在废物管理路线问题中,对基于元启发式的优化方法进行重大探索,以优化废物管理路线问题
Gauri Thakur1, Ashok Pal1, Nitin Mittal2
1Department of Mathematics, Chandigarh University, Ajitgarh, India.
Scientific reports
|June 27, 2024
概括
优化城市垃圾收集路线是复杂的. 本研究分析了车辆路线问题 (VRP) 和元启发式解决方案,发现它们对大规模废物管理有效.
科学领域:
- 运营研究 运营研究
- 环境工程 环境工程
- 计算机科学 计算机科学
背景情况:
- 大都会城市面临着优化垃圾收集路线的挑战,原因是垃圾产生量高和人口密度高.
- 低效路线导致城市固体废物管理中大量浪费时间,燃料和资源.
- 车辆路线问题 (VRP) 是有效的废物收集物流研究的关键领域.
研究的目的:
- 系统地分类车辆路线问题 (VRP) 和其变体,特别是在废物收集的背景下.
- 检查用于废物管理的VRP解决方案中的元启发式方法的应用和有效性.
- 确定研究缺口,并提出废物管理路线优化的未来研究方向.
主要方法:
- 2011年至2023年期间发表的关于废物收集中的车辆路线问题 (VRP) 的系统文献分析.
- 对于城市固体废物收集相关的VRP变体的分类.
- 对废物收集VRP应用的元启发算法的评估.
主要成果:
- 大多数审查的研究都采用了元启发式方法来解决垃圾收集问题.
- 对亚洲三个案例研究的分析表明,元启发算法在处理大规模废物收集数据方面具有能力.
- 超启发式算法为优化垃圾收集路线提供了有效的解决方案.
结论:
- 在城市废物管理中,元启发式算法对于解决复杂的车辆路线问题是非常有效的.
- 该研究提供了废物收集中VRP的结构化概述,并强调了元启发学的重要性.
- 未来的研究应侧重于发现的差距,以进一步推进废物管理路线优化.
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