A trust-based performance measurement modeling using DEA, T-norm and S-norm operators

Azadeh, AH; Zadeh, SA; Saberi, M; Hussain, FK; Hussain, OK

A trust-based performance measurement modeling using DEA, T-norm and S-norm operators

Azadeh, AH Zadeh, SA Saberi, M

Hussain, FK

Hussain, OK

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2014, pp. 1913 - 1920
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Azadeh, AH	en_US
dc.contributor.author	Zadeh, SA	en_US
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Hussain, OK	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2014, pp. 1913 - 1920	en_US
dc.identifier.isbn	9781479920723	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121599
dc.description.abstract	© 2014 IEEE. In today's highly dynamic economy and society, the performance evaluation of Decision Making Units (DMUs) is of high importance. This study presents an efficient model for analyzing the outputs of performance measurement methodologies by means of trust, which provides explicit qualitative scales instead of representing pure numerical data. The efficiency rate of the current, previous and coming years, as well as the average efficiency and standard deviation, are the five inputs for this model. These efficiency rates are calculated using Data Envelopment Analysis (DEA). The approach uses time series forecasting to predict the future efficiency rate. Furthermore, the implemented Auto Regressive (AR) model includes an Auto Correlation Function (ACF) for input selection. The model utilizes T-norms and S-norms as the final modeling tools. To illustrate the applicability of the proposed model, we apply it to a data set of DMUs. Ultimately, modified trust values for these DMUs are determined using the proposed approach.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2014.6891877	en_US
dc.title	A trust-based performance measurement modeling using DEA, T-norm and S-norm operators	en_US
dc.type	Conference Proceeding
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Beijing, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. In today's highly dynamic economy and society, the performance evaluation of Decision Making Units (DMUs) is of high importance. This study presents an efficient model for analyzing the outputs of performance measurement methodologies by means of trust, which provides explicit qualitative scales instead of representing pure numerical data. The efficiency rate of the current, previous and coming years, as well as the average efficiency and standard deviation, are the five inputs for this model. These efficiency rates are calculated using Data Envelopment Analysis (DEA). The approach uses time series forecasting to predict the future efficiency rate. Furthermore, the implemented Auto Regressive (AR) model includes an Auto Correlation Function (ACF) for input selection. The model utilizes T-norms and S-norms as the final modeling tools. To illustrate the applicability of the proposed model, we apply it to a data set of DMUs. Ultimately, modified trust values for these DMUs are determined using the proposed approach.

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http://hdl.handle.net/10453/121599