A study on deep reinforcement learning-based crane scheduling model for uncertainty tasks

Abstract

Abstract Aiming at the crane scheduling problem for uncertainty tasks in multi-crane scheduling situation, this article proposes a deep reinforcement learning-based crane scheduling modeling method that is not dependent on mathematical planning and has certain generality. First, the crane scheduling process is integrated into deep reinforcement learning framework in which the orbit space of the crane and the transportation task is environmental information and crane is the intelligent agent. Second, the interactive mode between reinforcement learning algorithm and environment is adjusted to adapt to the combined learning of multi-crane scheduling model. Last, the crane scheduling model for uncertainty tasks is constructed by optimizing reward discount factor, learning rate, and reward function intensive mode. Testing of the model is carried out based on practical crane scheduling in one steelmaking workshop. Scheduling proposal is generated and all crane tasks are completed within the planned time, which verifies the feasibility of this model. Results show that compared with manual scheduling plan, the scheduling proposal based on the new model reduces total task completion time by 11.52%, time of collision of crane routes decreases by 57.14%, and negative crane transportation distance shortens by 55.26%. The high efficiency of the scheduling model is therefore verified.