The construction sector is currently undergoing a shift from in-situ construction to modular building systems that take advantage of modern prefabrication techniques. Long established conventional on-site building construction practices are thus being replaced by processes imported from the manufacturing sector, where component fabrication takes place within a factory environment. In this context, similar to manufacturing industries, the productivity of building components fluctuates, owing to human error and machine malfunction. Since the site demand must always be met, outsourcing manufacturing is employed to supplement the uncertain self-production. As a result of these transformations, the current construction supply chains, which have focused on the delivery of raw materials to sites, are no longer apt and need to make way to new, strengthened, and time-critical logistics systems.
Moreover, previous studies have indicated that construction site delays constitute the most significant cause of schedule deviations. Thus, the primary reasons for causing schedule deviation need to be identified. In reality, owing to the complex structure of construction projects and long execution time, non-conformance in schedules occurs by a chain of events. Therefore, this research intends to investigate the factors causing schedule deviation in construction projects and understand the cause-effect relationships between the events leading to delays by implementing fault tree analysis.
The primary aim of this research is to develop mathematical models by mixed-integer linear programming, two-stage stochastic programming and robust optimisation for revealing optimal logistic configurations for modular construction projects under various prevalent site demand perturbations. The models’ outputs encompass the most favourable manufacturing, storage, transportation and outsourcing schemes, along with considerations of the best consolidation centre location for yielding the lowest modular construction supply chain operational cost. The applications of case studies demonstrate that the proposed models are effective and can serve as the foundation for a decision support system that optimises modular construction logistics under operational uncertainty.