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Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0

Smart Manufacturing Group, Advanced Remanufacturing and Technology Centre, A*STAR, Singapore 637143, Singapore
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Appl. Sci. 2019, 9(22), 4815; https://doi.org/10.3390/app9224815
Received: 14 October 2019 / Revised: 7 November 2019 / Accepted: 7 November 2019 / Published: 11 November 2019
Maintenance, Repair, and Overhaul (MRO) is a crucial sector in the remanufacturing industry and scheduling of MRO processes is significantly different from conventional manufacturing processes. In this study, we adopted a swarm intelligent algorithm, Ant Colony Optimization (ACO), to solve the scheduling optimization of MRO processes with two business objectives: minimizing the total scheduling time (make-span) and total tardiness of all jobs. The algorithm also has the dynamic scheduling capability which can help the scheduler to cope with the changes in the shop floor which frequently occur in the MRO processes. Results from the developed algorithm have shown its better solution in comparison to commercial scheduling software. The dependency of the algorithm’s performance on tuning parameters has been investigated and an approach to shorten the convergence time of the algorithm is emerging. View Full-Text
Keywords: Ant Colony Optimization; MRO; scheduling problem Ant Colony Optimization; MRO; scheduling problem
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Tran, L.V.; Huynh, B.H.; Akhtar, H. Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0. Appl. Sci. 2019, 9, 4815.

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