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- W4385689035 abstract "In line with Industry 4.0, various advanced technologies such as sensors, automation, and artificial intelligence (AI) methods have been leveraged to enhance maintenance processes in the rolling stock industry. In particular, AI techniques are useful for optimising maintenance scheduling and planning tasks for rolling stocks. This study focuses on the use of a metaheuristic method, namely an enhanced multi-objective Harris’ Hawk optimiser (MO-HHO), for optimising competing objectives based on data obtained from a railway maintenance company. The results of MO-HHO are evaluated and compared with those from other competing models. The findings demonstrate the usefulness of MO-HHO in tackling multi-objective train maintenance scheduling tasks in practical environments." @default.
- W4385689035 created "2023-08-10" @default.
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- W4385689035 date "2023-07-13" @default.
- W4385689035 modified "2023-09-27" @default.
- W4385689035 title "Optimisation of Multi-objective Rolling Stock Maintenance Scheduling with Harris’ Hawk Optimiser" @default.
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- W4385689035 doi "https://doi.org/10.1109/iaict59002.2023.10205863" @default.
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