通过数据驱动的废物收集优化,提高自愿回收项目的运营效率
Sanyapong Petchrompo1, Rasita Chitniyom1, Naplaifa Peerwantanagul1
1Mahidol University, Department of Mathematics, Faculty of Science, Bangkok, 10400, Thailand; Mhesi, Centre of Excellence in Mathematics, Bangkok, 10400, Thailand.
Waste management (New York, N.Y.)
|March 19, 2025
概括
这项研究优化了发展中国家非营利性回收项目的塑料废物收集. 数据驱动的方法显著增加了收集的塑料,并减少了旅行距离,提高了运营效率.
科学领域:
- 运营研究 运营研究
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 发展中国家的回收利用依赖于自愿的,通常是非营利性的,私营部门的倡议.
- 运营效率是这些举措的一个主要挑战,特别是在物流领域.
- 传统的优化方法,如旅行销售员问题,对于实际的,日常的使用来说太耗时了.
研究的目的:
- 为非营利性回收项目开发数据驱动的三步方法,以优化废物收集物流.
- 在资源有限的环境中提高废物收集操作的效率和实用性.
- 为了应对最大限度地利用收集的材料和最小化旅行距离的挑战.
主要方法:
- 采集点的地理分组使用K-means集群.
- 优化模型以最大限度地提高每次旅行的可回收塑料,并确定有效的路线.
- 通过智能交通信息中心集成实时废物量数据和实时交通信息.
主要成果:
- 在收集的平均塑料量中,每天显著增加.
- 实现了可观的减少废物收集所需的平均距离.
- 通过对Won项目152个收集点的案例研究来验证该方法.
结论:
- 拟议的数据驱动方法为日常垃圾收集操作提供了快速,可靠的解决方案.
- 开源软件可以实现实际实施,克服自愿回收项目中的后勤障碍.
- 该方法提高了发展中国家回收计划的可持续性和有效性.
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