To move precast concrete (PC) components to the site through vehicles in a PC project, a construction manager must establish a PC components–vehicle allocation (CVA) plan everyday by integrating factory, transport, and site information and by reflecting various considerations for CVA. However, in practice, the manager establishes the plan manually, which often leads to an increase in transportation costs due to inefficient use of vehicles and plan errors. Thus, this study develops an automated method for CVA by deriving considerations for CVA via interviews and literature review and by formalizing a CVA process that satisfies the considerations. The process has the following five steps: determining component loading type, developing preliminary CVA, determining vehicle loading type, crashing CVA for reducing the number of vehicles, and setting site arrival time and type of vehicles. Charrette test results indicate that the automated method can generate CVA plans faster than practitioners and reduce the number of vehicles by taking the derived considerations into account comprehensively. This study contributes to the off-site construction management theory by formalizing the process knowledge about how to deal with different considerations in CVA. This can help alleviate social and environmental problems, such as increased transportation cost and carbon dioxides emission by supporting decision-making of construction managers in planning installation of PC components and allocating them to vehicles.

Delays in the installation process of precast concrete (PC) components significantly impact the project execution. However, traditional scheduling and risk assessment methods fail to consider this process complexity and uncertainty adequately. With a systematic approach, this study analyzed complex delay mechanisms in the PC installation of three component types (columns, beams, and slabs) using 1881 observations across five work steps. Specifically, this study used k-means clustering to divide the observations into groups with certain characteristics. These groups were assessed quantitatively using the delay intensity metric. Based on the assessment, this study revealed six severe delay paths for different component types, which may combine to generate severe combinations of delay factors, considering factors such as the component size, wind conditions, worker availability, and installation location. This research contributed to PC construction management by presenting a systematic analysis of delay factors and by proposing specific severe delay paths during PC installation, offering project managers a basis for schedule optimization and risk management.