Application of queuing theory: A decision support model for construction companies in the implementation of 3D printing

The application of three-dimensional (3D) printing is a heated discussion in the construction industry. The automatic and accurate printing process not only helps reduce manpower and waste but also increases the productivity and project quality in construction. However, the high capital cost is the main barrier to adopting this new technology for companies in this cost-sensitive industry. Forecasting the performance of 3D printing during the design phase can support companies’ decision making on the issue. In this research, a stochastic model was developed to simulate the implementation of 3D printing in on-site and off-site construction. This paper first identifies the influential factors in the two different construction modes. By assuming the arrival of projects using Poisson distribution and the time interval between projects using a negative exponential distribution, a project stream generator for construction companies was developed based on queuing theory. The Excel Visual Basic programming language was adopted to execute the iteration of a Monte Carlo simulation. Data collected from precast concrete parking structure projects were used as the baseline of sensitivity analysis. Then, a sensitivity analysis was conducted to help the companies select the optimal construction strategies in different situations. By using the baseline value as the input of the model, the significant factors were identified as the number of printers, the number of projects, and the duration of printers’ remobilization and the insensitive factors were the duration of components’ transportation, variance of components’ erection and variance of components’ manufacturing hours. Further research will consider economic performance of 3D printers and uncertainties in the two modes.