在碳排放限制下,用于空运货物订单分配的智能随机优化方法
Zhenzhong Zhang1, Ling Zhang2, Deqiang Fu2
1CAAC Academy, Civil Aviation Flight University of China, Chengdu, Sichuan, China.
PloS one
|April 10, 2025
概括
本研究介绍了空运货物订单分配,平衡效率和碳排放的智能优化方法. 新方法有效地最大化预期利,同时管理不确定的订单到货量,减少环境影响.
科学领域:
- 运营研究 运营研究
- 物流管理物流管理
- 环境可持续性 环境可持续性
背景情况:
- 有效的订单分配对于航空货物效率和减少环境影响至关重要.
- 现实世界的航空货物面临着诸如不确定的客户订单到达和碳排放成本等挑战.
- 优化订单分配对于最大化利和最小化生态足迹至关重要.
研究的目的:
- 开发航空货物订单分配的随机优化模型.
- 将碳排放成本和不确定的订单到达纳入模型.
- 为了最大限度地提高航空货物运输的预期利.
主要方法:
- 研究了一个高维度随机顺序分配问题.
- 建立了一个随机优化模型,考虑碳排放成本.
- 引入了一种智能优化方法,结合了改进的适应性大规模社区搜索和场景生成.
主要成果:
- 拟议的方法有效地解决了在碳排放限制下订单分配的问题.
- 与现有方法相比,智能优化方法展示了优越的优化能力.
- 实验结果证实了该方法的增强优化效率.
结论:
- 开发的智能优化方法为航空货物订单分配提供了有效的解决方案.
- 该方法成功地平衡了经济目标与环境考虑.
- 这项研究有助于更可持续,更有利可图的航空货运运营.
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