Simulation and optimization of prefabricated building construction considering multiple objectives and uncertain factors

Abstract

The construction of prefabricated buildings involves complex processes, which have high uncertainties and objective conflicts, hindering the improvement of construction performance. This study aims to simultaneously optimize the activity logic and multiple construction performance objectives (concerning time, cost, quality, and CO2 emissions) of prefabricated building construction under the influence of uncertain factors. The eliminate, combine, rearrange, and simplify (ECRS) technique is combined with intelligent simulation technology to develop a simulation-based optimization method for prefabricated building construction considering multiple objectives and various uncertain factors. This method is characterized by enabling activity-logic improvements, revealing mathematical relationships between uncertain factors, activity parameters and construction performance, and achieving the trade-off optimization of construction performance objectives in various uncertain environments. A typical prefabricated building project is used for demonstrating and validating the method. The results show that the proposed method reduces the construction time by 5.73% through the ECRS technique, and further achieves a 10.53% reduction in construction cost, a 10.10% improvement in construction quality, and 60.32% CO2 emission mitigation through multiobjective optimization when only resource allocation is considered. Considering the interaction between resource allocation conditions and other three uncertain factors, the construction time is mainly affected by the supply of materials and weather conditions, while the familiarity level of work affects the construction time, cost, and CO2 emissions. The proposed method and findings of this study can facilitate robust decision-making by project managers when addressing the trade-offs between cost reduction, schedule shortening, quality improvement, and CO2 reduction under various uncertain scenarios.