.png){kind=icon}
返回
Optimizing demand-responsive IoT-based waste collection services: a two-step clustering technique
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rtbm.2025.101302.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Internet of Things (IoT) can bring radical advancements in the domain of waste collection, as it enables the organization of demand-responsive schedules which leads to higher efficiency operations. One major challenge in the deployment of demand-responsive schedules, nevertheless, is the uncertainty they bring in the planning of resources as they follow the daily waste demand. This is undesirable in real-life operations as it makes it difficult to reserve resources and ensure the stability of operational processes. Therefore, waste collection scheduling approaches need to be devised that are not only demand-responsive but also supply-friendly. In this paper, we present a solution approach for the waste collection vehicle routing problem in an IoT context (IoT-WCVRP) that focuses on these requirements. We demonstrate its applicability through a case study of Rotterdam in The Netherlands, where real-life household waste data are used and the observed waste collection operations in the city are compared against the optimized outcomes of the model. The application results show that our IoTWCVRP approach achieves the stated demand and supply trade-off, increases the vehicle utilization rates by 5%, and reduces emissions and travelled kilometres by 6% and 8% respectively.
Keyword:
IoT
WCVRP
Demand-responsive
Clustering methods
Urban waste collection
Reverse Logistics
期刊
R
IF:
4.4
论文数:
1.2K
被引数:
3.4K
机构
引用论文
Transmission dynamics of SARS-CoV-2: A modeling analysis with high-and-moderate risk populations
OAAI
OAAI分享收藏
Assessing the Environmental Performance of Municipal Solid Waste Collection: A New Predictive LCA Model评估城市固体废物收集的环境绩效: 一种新的预测LCA模型.png)
SUSTAINABILITYOAAI
SUSTAINABILITYIF3.3
OAAI分享收藏
查看更多内容