A Two‐Stage Optimization Model for Multiple‐Crane Service Schedules Problem

Scheduling tower crane movements is essential in a large construction site where multiple cranes are deployed. Efficient planning can avoid potential crane collisions, reduce construction duration, and save energy consumption. The multiple‐crane service schedules problem (MCSSP) is a classic combinatorial problem. The growth of tower cranes and material requests can exponentially increase the solution space. To address this challenge, this study proposes a two‐stage optimization model to efficiently identify high‐quality solutions in the space. In the first stage, the proposed model can adequately group the material requests around tower cranes by evaluating the material transportation distance. In the second stage, the model can efficiently schedule the crane movements to complete the grouped requests while keeping collision‐free. To balance the trade‐off between solution quality and computational efficiency, an exact algorithm implemented in Gurobi and an improved genetic algorithm (IGA) were used to solve the model in each stage. Two construction projects demonstrated the effectiveness of the model and solution methods. In the first project, the proposed grouping strategy and IGA can significantly speed up the computation time from 3.8 to 234.85 times compared with the previous research’s solutions. The total energy cost can be maintained at high quality while fluctuating between −1.42% and 0.36%. The second project presented two scenarios in which material requests must be completed regularly or urgently. The model implemented two scheduling strategies controlled by minimizing the total energy cost or the makespan. The results reveal that fully utilizing the tower cranes to complete the requests urgently can save 15.37% to 49.67% of the makespan while consuming about 10% more energy cost.