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- W2904832112 abstract "The hybrid flowshop scheduling problem (HFSP) has been widely studied in the past decades. The most commonly used criterion is production efficiency. Green criteria, such as energy consumption and carbon emission, have attracted growing attention with the improvement of the environment protection awareness. Limited attention has been paid to noise pollution. However, noise pollution can lead to health and emotion disorder. Thus, this paper studies a multi-objective HFSP considering noise pollution in addition to production efficiency and energy consumption. First, we formulate a new mixed-integer programming model for this multi-objective HFSP. To realize the green scheduling, one energy conservation/noise reduction strategy is embedded into this model. Then, a novel multi-objective cellular grey wolf optimizer (MOCGWO) is proposed to address this problem. The proposed MOCGWO integrates the merits of cellular automata (CA) for diversification and variable neighborhood search (VNS) for intensification, which balances exploration and exploitation. Finally, to validate the efficiency and effectiveness of the proposed MOCGWO, we compare our proposal with other well-known multi-objective evolutionary algorithms by conducting comparison experiments. The experimental results show that the proposed MOCGWO is significantly better than its competitors on this problem." @default.
- W2904832112 created "2018-12-22" @default.
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- W2904832112 date "2019-02-01" @default.
- W2904832112 modified "2023-10-18" @default.
- W2904832112 title "A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution" @default.
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- W2904832112 doi "https://doi.org/10.1016/j.asoc.2018.11.043" @default.
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