Optimization of Multi-Building Tower Crane Scheduling for Lean Prefabricated Construction

Tower cranes (TCs) as essential lifting equipment in construction engineering, play a critical role in prefabricated buildings (PBs). However, current construction scheduling primarily relies on manual observation and operator experience to execute repetitive tasks, leading to low efficiency, heavy workload, and potential safety risks. In typical PB construction projects, multiple buildings are often constructed in parallel, where each TC is assigned to serve a specific group of buildings independently. This allocation strategy is generally predetermined by the site layout plan to ensure operational safety and avoid inter-crane interference. To enhance lean construction performance and management efficiency in PBs, this study develops a scheduling optimization model that explicitly considers the initial hook position and the specific locations of prefabricated component (PC) supply and demand points. The proposed model is solved and compared using three meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Numerical results indicate that PSO outperforms GA and ABC in terms of convergence speed and cost minimization performance. After optimization, the operating times of two TCs are reduced by 23.94% and 12.16%, respectively, saving ¥207.29 and ¥293.96 per day in operating costs, and reducing total construction cost by approximately 8.0%. These results demonstrate that the proposed model can effectively improve construction efficiency and support lean management under the considered planning assumptions.