Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service Times

Many real-world processes are of dynamic nature and therefore subject to change. In this paper, dynamic warehouse operations are taken care of, more specifically crane operations that involve moving steel coils between storage locations within a large warehouse. An open-ended optimization approach is employed to create an optimal schedule of crane moves given a set of requested crane operations. Conventionally, the problem model defines static crane speeds and service times, the time needed to pickup and dropoff coils from/to locations. In a dynamic environment, these properties can depend on a variety of factors, including the proficiency of the crane operator or the storage locations that are accessed. Therefore, an open-ended genetic algorithm is enhanced with integrated machine learning (IML) tasked with learning crane speeds and service times from historical data and adapting said properties in the underlying problem model in order to provide the optimizer with a more realistic view on the current world state. To understand the performance gain achieved by this enhancement, experimental setups with and without IML are evaluated. The results show that IML improves the optimizer’s performance, as the algorithm gains better understanding of the current world state and is therefore able to create more suitable schedules, considering the crane’s current performance.