Optimal crane scheduling for tankhouse copper processing

With the development of industry and automation, optimizing crane operations becomes increasingly important for improving productivity and reducing costs in manufacturing systems. In this paper, we address a crane scheduling problem in tankhouse copper processing systems, where operations are tightly integrated with the production process and the resource sharing is frequent. We propose discrete-time mixed-integer linear programming (MILP), continuous-time MILP, and constraint programming (CP) models to tackle this problem. It is found that the CP model has the best performance considering both single- and multi- crane conditions. To efficiently solve real-world problems, we propose a machine-based decomposition strategy with the CP framework, which solves 128 tasks in 34 seconds over 24 iterations. The results demonstrate the effectiveness of the proposed approach in handling large-scale, complex scheduling problems with high computational efficiency.