Abstract
The technique for order preference by similarity to ideal solution (TOPSIS) has become a popular multi-criteria decision making (MCDM) technique, since it has a comprehensible theoretical structure and is able to provide an exact model for decision making. For the use of TOPSIS in group decisions, the common approaches in aggregating individual decision makers’ judgments are the geometric and the arithmetic mean methods, although these are too intuitive and do not consider either preference levels or preference priorities among alternatives for individual decision makers. In this paper, a TOPSIS group decision aggregation model is proposed in which the construction consists of three stages: (1) The weight differences are calculated first as the degrees of preferences among different alternatives for each decision maker; (2) The alternative priorities are then derived, and the highest one can be denoted as the degree to which a decision maker wants his most favorite alternative to be chosen; (3) The group ideal solutions approach in TOPSIS is used for the aggregation of similarities obtained from different decision makers. A comparative analysis is performed, and the proposed aggregation model seems to be more satisfactory than the traditional aggregation model for solving compromise-oriented decision problems.
Similar content being viewed by others
Abbreviations
- n :
-
the number of alternatives
- p :
-
the number of decision makers
- D :
-
decision matrix
- C ki :
-
relative closeness of alternative i by decision maker k
- α kij :
-
weighted difference between alternatives i and j by decision maker k
- α k :
-
weighted difference for decision maker k
- φ ki :
-
preference ranking of alternative i by decision maker k
- φ Gi :
-
preference ranking of alternative i by the group
- β ki :
-
preference priorities of alternative i by decision maker k
- \({\beta _i^{(G)}}\) :
-
preference priorities of alternative i by the group
- G :
-
group decision matrix
- \({C_{ki}^{(w)}}\) :
-
revised relative closeness of alternative i by decision maker k
- G′:
-
weighted group decision matrix
- v ki :
-
weighted relative closeness of alternative i by decision maker k
- \({A_{G}^{\ast}}\) :
-
group positive ideal solution set
- \({A_G ^{-}}\) :
-
group negative ideal solution set
- \({v_{Gk}^{\ast}}\) :
-
best scores with respect to decision maker k by the group
- \({v_{Gk}^{-}}\) :
-
worst scores with respect to decision maker k by the group
- \({S_{Gi}^{\ast}}\) :
-
separation of alternative i with respect to group positive ideal solution by the group
- \({S_{Gi}^{-}}\) :
-
separation of alternative i with respect to group negative ideal solution by the group
- C Gi :
-
relative closeness of alternative i by the group
- η ki :
-
differences of alternative i between decision maker k and the group
- \({\eta _{ki}^{\prime}}\) :
-
normalized differences of alternative i between decision maker k and the
- \({S_{Gi}^{-}}\) :
-
group
- η k :
-
mean differentiation coefficient by decision maker k
- ζ ki :
-
rank differences by alternative i between decision maker k and the group
- a :
-
smoothing constant
- r k :
-
revised rank correlation by decision maker k
- a ki :
-
weight by smoothing constant of alternative i by decision maker k
- \({r_{k}^{(d)}}\) :
-
decision’s rank correlation by decision maker k
- τ k :
-
satisfaction index by decision maker k
- τ :
-
satisfaction index by the group
References
Abo-Sinna MA, Amer AH (2005) Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems. Appl Math Comput 162(1): 243–256
Chen C-T (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1): 1–9
Chen M-F, Tzeng G-H (2004) Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Math Comput Modell 40: 1473–1490
Chu TC (2002) Facility location selection using fuzzy TOPSIS under group decision. Int J Uncertain Fuzz Knowl Based Syst 10(6): 687–701
Deng H, Yeh C-H, Willis RJ (2000) Inter-company comparison using modified TOPSIS with objective weights. Comput Oper Res 27(10): 963–973
Fang W-G, Zhou H (2007) A multi-attribute group decision-making method approaching to group ideal solution. In: Proceeding of 2007 IEEE international conference on frey systems and intelligent services. Nanjing, China, pp 815–819
Huang Y-S, Liao J-T, Lin Z-L (2009) A study on aggregation of group decisions. Syst Res Behav Sci 26(4): 445–454
Hwang CL, Yoon KP (1981) Multiple attribute decision making methods and applications: a state-of-the-art survey New York. Springer, Berlin
Janic M (2003) Multicriteria evaluation of high-speed rail, transrapid maglev and air passenger transport in Europe. Trans Plan Technol 26(6): 491–512
Lin H-T, Chang W-L (2008) Order selection and pricing methods using flexible quantity and fuzzy approach for buyer evaluation. Eur J Oper Res 187(2): 415–428
Lin M-C, Wang C-C, Chen M-S, Chang CA (2008a) Using AHP and TOPSIS approaches in customer-driven product design process. Comput Indus 59(1): 17–31
Lin Y-H, Lee P-C, Chang TP, Ting H-I (2008b) Multi-attribute group decision making model under the condition of uncertain information. Automat Construct 17(6): 792–797
Matsatsinis NF, Grigoroudis E, Samaras A (2005) Aggregation and disaggregation of preferences for collective decision-making. Group Decis Negot 14(3): 217–232
Olson DL (2004) Comparison of weights in TOPSIS models. Math Comput Modell 40(7-8): 721–727
Parkan C, Wu M-L (1998) Process selection with multiple objective and subjective attributes. Product Plan Control 9(2): 189–200
Pochampally KK, Gupta SM (2004) A business-mapping approach to multi-criteria group selection of collection centers and recovery facilities. In: IEEE international symposium on electronics and the environment, pp 321–326
Shih H-S, Shyur H-J, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Modell 45(7-8): 801–813
Shih H-S, Wang C-H, Lee ES (2004) A multiattribute GDSS for aiding problem-solving. Math Comput Modell 39(11–12): 1397–1412
Shih H-S, Huang L-C, Shyur H-J (2005) Recruitment and selection processes through an effective GDSS. Comput Math Appl 50(10–12): 1543–1558
Shyur H-J, Shih H-S (2006) A hybrid MCDM model for strategic vendor selection. Math Comput Modell 44(7–8): 749–761
Tsaur S-H, Chang T-Y, Yen C-H (2002) The evaluation of airline service quality by fuzzy MCDM. Tour Manage 23(2): 107–115
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huang, YS., Li, WH. A Study on Aggregation of TOPSIS Ideal Solutions for Group Decision-Making. Group Decis Negot 21, 461–473 (2012). https://doi.org/10.1007/s10726-010-9218-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10726-010-9218-2