Learning to Reduce State-Expanded Networks for Multi-activity Shift Scheduling

Till Porrmann, Michael Römer

Abstract: For personnel scheduling problems, mixed-integer linear programming formulations based on state-expanded networks in which nodes correspond to rule-related states often have very strong LP relaxations. A challenge of these formulations is that they typically give rise to large model instances. If one is willing to trade in optimality for computation time, a way to reduce the size of the model instances is to heuristically remove unpromising nodes and arcs from the state-expanded networks.