Jientrakul, Ranon

Loading...
Thumbnail Image
Preferred name
Jientrakul, Ranon
Main Affiliation
Email
ranon.ji@kmitl.ac.th

Settings

Now showing 1 - 1 of 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Crowdsourced adaptive vehicle routing framework for Last-Mile delivery in dynamic traffic environments
    (2025-01-01)
    Dahlan, Ahmad Faisal
    ;
    Cheng, Chen Yang
    ;
    Sae-chai, Pornkanok
    ;
    Jientrakul, Ranon
    ;
    Yuangyai, Chumpol
    Last-Mile Delivery (LMD) operations are significantly impacted by real-time traffic disruptions, leading to delays and increased costs. While traditional vehicle routing problem (VRP) model struggle to adapt to dynamic traffic environments, crowdsourced data from social media platforms presents a valuable source of real-time traffic data. This research proposes Crowdsourced Adaptive Vehicle Routing Framework (CAVRF) that integrates crowdsourced social media data into the VRP model for enhanced efficiency. The framework employs a machine learning model to classify tweets based on impact severity and effectively filtering relevant traffic information. Furthermore, a mathematical model known as the Adaptive Traffic VRP (AT-VRP) has been developed to accommodate the integration of social media data with the VRP model. The framework’s effectiveness is demonstrated through a case study using a package delivery network in Jakarta with various levels of traffic disruptions. The findings suggest that integrating crowdsourced social media data into AT-VRP significantly improves efficiency by avoiding any road closure. CAVRF offers a cost-effective and efficient solution to the dynamic challenges inherent in LMD.