Scholarlink

<p>Modular construction (MC) is a sound strategy to alleviate global issues such as housing crisis, labor shortage, and stagnant productivity. Project managers are aspired to achieve a Just-in-Time (JIT) delivery of their MC logistics. However, the efforts often fall short without the presence of a dedicated Estimated Time of Arrival (ETA) model. This study aims to bridge this gap by developing a MC-oriented ETA model. It does so by first identifying critical factors influencing ETA accuracy in general logistics and then developing an ETA model prototype, which is then calibrated using simulations and data collected from the Internet of Things (IoTs) applied in real-life MC projects in Hong Kong, China. Validated through a cross-border MC project in China’s Greater Bay Area, the MC-oriented ETA model achieved 90.6% prediction accuracy (±10 min), reduced transportation delays by 37.5%, and slashed daily planning time from 46.75 to 18.75 min. It is expected that the ETA model can be used in predictive planning of MC logistics delivery in the future. Ultimately, it can lead to the development of smart logistics planning and control systems to expedite MC housing delivery and alleviate urban congestion in high-density cities, offering valuable insights for policymakers, construction stakeholders, and supply chain managers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An estimated Time of Arrival (ETA) model for achieving Just-in-Time (JIT) modular construction delivery in high-density cities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月22日

查看全文
  • Liupengfei Wu,
  • Weisheng Lu,
  • Xiaohan Wang,
  • Bolun Wang,
  • Zhiming Dong

摘要

Modular construction (MC) is a sound strategy to alleviate global issues such as housing crisis, labor shortage, and stagnant productivity. Project managers are aspired to achieve a Just-in-Time (JIT) delivery of their MC logistics. However, the efforts often fall short without the presence of a dedicated Estimated Time of Arrival (ETA) model. This study aims to bridge this gap by developing a MC-oriented ETA model. It does so by first identifying critical factors influencing ETA accuracy in general logistics and then developing an ETA model prototype, which is then calibrated using simulations and data collected from the Internet of Things (IoTs) applied in real-life MC projects in Hong Kong, China. Validated through a cross-border MC project in China’s Greater Bay Area, the MC-oriented ETA model achieved 90.6% prediction accuracy (±10 min), reduced transportation delays by 37.5%, and slashed daily planning time from 46.75 to 18.75 min. It is expected that the ETA model can be used in predictive planning of MC logistics delivery in the future. Ultimately, it can lead to the development of smart logistics planning and control systems to expedite MC housing delivery and alleviate urban congestion in high-density cities, offering valuable insights for policymakers, construction stakeholders, and supply chain managers.