细胞形成和布局设计使用遗传算法和TOPSIS:水力工业国有公司的案例研究
Dhulfiqar Hakeem Dhayef1,2, Sawsan S A Al-Zubaidi2, Luma A H Al-Kindi2
1Department of Cooling and Air Conditioning Engineering, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
PloS one
|January 3, 2024
概括
这项研究引入了一种新的元启发式方法,将遗传算法和TOPSIS结合起来,以优化细胞制造系统. 它增强了细胞形成和机器布局,提高了效率和灵活性.
科学领域:
- 运营研究 运营研究
- 工业工程 工业工程 工业工程
- 计算智能是一种计算智能.
背景情况:
- 蜂制造系统 (CMS) 设计涉及到复杂的,NP难题的细胞形成 (CF) 和机器布局.
- 现有的元启发学提供解决方案,但往往缺乏灵活性,无法同时优化多个目标.
- 分组效率的权重因子显著影响CMS设计和细胞大小控制.
研究的目的:
- 开发一种新的元启发策略,将遗传算法 (GA) 和顺序偏好相似性与理想解决方案 (TOPSIS) 的技术集成到CMS设计中.
- 为了优化灵活的细胞形成和细胞内的机器布局.
- 用TOPSIS来确定组分效率的最佳权重因子,以提高系统灵活性和细胞大小控制.
主要方法:
- 基因算法 (GA) 用于机器细胞和部分家族的识别,以组分效率 (GE) 作为适应性函数.
- 对GE的各种权重因子值 (0.1,0.3,0.7,0.5和0.9) 的探索,偏离传统的固定权重.
- 顺序偏好相似度与理想解决方案 (TOPSIS) 的技术用于选择最合适的权重因子.
主要成果:
- 拟议的GA-TOPSIS方法有效地识别了最佳的机器细胞和零件家族.
- 该方法提高了分组效率 (GE),系统利用率 (SU) 和系统灵活性 (SF).
- 实现了总成本的最小化,包括材料处理和细胞间/细胞内移动 (TC).
- 性能与文献中的现有基准实例相提并论或优于它们.
结论:
- 综合GA-TOPSIS方法为复杂的CMS设计问题提供了有效的解决方案.
- 该方法成功地平衡了多个目标:提高效率,利用率和灵活性,同时降低成本.
- 使用TOPSIS来确定最佳权重因子,可以更好地控制CMS的灵活性和细胞大小.
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