城市废物管理中的路线优化使用局部调整的离散子搜索:一种混合的元启发式方法
Anuradha Goswami1, Poornima N V2, Prabu P3
1Symbiosis Institute of Business Management, Bengaluru, Symbiosis International University, Pune, India.
Scientific reports
|February 21, 2026
概括
本研究介绍了局部优化离散搜索 (LO-DCS) 优化城市废物管理路线,显著减少旅行距离,燃料消耗和排放. 新的框架为可持续城市规划提供了数据驱动的解决方案.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 城市化和人口增长增加了废物产生,需要有效的废物管理.
- 优化废物收集路线对于降低运营成本和环境影响至关重要.
- 现有的方法很难解决城市环境中离散路由问题的复杂性.
研究的目的:
- 提出一个混合的元启发框架,局部优化离散子搜索 (LO-DCS),以有效优化城市废物管理中的路线.
- 为了适应Cuckoo Search算法对离散路由问题,使用基于排列的随机步行,2-opt本地优化和K-means集群.
- 用现实世界城市数据集评估LO-DCS的性能,并与现有算法进行比较.
主要方法:
- 开发了一个混合的元启发框架,LO-DCS,将Cuckoo Search与离散优化技术集成在一起.
- 利用谷歌地球引擎和地理引用的卫星图像进行垃圾桶坐标提取.
- 在班加罗尔城市数据集上实施和测试LO-DCS,测量行程距离,燃料消耗,碳排放和运行时间.
主要成果:
- 在LO-DCS中,关键性能指标 (距离,燃料,二氧化碳排放,行程时间) 的集群平均改善了约85%.
- 取得了显著的改进 (大约. 78%) 与基线方法 (GA,PSO,DSMO,QANA) 相比,具有很强的趋同.
- 统计测试证实了LO-DCS的稳定性和竞争性性能,产生了接近最佳的解决方案.
结论:
- LO-DCS提供了一个可扩展的,数据驱动的决策支持工具,用于可持续和具有成本效益的城市废物管理.
- 该框架为市政当局和基础设施规划研究人员提供了宝贵的见解.
- 这种方法提高了城市废物物流中的环境责任.
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