An integrated Data Envelopment Analysis–Artificial Neural Network–Rough Set Algorithm for assessment of personnel efficiency

Azadeh, A.; Saberi, Morteza; Moghaddam, Reza Tavakkoli; Javanmardi, Leili

doi:10.1016/j.eswa.2010.07.033

Cited by 63 publications

(34 citation statements)

References 47 publications

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“…More recent literature has also shown that decision outcomes can be strengthened by merging the application of DEA with other quantitative approaches such as Artificial Neural Networks [39], the Knapsack problem [36], Analytical Hierarchy Process [34], Analytic Network Process [38], TOPSIS [42,43], and fuzzy logic [34,36].…”

Section: Literature Reviewmentioning

confidence: 99%

“…DEA applications are beneficial for many fields such as healthcare [31], education [32], banking [33], manufacturing enterprises [34], energy use [35], project evaluation and selection [35,36], supplier selection [37], and personnel evaluation and selection [38,39]. Furthermore, DEA has recently been given significant attention due to its ability to integrate other tools and techniques in order to formulate hybrid models that enhance the decision-making process.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Multi-Criteria Decision-Making Approach That Combines Fuzzy Topsis and Dea Methodologies

Taylan

Alidrisi

Kabli

2014

SAJIE

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Employee selection is a multi-criteria decision-making (MCDM) problem for selecting suitable applicants from a ready pool. The selection aims to make use of their knowledge, relevant skills, and other characteristics to perform a specific job. The aim of this study is to develop a systematic approach for selecting the best candidates among the air traffic controllers (ATCs) for aviation in Saudi Arabia. Three integrated methods were employed for decision-making in this study. First, a fuzzy decision tree was applied to determine the criteria weights, then the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the attributes. In the last step, the Data Envelopment Analysis (DEA) was used to transform the qualitative variables into quantitative equivalences. A survey was conducted by national and international decisionmakers to elicit the necessary information on the criteria and sub-criteria of the air traffic control system. The decision problem was formulated by employing five criteria and ten applicants. The relationship between the fuzzy TOPSIS and fuzzy-weighted average was very positive for decision-making. The outcomes of the fuzzy TOPSIS and DEA encouraged the development of a decision support system for the selection of ATCs. OPSOMMINGDie kies van werknemers vanuit 'n lys van gepaste aansoeke is 'n multi-kriteria besluitnemingsprobleem. Die seleksie se doel is om gebruik te maak van die potensiële werknemers se relevante vaardighede en ander eienskappe om 'n spesifieke taak te verrig. Die doel van hierdie navorsing is om 'n sistematiese benadering, om die beste kandidate vir lugruimbeheerderposisies in Saoedi Arabië te identifiseer, te ontwikkel. Drie geïntegreerde metodes is ingespan vir die besluitneming in hierdie studie. Eerstens is 'n wasige besluitnemingsboom toegepas om die kriteria gewigte te bepaal. Daarna is die wasige tegniek van Orde Voorkeur deur Ooreenkoms tot die Ideale Oplossing toegepas om die kenmerke te rangskik. Laastens is die Data Omvangs Analise gebruik om die kwalitatiewe veranderlikes tot kwantitatiewe gelykhede om te skakel. 'n Peiling is onder nasionale en internasionale besluitnemers geneem om die noodsaaklike inligting rakende die kriteria en sub-kriteria van die lugruimbeheerstelsel te bepaal. Die besluitnemingsvraagstuk is geformuleer deur vyf kriteria en tien aansoekers in te span. Die verhouding tussen die wasige Orde Voorkeur deur Ooreenkoms tot die Ideale Oplossing en wasig-geweegde gemiddeld was besonder positief vir besluitneming. Die resultate van die wasige Orde Voorkeur deur Ooreenkoms tot die Ideale Oplossing en die Data Omvangs Analise moedig die ontwikkeling van 'n besluitneming ondersteuningstelsel vir lugruimbeheerders aan.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Criteria Decision-Making Approach That Combines Fuzzy Topsis and Dea Methodologies

