LAUSR

Abstract Construction hoists at most building construction sites are manually controlled by human operators using their intuitions; as a result, unnecessary trips are often made when multiple hoists are operating simultaneously and/or when complicated hoist calls are requested. These trips increase a passenger's waiting time and lifting time, reducing the lifting performance of the hoists. To address this issue, the authors develop an autonomous hoist supported by a deep Q-network (DQN), a deep reinforcement learning method. The results show that the DQN algorithm can provide better control policy in complicated real-world hoist control situations than previous control algorithms, reducing the waiting time and lifting time of passengers by up to 86.7%. Such an automated hoist control system helps shorten the project schedule by increasing the lifting performance of multiple hoists at high-rise building construction sites.