Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques
Evolution of manufacturing systems passed through different environment to full fill a need for optimal operating system. This led dynamic environment of manufacturing to Reconfigurable Manufacturing Systems (RMSs), which characterized by shorter product life cycle and changes in demand. To impleme...
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my.upm.eprints.340362015-04-21T08:05:30Z http://psasir.upm.edu.my/id/eprint/34036/ Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques Nehzati, Taravatsadat Evolution of manufacturing systems passed through different environment to full fill a need for optimal operating system. This led dynamic environment of manufacturing to Reconfigurable Manufacturing Systems (RMSs), which characterized by shorter product life cycle and changes in demand. To implement a successful RMS, effective process plan and production schedule is essential. However, little is known about applicability and effectiveness of intelligent techniques in reconfigurable manufacturing. On the other hand, investigation on the influence of intelligent production scheduling techniques on RMS performance is not negligible. This research proposed fuzzy model is subject to evaluate thedefined performance measures, such as; Machine utilization, Scalability, Product completion time and Lateness. To verify the methodology, the obtained result brought to comparison with those results acquired in conventional manufacturing systems. In addition a recursive mathematical model is developed to enhance the research validation. In this study, fuzzy logic model usedfour suitable fuzzy input variables, namely machine allocated processing time, machine priority, due date priority and machine structure, to solve production scheduling problem in selecting machine for each job operation and determining the processing sequence for each machine,simultaneously. The proposed fuzzy model used a fuzzy rule based inference system to determine job priority as a fuzzy output variable for the production scheduling purpose. The production schedule showed that is able to improve performance criteria in machine utilization, machine completion time, and also in product completion time as well as scalability. Experimental and comparative test indicates superiority of RMS environment over Flexible Manufacturing System environment (FMS)on using the same fuzzy based production scheduling model in terms of machine utilization (increased by 6.6%), machine completion time (increased by 63.3%), lateness (decreaseddeistically) and product completion time (increased by 22.13), as well as throughput (increased by 20%). Employing mathematical programming showed that the fuzzy scheduling is the most successful approach to solve RMS scheduling problem. However, investigating on the effect of changing manufacturing environment and fuzzy production scheduling model on a conventional production scheduling demonstrated slight increment in machine utilization, machine completion time and product completion time increased by 0.82%, 9.1%, and 2.4% respectively while lateness decreased by 35%, however the through put reduced by -2.5%.Although the obtained results are discussable based on assumptions and input data, however the positive impact of fuzzy technique in RMS environment is easily interpretable. The performance in fuzzy based production scheduling of RMS is superior to that of conventional manufacturing systems.The results would motivates researches to continue with evaluating different performance measures and assumptions and evaluating the effect of fuzzy logic techniques in order to come up with utilized production scheduling model for future manufacturing environment. 2012-05 Thesis NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34036/1/FK%202012%206R.pdf Nehzati, Taravatsadat (2012) Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques. Masters thesis, Universiti Putra Malaysia. |
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Evolution of manufacturing systems passed through different environment to full fill a need for optimal operating system. This led dynamic environment of manufacturing to
Reconfigurable Manufacturing Systems (RMSs), which characterized by shorter product life cycle and changes in demand. To implement a successful RMS, effective process
plan and production schedule is essential. However, little is known about applicability and effectiveness of intelligent techniques in reconfigurable manufacturing. On the other hand, investigation on the influence of intelligent production scheduling techniques on RMS performance is not negligible.
This research proposed fuzzy model is subject to evaluate thedefined performance measures, such as; Machine utilization, Scalability, Product completion time and
Lateness. To verify the methodology, the obtained result brought to comparison with those results acquired in conventional manufacturing systems. In addition a recursive
mathematical model is developed to enhance the research validation.
In this study, fuzzy logic model usedfour suitable fuzzy input variables, namely machine allocated processing time, machine priority, due date priority and machine structure, to solve production scheduling problem in selecting machine for each job operation and determining the processing sequence for each machine,simultaneously. The proposed
fuzzy model used a fuzzy rule based inference system to determine job priority as a fuzzy output variable for the production scheduling purpose.
The production schedule showed that is able to improve performance criteria in machine utilization, machine completion time, and also in product completion time as well as scalability. Experimental and comparative test indicates superiority of RMS environment over Flexible Manufacturing System environment (FMS)on using the same
fuzzy based production scheduling model in terms of machine utilization (increased by 6.6%), machine completion time (increased by 63.3%), lateness (decreaseddeistically)
and product completion time (increased by 22.13), as well as throughput (increased by 20%). Employing mathematical programming showed that the fuzzy scheduling is the
most successful approach to solve RMS scheduling problem. However, investigating on the effect of changing manufacturing environment and fuzzy production scheduling
model on a conventional production scheduling demonstrated slight increment in machine utilization, machine completion time and product completion time increased by 0.82%, 9.1%, and 2.4% respectively while lateness decreased by 35%, however the through put reduced by -2.5%.Although the obtained results are discussable based on assumptions and input data, however the positive impact of fuzzy technique in RMS environment is easily interpretable.
The performance in fuzzy based production scheduling of RMS is superior to that of conventional manufacturing systems.The results would motivates researches to continue
with evaluating different performance measures and assumptions and evaluating the effect of fuzzy logic techniques in order to come up with utilized production scheduling model for future manufacturing environment. |
| format |
Thesis |
| author |
Nehzati, Taravatsadat |
| spellingShingle |
Nehzati, Taravatsadat Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| author_facet |
Nehzati, Taravatsadat |
| author_sort |
Nehzati, Taravatsadat |
| title |
Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| title_short |
Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| title_full |
Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| title_fullStr |
Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| title_full_unstemmed |
Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| title_sort |
production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques |
| publishDate |
2012 |
| url |
http://psasir.upm.edu.my/id/eprint/34036/1/FK%202012%206R.pdf http://psasir.upm.edu.my/id/eprint/34036/ |
| _version_ |
1643831039841271808 |
| score |
13.211869 |