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

Abdul Rahman, Azrul Azwan and Joshua, Adeboye Oluwamayowa and Joe Yee, Tan and Salleh, Mohd Rizal and Rahman, M.A.A (2022) An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems. JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING, 16 (5). pp. 793-809. ISSN 1995-6665

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Genetic algorithm, Optimization, Predictive-reactive, Reconfigurable manufacturing system, Scheduling, Simulation
Divisions:	Faculty of Manufacturing Engineering
Depositing User:	Wizana Abd Jalil
Date Deposited:	15 Dec 2023 15:57
Last Modified:	17 Jan 2024 14:35
URI:	http://eprints.utem.edu.my/id/eprint/27064
Statistic Details:	View Download Statistic

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