关键词: 柔性作业车间低碳调度, 恶化工件, 灰狼优化算法, 车间调度算例

Abstract: To address the low-carbon scheduling problem of flexible job shops with deteriorating workpieces, an improved grey wolf optimization (GWO) algorithm is proposed. The algorithm aims to minimize the maximum completion time, reduce carbon emissions, and improve processing quality. A three-layer encoding mechanism based on the ranked order value rule is designed, along with a novel insert-type greedy decoding method that took machine idle times into account. This is used for dynamically optimizing the scheduling scheme and improving the quality of the initial population. Then, a search mechanism combining swap, reverse, insertion, and mutation operations is proposed to improve the issue of the grey wolf optimizer easily falling into local optima during iterations. Additionally, a normal random sine parameter strategy and an adaptive inertia weight strategy are introduced to improve the population position update mechanism. Finally, the scheduling model is tested using the Brandimarte benchmark. The simulation results show that the proposed algorithm is effective and superior, making it suitable for addressing processing deviations caused by workpieces deterioration.

Key words: low-carbon scheduling in flexible job shop, deteriorating workpieces, grey wolf optimization algorithm, shop scheduling example