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: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://etd.uum.edu.my/5367/ |
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| Institution: | Universiti Utara Malaysia |
| Language: | English English |
| Summary: | 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. |
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