在学习和处理时间的不确定性下,AGV辅助机器人灵活流量车间的能效调度
Saeed Dehnavi1, Hadi Mokhtari2, Mohammad Taghi Rezvan2
1Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran. dehnavi@kashanu.ac.ir.
Scientific reports
|December 16, 2025
概括
本研究介绍了一个节能灵活的流量车间调度问题,将自动引导车辆和学习效应集成在一起. 基于模糊的NSGA-II为可持续制造提供了卓越的解决方案.
科学领域:
- 运营研究 运营研究
- 制造系统工程 制造系统工程
- 计算智能是一种计算智能.
背景情况:
- 灵活流量车间调度问题 (FFSP) 在制造业中至关重要.
- 整合自动引导车辆 (AGV),依赖序列的设置时间和学习效应带来了复杂的挑战.
- 能源效率在现代生产系统中是一个日益关注的问题.
研究的目的:
- 开发一个节能灵活的流量工厂调度问题 (EEFFSP) 模型.
- 将模糊不确定性纳入处理时间和学习系数.
- 为了同时最大限度地降低makepan和总能耗.
主要方法:
- 制定了一个混合整数编程模型.
- 使用Jiménez排名方法的模糊编程来处理不确定性.
- 使用AUGMECON,NSGA和NSGA-II算法进行了多目标优化.
主要成果:
- 基于模糊的NSGA-II算法在提供高质量的帕雷托解决方案方面表现出卓越的性能.
- 拟议的混合框架实现了能源效率和生产性能之间的平衡.
- 与其他方法相比,NSGA-II显示了增强的解决方案多样性和稳定性.
结论:
- 该研究介绍了EEFFSP中模糊性,学习效应和AGV调度的新整合.
- 基于模糊的NSGA-II对于复杂,不确定的生产环境是有效的.
- 研究结果为设计可持续制造系统提供了宝贵的见解.
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