Grant: Collaborative Research and Development Grants

Prefabricated home building has become a popular construction method among home builders due to the associated improved productivity, reduced waste, and increased quality. This system divides a building into wall, floor, and roof elements, which are manufactured in a plant and then shipped to site and installed. Given that the majority of activities in the panelized approach are performed in a factory setting, the controlled environment makes it possible to greatly improve productivity. This system provides a high level of flexibility, such that wall, floor, and roof panels can be custom-manufactured based on the project requirements. However, its full potential in terms of productivity improvement has not yet been realized due to the absence of an automated production control system. We thus propose to develop a process improvement model for prefabricated panelized home production facilities utilizing a radio frequency identification (RFID) system, discrete-event simulation (DES), and optimization technologies. The process improvement model will utilize building information modelling to obtain wall, floor, and roof panel information and optimize the production process using simulation to reduce material waste and production time. The model will also use RFID to provide a real-time location system and update the simulation model input parameters. As a general result of this research, the industrialized construction domain is expected to benefit in terms of improved productivity and reduced material waste through integrated simulation-based optimization and RFID system. The proposed research will reduce production cost by streamlining the process, which can be achieved by applying simulation, optimization, and RFID technology. It will also help the construction industry to move towards panelized production by providing effective production tools which will reduce production cost and environmental impact and will extend its benefit towards other construction sectors by providing a solution to the production scheduling problem through simulation-based optimization.