An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems

• Main Authors: Abdul Rahman, Azrul Azwan, Joshua, Adeboye Oluwamayowa, Joe Yee, Tan, Salleh, Mohd Rizal, Rahman, M.A.A
• Format: Article
• Language: English
• Published: Hashemite University 2022
• 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

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.