Supplier Selection by Extended TOPSIS to Obtain the Ideal Compromise Solution in Group Decision Making

2017 ◽  
Vol 4 (3) ◽  
pp. 71-85
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Intuitionistic Fuzzy Set ◽  
Individual Preference ◽  
Multiple Criteria Decision Making ◽  
Extended Model ◽  
The Individual ◽  
Collective Preference

In the current literature, there are several studies, which the supplier selection is typically a Multi Criteria Group Decision Making problem. Several solutions for the above problem are proposed (from simple approaches; like, Borda, Condorcet, etc., to complex ones; like, Multiple Criteria Decision Making model combined with intuitionistic fuzzy set, etc.). To solve this problem, different method (particularly, extended TOPSIS method) are proposed in this paper. Firstly, we have used TOPSIS to find the individual preference ordering, then, we have used the extended version of this method to find the collective preference orderings. In addition, this model is capable of considering the expert weights. Finally, the proposed approach is compared with an existed approach (i.e., TOPSIS and Borda's function). Compared results show the advantage of our extended model over previous one.

A Multiple Attribute Group Decision-making Model for Selecting the Best Supplier

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Present Model ◽  
Group Decision ◽  
Individual Preference ◽  
Multiple Attribute ◽  
The Individual ◽  
Collective Preference ◽  
Decision Making Model

In the current literature, supplier selection is an important Multi Attribute Group Decision Making (MAGDM) problem which heavily contributes to the overall supply chain performance. Several solutions for the above problem are proposed. In this paper, TOPSIS and Bordas function approach, which is one of these methods, is discussed. So that, in the present model, first TOPSIS is used to find the individual preference ordering. Then, Bordas function is used to find the collective preference orderings. Finally, a simple example is provided in order to demonstrate its applicability and effectiveness of the proposed method.

scholarly journals TWO-STAGE PRIORITIZATION PROCEDURE FOR MULTIPLICATIVE AHP-GROUP DECISION MAKING

2020 ◽  
Vol 26 (2) ◽  
pp. 525-545
Author(s):  
Changsheng Lin ◽  
Gang Kou ◽  
Yi Peng ◽  
Fawaz E. Alsaadi
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Two Stage ◽  
Analytic Hierarchy ◽  
Priority Vector ◽  
The Individual ◽  
Collective Preference ◽  
Hierarchy Process ◽  
Multiplicative Ahp

In this paper, we propose two-stage prioritization procedure (TSPP) for multiplicative Analytic Hierarchy Process-group decision making (AHP-GDM), which involves determining the group priority vector based on the individual pair-wise comparison matrices (PCMs), simultaneously considering the consensus and consistency of the individual PCMs. The first stage of the TSPP involves checking and revising the individual PCMs for reaching the acceptable consensus and consistency. The second stage of the TSPP involves estimating the group priority vector using Bayesian approach. The main characteristics of the proposed TSPP are as follows: 1) It makes full use of the prior information as well as the sample information during the Bayesian revision of the individual PCMs and the Bayesian estimation of the group priority vector; 2) It ensures that the revised individual PCMs reach the acceptable consensus and consistency; 3) It enriches the aggregation methods for the collective preference in multiplicative AHP-GDM. Finally, two numerical examples are used to evaluate the applicability and effectiveness of the proposed TSPP by the comparisons with several other methods.

scholarly journals Mental simulation and the individual preference effect

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dawn H. Nicholson ◽  
Tim Hopthrow ◽  
Georgina Randsley de Moura
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Experimental Studies ◽  
Individual Preference ◽  
Mental Simulation ◽  
Decision Quality ◽  
Content Type ◽  
Hidden Profile ◽  
The Individual