Taylan

Alidrisi

Kabli

2014

SAJIE

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“…This indicated that a developing country like South Africa required to improve its energy use by 95 times to be on the same level as a developed country like Canada (Olanrewaju et al, 2013). Azadeh et al (2011a) applied DEA-ANN rough set theory (RST) for assessment of personnel efficiency. The proposed algorithm assessed the impact of personnel efficiency attributes on total efficiency through DEA, ANN, and RST.…”

Section: Literature Reviewmentioning

confidence: 99%

Cooperative mechanism based on data envelopment analysis and artificial neural network to measure efficiency: case study of Iranian ports

Mousavizadeh

Khalili‐Damghani

2017

IJADS

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Ports are main parts of business and transportation in every country. So, measuring the efficiency of ports is essential. There are several criteria involved in assessment of ports performance. This paper presents a cooperative approach based on data envelopment analysis (DEA) and artificial neural network (ANN) to measure the efficiency and ranking of 11 main ports in Iran. The results show that the DEA-ANN approach outperforms classic DEA approach. The proposed approach provides following advantages: 1) high discrimination power in presence of low number of decision making units (DMUs) and high number of inputs and outputs; 2) suitable estimation of future production function; 3) providing a proper estimation of future efficiency scores; 4) no need to use super-efficiency models to rank the DMUs. The proposed model of this study can help managers to evaluate the performance of DMUs on the basis of future estimated inputs and outputs.Keywords: data envelopment analysis; DEA; artificial neural network; ANN; hybrid DEA-ANN; efficiency measurement; port efficiency; Iran. He has published more than 80 scientific papers in international journals and conferences. He serves as an Associate Editor of six international scientific journals. His researches can be found at: http://kaveh-khalili.webs.com. His research interests are computational intelligence, soft computing, data envelopment analysis, fuzzy optimisation and decision-making, applied operations research.

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“…In these articles, the authors demonstrate analyses that were conducted between 1977 and 2009. Other applications that used DEA-ANN proved it to be adequate to measure the efficiency in research groups (Özdemir, 2009); in supplier evaluation systems (Pino-Mejías et al, 2010;Azadeh et al, 2011), utilized it to evaluate personnel efficiency; and for performance modeling of business schools (Sreekumar and Mahapatra, 2011). However, there is no evidence that it has been used as a technique to measure logistics efficiency; consequently, this study uses a combination of these techniques to measure the efficiency of logistics in small and medium enterprises in the state of Queretaro, México.…”

Section: Introductionmentioning

confidence: 99%

Logistics efficiency in small and medium enterprises: A logistics, data envelopment analysis combined with artificial neural network (DEA-ANN) approach

M¹,

A²,

Y³

et al. 2012

Afr. J. Bus. Manage.

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This article introduces a novel method for measuring logistics efficiency in small and medium enterprises (SME's) for use with logistics, data envelopment analysis -artificial neural network (DEA-ANN). This method has never been used in logistics before. The research was conducted in Querétaro, Mexico. The sample included 92 SME's, using a questionnaire of 38 questions, 37 of these questions was related to logistics practices and one was about the monthly logistic costs. The database was used to perform a DEA, taking into account the 37 questions of logistics practices as inputs and the logistics cost as outputs; a model of undesirable outputs was used because it is a cost where increases are always undesirable for any enterprise. With the information from DEA, an ANN was created that features a prediction of the index of logistics efficiency; the results with the neural network were very satisfactory, 0.84 R 2 . While modeling was rather accurate, this highlights the inefficiency detected in a large proportion of SME's, further representing problems for these types of firms. Logistic inefficiency may be the reason why many SME's go bankrupt.

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An integrated Data Envelopment Analysis–Artificial Neural Network–Rough Set Algorithm for assessment of personnel efficiency

Cited by 63 publications

References 47 publications

A Multi-Criteria Decision-Making Approach That Combines Fuzzy Topsis and Dea Methodologies

A Multi-Criteria Decision-Making Approach That Combines Fuzzy Topsis and Dea Methodologies

Cooperative mechanism based on data envelopment analysis and artificial neural network to measure efficiency: case study of Iranian ports

Logistics efficiency in small and medium enterprises: A logistics, data envelopment analysis combined with artificial neural network (DEA-ANN) approach

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