A comprehensive fuzzy decision-making method for determining optimum machining strategy in production line improvement
The manufacturing sector plays a significant role in utilizing the economy of a country. Statistically, more than 37.4% of the GDP of Malaysia belongs to industries. Having a production strategy, which relies on the reality of an industrial environment, will enhance the success of that busines...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/97774/1/FK%202021%2051%20UPMIR.pdf |
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| Summary: | The manufacturing sector plays a significant role in utilizing the economy of a
country. Statistically, more than 37.4% of the GDP of Malaysia belongs to
industries. Having a production strategy, which relies on the reality of an
industrial environment, will enhance the success of that business. Therefore, it
is crucial to propose methods to skyrocket the production factors such as
production time. An in-depth review of the literature during the last half-century
found that many research studies have been carried out to utilize the production
line performances. One major gap found by the literature review is that many
industries do not pay enough attention to the factors that can enhance
productivity. Such ignorance yields too many problems and sometimes failing to
manufacture enough products to fulfill the market demand. Such problems often
can be found in small and medium-scale companies in developing countries.
Besides, in the real industrial environment, most factors are uncertain and can
get various values depending on different conditions. Such phenomena even
worsen the condition for forecasting the effective factors in an industrial
company. The primary objective of current research is to develop a
comprehensive fuzzy decision-making method for determining the optimum
machining strategy in order to utilize the completion time of a product. For this
purpose, finding the effective factors on completion time in production lines and
identifying the correlations between factors are considered the next objectives.
For this purpose, using the library study and interview with the experts, a list of
factors was found. Then a questionnaire is designed and distributed to the
academic and industry experts. According to the findings, the effective internal
factors in minimizing product completion time can be divided into five main
clusters: Technology, Human Resource, Machinery, Material, and Facility
Design. Then using the statistical analysis, the correlations between factors are
found. It is found that there are positive correlations exist between most of the
factors in a range between -0.048 and 0.636 at a significant level of 0.05 which
means they may have positive or negative interactions while happens at the same time in an industrial environment. In continue, using the regression
method, the impact of each factor on the dependent variable for the research
(product completion time) is determined. Using the data gathered from the
experts, a Fuzzy Inference System (FIS) model was developed, which could
reflect the uncertainty of the factors in decreasing (or increasing) the product
completion time in manufacturing systems. Then, a hybrid Fuzzy-TOPSIS based
heuristic is developed using MATLAB to solve the case studies. In order to
evaluate the performance of the proposed heuristic method, a number of
experiments are designed and solved by Taguchi Method (DOE). Then, the
performance of the method was evaluated using several indicators, including
completion time. Our findings showed that the Human resource, Machinery,
Material, Technology and Social Environment positively minimize product
completion time, respectively. It is found that the proposed Fuzzy-TOPSIS
heuristic is capable of reducing the product completion time in a range between
0 and 10.3%. |
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