A novel credibility-based group decision making method for Enterprise Architecture scenario analysis using Data Envelopment Analysis

Fasanghari, Mehdi; Amalnick, Mohsen Sadegh; Anvari, Reza; Razmi, Jafar

doi:10.1016/j.asoc.2015.03.052

Cited by 23 publications

(12 citation statements)

References 52 publications

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“…This section will discuss the conceptual framework for welding process selection for repairing shredder hammer. In accordance with the existing literature, Charnes et al (1978) introduced Data Envelopment Analysis (DEA) and Fasanghari, Amalnick, Anvari, and Razmi (2015) develop data envelopment analysis (DEA) to be able to resolve ambiguity and vagueness from group decision-making by integrated data envelopment analysis (DEA) and p-robust fuzzy credibility constrained programming technique approaches. Fasanghari et al (2015) used that method as a multi-criteria decision-making (MCDM) for enterprise architecture (EA) scenario analysis.…”

Section: Methodsmentioning

confidence: 97%

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A novel group decision-making method by P-robust fuzzy DEA credibility constraint for welding process selection

2020

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Section: Methodsmentioning

confidence: 97%

“…However, the main issue of conventional DEA cannot cope with uncertainty data. In the group decision-making has two main uncertainty data, such as ambiguity and vagueness (Fasanghari et al, 2015). The ambiguity refers to multiple interpretations of the expert, and vagueness is due to the lack of expert opinion precision.…”

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

A novel group decision-making method by P-robust fuzzy DEA credibility constraint for welding process selection

2020

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“…Integrated DEA and p-robust technique are introduced by Fasanghari [13]. In this paper, the efficiency score of DMUh for each expert is an independent scenario.…”

Section: Dea and P-robust Techniquementioning

confidence: 99%

Selection of Welding Process for Repairing Shredder Hammer by Integrated Data Envelopment Analysis (DEA) and P-robust Technique

2019

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The Selection of the welding process is one of the most significant decision-making problems, and it involves a wide range of information following the type of product. Hence, the automation of knowledge through a knowledge-based system will significantly enhance the decision-making process and simplify for identifying the most appropriate welding processes. The aims of this paper for explicates a knowledge-based system developed for recognising the most suitable welding processes for repairing shredder hammer by using data envelopment analysis (DEA) and p-robust technique. The proposed approach is used for ranking six welding processes which are commonly used, namely shielded metal arc welding (SMAW), flux cored arc welding (FCAW), submerged arc welding (SAW), oxyacetylene gas welding (OAW), gas tungsten arc welding (GTAW), and gas metal arc welding (GMAW). In order to determine the best welding process among competitive welding processes for repairing of shredder hammer, ten parameters are used, namely the availability of consumable, welding process type (manual and automatic), flexibility of welding position, weld-ability on base metal, initial preparation required, welding procedures, post-weld cleaning, capital cost, operating factor, and deposition rate. Furthermore, the sensitivity analysis of regret value (p) is investigated in three cases proposed.

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“…The concept of the satisfaction degree was introduced into the stochastic DEA problem and the consensus DEA model was proposed, e.g., by [38][39][40][41][42][43]. Furthermore, the confidence region or level was introduced into the stochastic DEA model, e.g., by [44][45][46][47][48]. The Monte Carlo simulation algorithm was used to solve the problem of stochastic input and output variables and the study of stochastic DEA is one of the directions of current research, such as studies by [49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry

Gong

Chen

2017

Sustainability

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This study utilizes the Data Envelopment Efficiency (DEA) model to assess input-output efficiency from two perspectives. First, not considering the distribution of interval data, we introduce an adjusted parameter to transform interval data to determination data. Second, by contrast, we take into account the distribution characteristics of interval data and test the DEA model with interval data based on linear uniform distribution and normal distribution with uncertainty. Based on the normal distribution DEA evaluation model, this paper aims to evaluate the input-output performance of the manufacturing industry with the constraint of environmental pollution in the Yangtze River Delta (YRD) region, China. Research has shown that the optimal solution of the normal distribution model is better than that of linear distribution. Therefore, it is imperative to adopt an appropriate method to evaluate the energy and environmental efficiency of this region.

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A novel credibility-based group decision making method for Enterprise Architecture scenario analysis using Data Envelopment Analysis

Cited by 23 publications

References 52 publications

A novel group decision-making method by P-robust fuzzy DEA credibility constraint for welding process selection

A novel group decision-making method by P-robust fuzzy DEA credibility constraint for welding process selection

Selection of Welding Process for Repairing Shredder Hammer by Integrated Data Envelopment Analysis (DEA) and P-robust Technique

Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry

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