Solving multi-mode resource-constrained scheduling problem of prefabricated construction using genetic algorithm

Purpose This study aims to optimize and solve the prefabricated project scheduling model, which can simultaneously consider the precedence relationship constraints of activities, resource limitations and supply time constraints of prefabricated components. The scheduling plan obtained by the optimization model can guide the project manager to reasonably organize the execution process of the construction project and rationally allocate resources to improve project performance. Design/methodology/approach The paper proposes a multi-mode construction scheduling model for prefabricated projects, which enriches the key constraints of the scheduling model and reflects the construction characteristics of the off-site production and installation process of assembly projects. In addition, an improved genetic algorithm was designed to solve the model, and its effectiveness was verified using the PSPLIB dataset, followed by the application of the scheduling model to a practical construction project. Findings The proposed algorithm, validated using PSPLIB datasets, demonstrates superior accuracy and efficiency than the methods discussed in the literature referenced in this paper. It achieves better results, with average deviation rates of 0.00, 0.10 and 0.31% for the J12, J18 and J20 datasets, respectively. Applied to a real-world case, the model significantly reduces project durations and enhances resource utilization compared to the traditional CPM method. The algorithm also effectively minimizes delay impacts by optimizing activity sequences and resource allocation, ensuring strong practical applicability. Originality/value This research tackles the scheduling problem of prefabricated projects by considering multiple execution modes for each activity and the supply time constraints of components based on actual construction conditions. An improved genetic algorithm is used to obtain the shortest duration scheduling scheme that meets process priority and resource feasibility. Numerical experiments with PSPLIB examples show the algorithm’s competitive quality, aiding project managers in developing effective schedules and resource allocation. The study also highlights the algorithm’s ability to optimize resource use and minimize delays, ensuring efficient handling of unexpected postponements and enhancing project efficiency.