| 邵博琰1， 吉卫喜1,2*.改进金豺算法求解多目标柔性车间低碳调度问题[J].轻工机械,2024,42(4):95-104 |
| 改进金豺算法求解多目标柔性车间低碳调度问题 |
| Multi Objective Flexible Workshop Low Carbon Scheduling Based on Improved GJO Algorithm |
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| DOI：10.3969/j.issn.1005 2895.2024.04.014 |
| 中文关键词: 柔性作业车间 低碳生产调度 金豺算法 车间碳排放量 多目标优化 |
| 英文关键词:flexible job shop low carbon scheduling GJO（golden jackal optimizer） workshop carbon emission multi objective optimization |
| 基金项目:国家自然科学基金青年基金(51805213)。 |
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| 摘要点击次数: 120 |
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| 中文摘要: |
| 为了降低多目标柔性车间调度过程中的碳排放量和能量消耗，考虑加工过程中准备过程和终止过程对低碳调度的影响，以优化最大完工时间、车间碳排放量及机器负载为目标，建立车间调度模型，课题组提出一种改进的金豺算法（improvement golden jackal optimization，I GJO）来对目标函数进行求解。首先引入融合贝塔分布（beta distribution）的帐篷（Tent）混沌映射来进行种群初始化以提高种群质量；然后采取折射反向学习策略来扩大算法寻优范围, 从而显著提高算法跃出局部极值的能力；最后加入自适应惯性权重，通过设定合适的惯性权重，来提高算法的收敛速度和寻优精度。调度实例数据验证表明：对于多目标柔性车间低碳调度问题，改进后的金豺算法相较于原算法与灰狼算法有更好的适用性，可以获得质量更高的解。 |
| 英文摘要: |
| To reduce carbon emissions and energy consumption in multi objective flexible shop scheduling, considering the impact of setup and termination processes on low carbon workshop scheduling, a workshop scheduling model was established to optimize maximum completion time, workshop carbon emissions and machine load. An improved golden jackal optimization (I GJO) algorithm was proposed to solve the objective functions. Firstly, a Tent chaotic mapping combined with beta distribution was introduced for population initialization to improve population quality. Then, a refraction opposition based learning strategy was adopted to expand the optimization range of the algorithm, so as to significantly improve its ability to escape local extreme value. Finally, adaptive inertia weights were incorporated to improve the convergence rate and optimization accuracy of the algorithm by setting appropriate inertia weights. Scheduling example data shows that the improved GJO algorithm has better applicability than the original algorithm and grey wolf algorithm for multi objective flexible workshop low carbon scheduling problems, and can obtain higher quality solutions. |
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