Trust-based performance measurement using fuzzy operators

Azadeh, A; Zia, NP; Saberi, M; Hussain, FK; Hussain, OK; Chang, E

Trust-based performance measurement using fuzzy operators

Azadeh, A Zia, NP Saberi, M

Hussain, FK

Hussain, OK Chang, E

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 2014, pp. 1701 - 1706
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Azadeh, A	en_US
dc.contributor.author	Zia, NP	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.contributor.author	Chang, E	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 2014, pp. 1701 - 1706	en_US
dc.identifier.isbn	9781479943166	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123028
dc.description.abstract	© 2014 IEEE. Performance assessment is a critical aspect for any organization, as it provides them with the means to measure their performance. Decision makers and top management need to gain a comprehensive view of the capabilities and performance of decision making units (DMU's) in order to make efficient decisions and beneficial improvements. In this study a novel model has been proposed to place performance assessment outputs in linguistic form, which utilize proper trust labels. Trust labels provide explicit qualitative scales, instead of representing pure numerical data, which are more meaningful for top manager. Fifteen scenarios are formed based on two main factors: the number of decision making units and the number of timeslots, which together form the basis of the proposed method for performance assessment. The efficiency rates of the current, previous and following years, along with the average efficiency and standard deviation, are the five inputs to this model. The approach uses time series forecasting to predict the future efficiency rate and is armed with an Auto Correlation Function (ACF) for input selection. The model utilizes fuzzy t-norm and s-norm as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set provided by running a simulation structured by a unique logic.	en_US
dc.relation.ispartof	Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014	en_US
dc.relation.isbasedon	10.1109/ICIEA.2014.6931442	en_US
dc.title	Trust-based performance measurement using fuzzy operators	en_US
dc.type	Conference Proceeding
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
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. Performance assessment is a critical aspect for any organization, as it provides them with the means to measure their performance. Decision makers and top management need to gain a comprehensive view of the capabilities and performance of decision making units (DMU's) in order to make efficient decisions and beneficial improvements. In this study a novel model has been proposed to place performance assessment outputs in linguistic form, which utilize proper trust labels. Trust labels provide explicit qualitative scales, instead of representing pure numerical data, which are more meaningful for top manager. Fifteen scenarios are formed based on two main factors: the number of decision making units and the number of timeslots, which together form the basis of the proposed method for performance assessment. The efficiency rates of the current, previous and following years, along with the average efficiency and standard deviation, are the five inputs to this model. The approach uses time series forecasting to predict the future efficiency rate and is armed with an Auto Correlation Function (ACF) for input selection. The model utilizes fuzzy t-norm and s-norm as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set provided by running a simulation structured by a unique logic.

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http://hdl.handle.net/10453/123028