Schedule delay analysis of prefabricated housing production: A hybrid dynamic approach

Abstract

Abstract Design, manufacturing, storage, transportation, and on-site assembly are fundamental processes in prefabricated housing production (PHP) in Hong Kong. However, these processes are fragmented and entail various risks that adversely affect the schedule performance of PHP, thereby causing frequent delay problems in PHP projects and hindering the government from satisfying the high housing demands. Accordingly, many researchers have examined these schedule risks to resolve these delay problems. However, none of these studies developed an effective tool for managing schedule risks of PHP by envisaging the key characteristics of schedule risks and PHP. Most of previous research regarding to the management of prefabrication construction tends to consider risks from static and isolated perspectives, despite that these risks are coherently interrelated with each other and their influence varies throughout the whole PHP process. To fill the research gaps, a hybrid dynamic model is developed in this research to evaluate and simulate the impact of identified schedule risks on the schedule performance of PHP in view of underlying interrelationships and interactions, employing the hybrid system dynamics (SD) and discrete event simulation (DES) method. The resulting hybrid model is validated through a serial of model structure tests and model behavior tests, with the use of data collected from a PHP project in Hong Kong. This study offers an in-depth understanding of how schedule performance of PHP are dynamically influenced by interrelationships and interactions underlying various schedule risk variables. The developed model not only has the benefits of ease of modifying model structure to reflect real schedule situation of PHP project, performing various risk analyses and communicating with simulation results, but also is of value of providing an experiment platform for identifying and determining managerial and technical solutions proposed to minimize and mitigate the influence of corresponding schedule risks prior to implementation.