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有效的社会蜘蛛优化算法用于分布式组件变换流程车间调度问题在汽车制造业供应链中的问题

Weiwei Zhang¹, Jianhua Hao², Fangai Liu³

¹School of Science, Shandong Jiaotong University, Jinan, 250357, China.

Scientific reports

|March 17, 2024

概括

这项研究引入了一个新的制造和装配的调度问题,优化生产效率. 开发的HSSOR算法在最大限度地缩短生产时间方面明显优于现有方法.

科学领域:

运营研究运营研究
工业工程工业工程工业工程
制造系统制造系统的制造

背景情况:

解决了由汽车生产复杂性所启发的新型分布式组装变换流程车间调度问题 (DAPFSP).
包含多部件组件和整合制造流程,与工业4.0目标保持一致.
扩展现有的DAPFSP研究,考虑在最终产品组装之前的部件制造.

研究的目的:

开发和评估用于解决DAPFSP的新型算法,目的是最大限度地减少制作时间.
引入一个针对DAPFSP量身定制的三级表示和初始化方法.
通过特定问题的本地搜索和重启程序来增强优化算法的搜索能力.

主要方法:

引入了DAPFSP的三级表示和新的初始化方法.
设计了三个本地搜索方法和两个重启程序以提高算法性能.
开发了社会蜘蛛优化 (SSO) 算法的三个变体:HSSO,HSSOR和HSSORP,整合了特定问题的知识.

主要成果:

在810个从众所周知的基准问题中衍生的扩展实例上测试了拟议的算法.
该HSSOR算法表现出卓越的性能,达到0.158%的平均比较指标.
HSSOR的表现明显超过了最佳比较方法,该比较方法的平均指标为2.446%,其表现为15.481.1.

结论:

拟议的算法,特别是HSSOR,在解决分布式组件变换流程车间调度问题上是有效的.
整合本地搜索策略和重新启动程序有效地提高了优化性能.
该研究通过优化综合制造和装配流程,为工业4.0做出了宝贵的贡献.

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