一个增强的基于基因的多目标数学模型,用于工业供应链网络
1Department of Basic Sciences, Jilin University of Architecture and Technology, Changchun, Jilin, China.
PloS one
|March 4, 2025
概括
本研究引入了一种改进的遗传算法,以优化工业供应链,显著降低成本,运营时间,并提高资源调度效率,以实现更好的网络平衡.
科学领域:
- 运营研究 运营研究
- 工业工程 工业工程 工业工程
- 计算机科学 (软件计算)
背景情况:
- 工业供应链需要全面的成本分析,产品交付协调和提高网络效率.
- 现有的方法经常忽视工业供应链网络的特殊复杂性.
研究的目的:
- 开发一种用于多目标工业供应链问题的新型模型.
- 提高新兴工业供应链网络中的效率和平衡.
主要方法:
- 使用改进的遗传算法 (GA) 的元启发式方法.
- 一种混合方法,将拓理论和初始人口生成的随机搜索结合起来.
- 增强的交叉和突变操作,其概率由精英选择和滚球方法确定.
主要成果:
- 从0.678降低到0.535.5 的供应负载.
- 劳动力成本从1832年降至1790元.
- 运行时间缩短了39.5% (从48秒降至29.5秒).
- 节点利用率的变化明显减少 (从30.1%降至12.25%).
结论:
- 改进的遗传算法有效地解决了多目标的工业供应链挑战.
- 实现了提高资源安排效率和整体供应链平衡.
- 开发的模型为优化复杂的工业网络提供了强大的解决方案.
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