关于优化不确定的多阶段生产的研究 基于改进的灰狼优化器集成决策
Weifei Gan1, Xin Zhou2, Wangyu Wu3
1School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai 201209, China.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
本研究引入了一个新的框架,通过整合质量检查和决策来管理生产缺陷,提高复杂制造环境中的效率和利.
科学领域:
- 运营研究 运营研究
- 制造系统工程 制造系统工程
- 人工智能的人工智能
背景情况:
- 多阶段生产中缺陷率的不确定性导致运营效率低下和资源分配不当.
- 现有的模型往往将质量检查与工厂决策分开,限制了适应性反应.
研究的目的:
- 开发一个综合框架 (MsPID) 统一质量检查和生产决策.
- 为了解决缺陷率的不确定性和优化多阶段制造业务.
- 提高生产系统的效率,弹性和利能力.
主要方法:
- 开发了一个多阶段的生产综合决策 (MsPID) 框架.
- 结合了两阶段的抽样检查政策和多过程,多部分的生产决策模型.
- 使用一个改进的灰狼优化器 (IGWO) 与拉丁超立方体采样 (LHS),进化机制 (SBX) 和基于对立的学习进行优化.
主要成果:
- 确定了单阶段生产基准的最低成本政策.
- 在多个阶段的生产场景中,实现了43,800辆的总利.
- 在不确定的制造环境中表现出强大的性能和适应能力.
结论:
- MsPID框架有效地管理多阶段生产中的缺陷率不确定性.
- 生物灵感优化可实现适应性,高效性和弹性的综合生产管理.
- 敏感性分析证实了决策能够适应不断变化的操作参数.
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