一个新型的参数化的中性学分数函数及其在遗传算法中的应用
Yi Zhao1, Fangwei Zhang2, Bing Han3
1College of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing, Jiangsu, China.
PeerJ. Computer science
|December 13, 2024
概括
这项研究引入了一种新的参数化的中性学客观比例遗传算法 (PNO-PGA),以优化仓库运营. PNO-PGA模型提高了效率,安全性和成本管理,超过了传统的遗传算法 (GA).
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 人工智能的人工智能
背景情况:
- 仓库运营是根据效率,安全和成本来评估的.
- 这些因素的不确定性会导致严重的经济损失和资源浪费.
研究的目的:
- 为优化仓库效率,安全性和成本提出一种新的参数化的中性学客观比例遗传算法 (PNO-PGA).
- 解决这些关键运营指标中固有的不确定性.
主要方法:
- 开发一个新的评分函数,用于中性质集 (NSs),以整合效率,安全性和成本评估中的模糊性.
- 适应性代和遗传的新型比例遗传算法的应用.
- 引入两个参数,以灵活适应各种环境和问题.
主要成果:
- 拟议的PNO-PGA模型有效地整合了模糊的评估指标.
- 该算法展示了自适应代和继承能力.
- 与标准遗传算法 (GA) 相比,PNO-PGA在仓库运营中显示出优越的解决问题的能力.
结论:
- PNO-PGA模型提供了一种强大的方法来优化复杂的仓库运营.
- 该方法有效地处理评估标准中的不确定性和模糊性.
- 这项研究为提高仓库性能和资源管理提供了有价值的工具.
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