使用改进的遗传算法分析车辆路径优化,在随机扰动物质存在的情况下
Shengdong Mu1,2,3, Boyu Liu4, Gu Jijian2
1Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing, 408100, China.
Scientific reports
|October 31, 2024
概括
本研究介绍了一种改进的遗传算法 (GA),用于在不同的条件和碳税下优化物流路线. 增强的GA显著加快了融合速度,并减少了车辆路径优化计算时间.
科学领域:
- 运营研究 运营研究
- 物流和供应链管理的物流和供应链管理.
- 计算智能是一种计算智能.
背景情况:
- 车辆路径优化对于高效的物流和分销至关重要.
- 随机扰动,时间窗口变化和负载能力限制使路线规划复杂化.
- 碳税机制为分配策略增加了经济层面.
研究的目的:
- 开发一个包含碳税的物流和配送的扰乱调度模型.
- 提出一个增强的基因算法 (GA) 与高斯矩阵突变 (GMM) 运算符,以提高优化.
- 为了解决车辆路径优化中的时间窗口变化和负载能力限制.
主要方法:
- 建立了物流的扰动调度模型,考虑了随机性和碳税.
- 开发了一种使用高斯矩阵突变 (GMM) 运算符的增强遗传算法 (GA).
- 基于基因突变的位点位置顺序构建了一个高斯概率矩阵.
- 采用了轮盘轮选择方法来引导人口进化.
主要成果:
- 与经典的GA相比,增强的GA显示平均收速度增加了50-60%.
- 借助提出的方法,算法执行时间减少了48%.
- 溶液的精度保持在1%的差异范围内.
- 实验模拟验证了基于GMM的GA的有效性.
结论:
- 增强的GA有效地优化了复杂的约束和碳税政策下的车辆路径.
- 该GMM运营商成功地平衡了种群多样性和融合速度.
- 拟议的模型为物流和分销业务的效率提供了显著的改进.
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