绿色城市物流路径的规划和设计基于遗传算法
Limin Ran1, Shengnan Ran2, Chunmei Meng3
1Flight College, Anyang Institute of Technology, Anyang, Henan, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究引入了用于物流路由的增强基因算法,提高了对时间敏感订单和反向分发的效率. 新方法的性能优于其他优化算法,确保更好的全球解决方案.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 供应链管理 供应链管理
背景情况:
- 有效的物流配送对于企业的竞争力至关重要.
- 挑战包括不理想的调度和反向分销物流.
- 现有的算法可能会在本地优化方面遇到困难.
研究的目的:
- 开发用于物流路由的增强基因算法.
- 为了解决次优调度和反向分配问题.
- 提高物流的全球优化能力.
主要方法:
- 整合大都市标准与遗传算法.
- 将遗传算法与模拟化相结合,用于增强本地搜索.
- 保存最佳染色体和概率性接受劣质的染色体.
主要成果:
- 拟议的基因算法与大都会标准证明了卓越的性能.
- 与标准遗传算法相比,实现了更好的全球优化.
- 经验性比较显示,与群优化,人工蜂群和粒子群优化相比,它们具有优势.
结论:
- 改进的遗传算法有效地解决了复杂的物流路由问题.
- 整合大都会标准和模拟回火的整合提高了优化.
- 这种方法提供了一个强大的解决方案,以提高物流效率和竞争力.
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