An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems
The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-topredict global business market, especially job-shop production. However, even if there is a properly planned schedule for production, and there is also a technique for scheduling in...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Hashemite University
2022
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27064/2/0067728022023.PDF http://eprints.utem.edu.my/id/eprint/27064/ https://jjmie.hu.edu.jo/Vol16.htm |
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| Summary: | The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-topredict global business market, especially job-shop production. However, even if there is a properly planned schedule for
production, and there is also a technique for scheduling in Reconfigurable Manufacturing System (RMS) but job-shop
production will always come out with errors and disruption due to complex and uncertainty happening during the production
process, hence fail to fulfil the due-date requirements. This study proposes a generic control strategy for piloting the
implementation of a complex scheduling challenge in an RMS. This study is aimed to formulate an optimization-based
algorithm with a simulation tool to reduce the throughput time of complex RMS, which can comply with complex product
allocations and flexible routings of the system. The predictive-reactive strategy was investigated, in which Genetic Algorithm
(GA) and dispatching rules were used for predictive scheduling and reactivity controls. The results showed that the proposed
optimization-based algorithm had successfully reduced the throughput time of the system. In this case, the effectiveness and
reliability of RMS are increased by combining the simulation with the optimization algorithm. |
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