PurposeThe “Individual Preference Effect” (IPE: Faulmüller et al., 2010; Greitemeyer and Schulz-Hardt, 2003; Greitemeyer et al., 2003), a form of confirmation bias, is an important barrier to achieving improved group decision-making outcomes in hidden profile tasks. Group members remain committed to their individual preferences and are unable to disconfirm their initial suboptimal selection decisions, even when presented with full information enabling them to correct them, and even if the accompanying group processes are perfectly conducted. This paper examines whether a mental simulation can overcome the IPE.Design/methodology/approachTwo experimental studies examine the effect of a mental simulation intervention in attenuating the IPE and improving decision quality in an online individual hidden profile task.FindingsIndividuals undertaking a mental simulation achieved higher decision quality than those in a control condition and experienced a greater reduction in confidence in the suboptimal solution.Research limitations/implicationsResults suggest a role for mental simulation in overcoming the IPE. The test environment is an online individual decision-making task, and broader application to group decision-making is not tested.Practical implicationsSince mental simulation is something we all do, it should easily generalise to an organisational setting to improve decision outcomes.Originality/valueTo the authors' knowledge, no study has examined whether mental simulation can attenuate the IPE.

scholarly journals A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1554
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Grey System Theory ◽  
Multiple Criteria ◽  
Decision Making Process ◽  
Grey System ◽  
Criteria Weights

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.

CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection

2021 ◽  
pp. 1-11
Author(s):  
Huiyuan Zhang ◽  
Guiwu Wei ◽  
Xudong Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Comparison Method ◽  
Entropy Method ◽  
Decision Makers ◽  
Green Supplier Selection ◽  
Multiple Attribute ◽  
Psychological Perceptions

The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified.

scholarly journals A Novel Multi-Criteria Group Decision-Making Approach Based on Bonferroni and Heronian Mean Operators under Hesitant 2-Tuple Linguistic Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1489
Author(s):  
Shahzad Faizi ◽  
Wojciech Sałabun ◽  
Nisbha Shaheen ◽  
Atiq ur Rehman ◽  
Jarosław Wątróbski
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Weighted Average ◽  
Aggregation Operators ◽  
Bonferroni Mean ◽  
Operational Laws ◽  
Geometric Bonferroni Mean ◽  
The Individual ◽  
Heronian Mean

Ambiguous and uncertain facts can be handled using a hesitant 2-tuple linguistic set (H2TLS), an important expansion of the 2-tuple linguistic set. The vagueness and uncertainty of data can be grabbed by using aggregation operators. Therefore, aggregation operators play an important role in computational processes to merge the information provided by decision makers (DMs). Furthermore, the aggregation operator is a potential mechanism for merging multisource data which is synonymous with cooperative preference. The aggregation operators need to be studied and analyzed from various perspectives to represent complex choice situations more readily and capture the diverse experiences of DMs. In this manuscript, we propose some valuable operational laws for H2TLS. These new operational laws work through the individual aggregation of linguistic words and the collection of translation parameters. We introduced a hesitant 2-tuple linguistic weighted average (H2TLWA) operator to solve multi-criteria group decision-making (MCGDM) problems. We also define hesitant 2-tuple linguistic Bonferroni mean (H2TLBM) operator, hesitant 2-tuple linguistic geometric Bonferroni mean (H2TLGBM) operator, hesitant 2-tuple linguistic Heronian mean (H2TLHM) operator, and a hesitant 2-tuple linguistic geometric Heronian mean (H2TLGHM) operator based on the novel operational laws proposed in this paper. We define the aggregation operators for addition, subtraction, multiplication, division, scalar multiplication, power and complement with their respective properties. An application example and comparison analysis were examined to show the usefulness and practicality of the work.

scholarly journals Pythagorean Fuzzy Hamy Mean Operators in Multiple Attribute Group Decision Making and Their Application to Supplier Selection

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 505 ◽  
Author(s):  
Zengxian Li ◽  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Multiple Attribute ◽  
Pythagorean Fuzzy Numbers

In this paper, we extend the Hamy mean (HM) operator and dual Hamy mean (DHM) operator with Pythagorean fuzzy numbers (PFNs) to propose Pythagorean fuzzy Hamy mean (PFHM) operator, weighted Pythagorean fuzzy Hamy mean (WPFHM) operator, Pythagorean fuzzy dual Hamy mean (PFDHM) operator, weighted Pythagorean fuzzy dual Hamy mean (WPFDHM) operator. Then the multiple attribute group decision making (MAGDM) methods are proposed with these operators. In the end, we utilize an applicable example for supplier selection to prove the proposed methods.

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