A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator

A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator

Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainab...

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Main Author: Adam Shariff, Adli Aminuddin
Format: Thesis
Language:eng
eng
Published: 2015
Subjects:
T Technology (General)
TS Manufactures
Online Access:https://etd.uum.edu.my/5367/1/s93356.pdf
https://etd.uum.edu.my/5367/2/s93356_abstract.pdf
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institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Mohd Nawawi, Mohd Kamal
Nik Mohamed, Nik Mohd Zuki
topic T Technology (General)
TS Manufactures
spellingShingle T Technology (General)
TS Manufactures
Adam Shariff, Adli Aminuddin
A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
description Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainable manufacturing, the development of appropriate indicators needs to be emphasized as compared to other efforts. Regrettably, the existing indicators have several drawbacks that may hamper the accuracy of sustainability performance assessment of an organization. As such, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this study suggests a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which can assist the decision making process of sustainable manufacturing by developing a new indicator mechanism. The KBFANP system comprises of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system incorporates the advantages of Knowledge-Based System Fuzzy Set Theory and Analytic Network Process into a single unified approach as a standardized indicator, which is applicable to all types of problem setting. A prototype of KBFANP system was developed, tested and analyzed on three experimental data sets and two real manufacturing settings. The system was able to provide solutions on the areas that need improvement with different levels of priority. This study also supports the notion of lean and green manufacturing as the elementary foundation of sustainable manufacturing implementation. The proposed KBFANP system can act as an advisory Decision Support System which is beneficial to both academia and industrial practitioners.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Adam Shariff, Adli Aminuddin
author_facet Adam Shariff, Adli Aminuddin
author_sort Adam Shariff, Adli Aminuddin
title A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
title_short A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
title_full A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
title_fullStr A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
title_full_unstemmed A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
title_sort prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2015
url https://etd.uum.edu.my/5367/1/s93356.pdf
https://etd.uum.edu.my/5367/2/s93356_abstract.pdf
_version_ 1747827918588870656
spelling my-uum-etd.53672021-03-18T00:23:59Z A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator 2015 Adam Shariff, Adli Aminuddin Mohd Nawawi, Mohd Kamal Nik Mohamed, Nik Mohd Zuki Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences T Technology (General) TS Manufactures Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainable manufacturing, the development of appropriate indicators needs to be emphasized as compared to other efforts. Regrettably, the existing indicators have several drawbacks that may hamper the accuracy of sustainability performance assessment of an organization. As such, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this study suggests a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which can assist the decision making process of sustainable manufacturing by developing a new indicator mechanism. The KBFANP system comprises of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system incorporates the advantages of Knowledge-Based System Fuzzy Set Theory and Analytic Network Process into a single unified approach as a standardized indicator, which is applicable to all types of problem setting. A prototype of KBFANP system was developed, tested and analyzed on three experimental data sets and two real manufacturing settings. The system was able to provide solutions on the areas that need improvement with different levels of priority. This study also supports the notion of lean and green manufacturing as the elementary foundation of sustainable manufacturing implementation. The proposed KBFANP system can act as an advisory Decision Support System which is beneficial to both academia and industrial practitioners. 2015 Thesis https://etd.uum.edu.my/5367/ https://etd.uum.edu.my/5367/1/s93356.pdf text eng public https://etd.uum.edu.my/5367/2/s93356_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abdullah, M. S., Evans, A., Benest, I., Paige, R., & Kimble, C. (2004). Modelling knowledge based systems using the executable modelling framework (XMF). In Cybernetics and Intelligent Systems, 2004 IEEE Conference, 2, pp. 1055-1060). IEEE. Abdullah, S., M., Kimble, C., Benest, I., & Paige, R. (2006). Knowledge-based systems: a re-evaluation. Journal of Knowledge Management, 10(3), 127-142. Adamcsek, E. (2008). The analytic hierarchy process and its generalizations. Eotvos Lorand University. 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