compromise weights [J]. Journal of Systems Engineering and Electronics, 2010, 21(4): 591-597.
[9]	Wang Jianqiang & Chen Xiaohong. Multi-criteria linguistic interval group decision-making approach [J]. Journal of Systems Engineering and Electronics, 2008, 19(5): 934-938.

Decision-making approach by employing grey incidence analysis and group negotiation

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 2

References 47

Related Articles 9

Recommended Articles

Metrics

Comments