一个改进的遗传算法与动态的邻里搜索工作商店安排问题问题
Kongfu Hu1, Lei Wang1, Jingcao Cai1,2
1School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
这项研究介绍了一种改进的基因算法 (IGA) 用于工作商店调度问题 (JSP),以最大限度地减少制作时间. IGA在基准实例上展示了竞争性表现.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 工作室调度问题 (JSP) 是一个复杂的组合优化挑战,具有重大实际影响.
- 尽量减少制作时间 (完成所有工作的总时间) 是JSP的主要目标.
研究的目的:
- 引入一个改进的遗传算法 (IGA),旨在有效解决工作车间安排问题.
- 通过结合动态邻居搜索和新型遗传运算符来提高JSP遗传算法的性能.
主要方法:
- 开发一个IGA,结合动态的邻里搜索.
- 在解码过程中引入了一个基于空时间的插入操作.
- 介绍了一个改进的变换与操作交叉 (POX) 操作员.
- 设计一个新的突变操作,用于邻里解决方案的探索.
- 使用动态基因银行的新遗传重组策略的实施.
- 应用精英保留策略的应用.
主要成果:
- 通过使用几个标准的JSP基准来评估IGA.
- 计算结果表明,IGA实现了有前途的,具有竞争力的减少损伤的结果.
- 拟议的改进有助于提高解决JSP的性能.
结论:
- 开发的IGA提供了一种有效的方法来解决工作室调度问题.
- IGA的新型组件,包括动态社区搜索和改进的遗传操作员,增强了其解决问题的能力.
- 该算法在基准实例上的性能验证了其在调度中的实际应用潜力.
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