Group decision making in data envelopment analysis: A robot selection application

2021 ◽  
Author(s):  
Chiang Kao ◽  
Shiang-Tai Liu
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Group Decision Making ◽  
Group Decision ◽  
Data Envelopment ◽  
Robot Selection

Evaluating two-stage holistic production systems using novel CSDEA approach

2018 ◽  
Vol 13 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Dujun Zhai ◽  
Minyue Jin ◽  
Jennifer Shang ◽  
Chenfeng Ji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Production Process ◽  
Group Decision Making ◽  
Group Decision ◽  
Collective Decision ◽  
Data Envelopment ◽  
Two Stage ◽  
Decision Strategies ◽  
Content Type

Purpose The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences. Design/methodology/approach The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences. Findings The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set. Originality/value The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

scholarly journals 2-Tuple Unbalanced Linguistic Multiple Criteria Group Decision Making using Prospect Theory Data Envelopment Analysis

2021 ◽  
Author(s):  
Imran Khan ◽  
Anjana Gupta ◽  
Aparna Mehra
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Prospect Theory ◽  
Group Decision Making ◽  
Group Decision ◽  
Programming Model ◽  
Multiple Criteria ◽  
Data Envelopment ◽  
Linguistic Term ◽  
Criteria Weights

Abstract The linguistic terms in a balanced linguistic term set describing qualitative data are symmetrical around the central linguistic word. With the growing complexity of the problems, the symmetric linguistic term set appears to be confined. This work examines the multiple criteria group decision-making problems where decision-makers employ a 2-tuple unbalanced linguistic term set to provide entries of alternative-criteria matrices.We adopt a data envelopment analysis (DEA) method and create a linear programming model to evaluate alternative-criteria weights for each decision-maker. The value function from prospect theory models the non-rational aspect of risk in criteria. The values of prospect gain and prospect loss on cost and benefit criteria are computed and used to create a DEA model that evaluates the weights of each criterion on each alternative. Finally, the entropy values of the cross-efficiency scores deliver a ranking of the alternatives. A numerical example illustrates the proposed methodology

scholarly journals Industrial robot selection using a multiple criteria group decision making method with individual preferences

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259354
Author(s):  
Jinling Zhao ◽  
Yubing Sui ◽  
Yang Xu ◽  
K. K. Lai
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Industrial Robot ◽  
Multiple Criteria ◽  
Industrial Robots ◽  
Individual Decision ◽  
Decision Matrix ◽  
Individual Preferences ◽  
Robot Selection

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

scholarly journals New Extended Topsis Method Based On The Entropy Measure And Picture Fuzzy Rough Set Information And Their Application In Decision Support System

2021 ◽  
Author(s):  
Muhammad Ali Khan ◽  
Saleem Abdullah ◽  
Abbas Qadir
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Group Decision Making ◽  
Distance Measure ◽  
Group Decision ◽  
Score Function ◽  
Entropy Measure ◽  
Comparison Results ◽  
Multiple Attribute ◽  
Robot Selection

Abstract In this article, we shall introduce a novel technique for order preference by similarity to ideal solution (TOPSIS)-based methodology to resolve multicriteria group decision-making problems within picture fuzzy environment, where the weights information of both the decision makers (DMs) and criteria are completely unknown. First, we briefly review the definition of picture fuzzy sets (PFS), score function and accuracy function of PFRSs and their basic operational laws. In addition, defined the generalized distance measure for PFRSs based on picture fuzzy rough entropy measure to compute the unknown weights information. Secondly, the picture fuzzy information based decision-making technique for multiple attribute group decision making (MAGDM) is established and all computing steps are simply depicted. In our presented model, it's more accuracy and effective for considering the conflicting attributes. Finally, an illustrative example with robot selection is provided to demonstrate the effectiveness of the proposed picture fuzzy decision support approaches, together with comparison results discussion, proving that its results are feasible and credible.

Sign in / Sign up

Export Citation Format

Share Document