A DES simulation aided construction planning of modular integrated construction: A Hong Kong Case Study
Keywords:
Carbon emission, Discrete Event Simulation (DES), Modular Integrated Construction (MiC), Performance evaluation, Resource planningAbstract
Many countries face ongoing pressures from limited land resources and housing shortages, which has driven planners to seek more efficient construction methods to meet the growing demand for high-density and sustainable housing. Given that, Modular Integrated Construction (MiC) has emerged as an innovative solution that transforms conventional fragmented on-site construction into integrated off-site production and on-site assembly of prefabricated modules. Previous research on MiC has explored various aspects, such as schedule optimization, transportation arrangements, and site management strategies. However, most studies have overlooked the uncertainty associated with off-site resource planning and activity durations. Meanwhile, there is still a lack of a decision support system for MiC that can comprehensively evaluate project performance. Therefore, this study develops a Discrete Event Simulation (DES) model to simulate the MiC construction process while considering uncertainty, aiming to optimize comprehensive performance, including cost, duration, and carbon emissions. After incorporating activity durations and available resources, this study continuously adjusts off-site and on-site resource planning to determine the optimal resource configurations and identify the factors that most significantly impact performance. The DES model is applied to a case study in Hong Kong after validation. The results show that MiC performance is more sensitive to off-site resources, like trucks, cranes, and wrapping crews. Furthermore, the weight of key performance indicators also significantly affects resource planning. The proposed DES model provides practical support for MiC construction, enabling managers to make better decisions under multiple objectives and complex constraints, thereby optimizing project resource planning and overall performance.