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• W3044246128 abstract "Recently, with the strategic requirements of sustainable development, more and more attention has been paid on the green scheduling problems. Different from the traditional flow-line scheduling problem, the ship plane block possesses the characteristics of larger volume and weight, so the problem of ship plane block flow-line can be regarded as a blocking flow-line scheduling problem (BFSP). In this paper, considering the complexity of ship building and the particularity of green scheduling problem (e.g., carbon emission and noise of equipment), the green blocking flow-line scheduling problem model of panel block (GBFSP) in shipbuilding was established to minimize the maximum completion time, carbon emission cost and noise cost. Moreover, the improved gray wolf optimization (IGWO) algorithm was proposed to solve this problem effectively. In the proposed IGWO algorithm, a ranked order value (ROV) method was performed to realize the transformation from continuous gray wolf individual position to discrete optimal solution. Secondly, a nonlinear convergence factor and PSO algorithm were introduced to balance the development and exploration ability of the IGWO algorithm. In addition, variable neighborhood search (VNS) was also used to improve the accuracy and effectiveness of local search. Furthermore, the validity of the proposed IGWO algorithm is verified by some famous benchmark examples. In addition, a real data of a shipyard was used for example verification and multiple numerical experiments. Our results suggested that the model and the IGWO algorithm can solve the problems existing in the ship plane block flow-line effectively. Meanwhile, reducing the impact of pipeline on the environment, and achieving the goal of green production." @default.
• W3044246128 created "2020-07-29" @default.
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• W3044246128 date "2020-10-01" @default.
• W3044246128 modified "2023-10-03" @default.
• W3044246128 title "Green scheduling optimization of ship plane block flow line considering carbon emission and noise" @default.
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• W3044246128 doi "https://doi.org/10.1016/j.cie.2020.106680" @default.
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