一个基于遗传算法的自学超听觉算法:关于游轮制造商预制模块化客单元物流调度的案例研究
Jinghua Li1,2, Ruipu Dong3, Xiaoyuan Wu4
1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
一个新的自我学习超启发式算法优化游轮物流. 这种基于遗传算法的方法显著减少了预制模块化客单元的运输时间,提高了复杂的调度问题的效率.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 物流管理物流管理的管理.
背景情况:
- 工程优化问题往往涉及复杂的约束.
- 元启发式算法可以在评估和修复不可行的解决方案方面扎.
- 游轮预制模块化客单位 (PMCUs) 的物流调度提出了多目标的模糊挑战.
研究的目的:
- 引入一个自我学习的超启发式算法 (GA-SLHH) 来优化游轮PMCUs物流.
- 提高解决复杂,多目标模糊物流调度问题的效率和稳定性.
- 在现实世界制造场景中验证算法的有效性.
主要方法:
- 开发了一种自学超启发式算法 (GA-SLHH),使用基因算法作为高级策略.
- 通过结合自学策略和经典调度规则,优化了低级别启发式学习 (LLHs).
- 进行了多组数值实验,并与实际的企业案例进行了验证.
主要成果:
- 与其他方法相比,GA-SLHH表现出优越的综合优化能力和稳定性.
- 该算法有效地解决了多目标模糊物流协作调度的挑战.
- 实际的案例研究证实了该算法在游轮制造中的适用性.
结论:
- 该GA-SLHH是一个强大的和高效的算法复杂的物流调度问题.
- 拟议的方法可以显著减少运输时间,在实际应用中实现高达37%的减少.
- GA-SLHH为游轮行业的现实决策提供了可行的解决方案.
关键词:
在PMCU中使用PMCU.游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游轮游船游轮游轮游船游轮游船游轮游船游轮游船游轮游船游轮游轮游船游轮游船游轮游船游轮游轮游船游轮游船游轮游船游轮游船游轮游船游轮游轮游船游轮游轮游轮游船游轮游船游轮游船游轮游船游船游轮游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船游船模糊的物流调度时间表基于遗传算法的自我学习超启发式算法.
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