Stacking problems are central to multiple billion-dollar industries. The container shipping industry needs to stack millions of containers every year. In the steel industry the stacking of steel slabs, blooms, and coils needs to be carried out efficiently, affecting the quality of the final product. The immediate availability of data - thanks to the continuing digitalization of industrial production processes - makes the optimization of stacking problems in highly dynamic environments feasible.

For the first time, we will be hosting all three tracks:

Hotstorage (HS): In the first track a dynamic environment is provided that represents a simplified stacking scenario. Blocks arrive continuously at a fixed arrival location from which they have to be removed swiftly. If the arrival location is full, the arrival of additional blocks is not possible. To avoid such a state, there is a range of buffer stacks that may be used to store blocks. Each block has a due date before which it should be delivered to the customer. However, blocks may leave the system only when they become ready, i.e., some time after their arrival. To deliver a block it must be put on the handover stack - which must contain only a single block at any given time. There is a single crane that may move blocks from arrival to buffer, between buffers, and from buffer to handover. The optimization must control this crane in that it reacts to changes with a sequence of moves that are to be carried out. The control does not have all information about the world. A range of performance indicators will be used to determine the winner.

Rolling Mill (RM): In this second scenario, blocks also arrive at fixed arrival locations. Each block must eventually be delivered to a predefined handover location, after which it is further processed by a respective rolling mill. The handover location is defined by the rolling mill program, of which only a few next entries (i.e. block to be delivered) are known. Blocks placed on a handover location other than then one specified in the rolling mill program count as rolling mill mess-ups. Two crane must be tasked to move blocks from arrival to handover locations, and can utilize buffer stacks for temporary storage. Both share a single crane lane, i.e. they cannot overtake each other. Furthermore, location access of cranes is limited, as one crane can only access all arrival and buffer locations and the other crane can only access all buffer and handover locations. Finally, the cranes have a carrying capacity greater than one, i.e. multiple blocks can be moved at a time, which represents an additional challenge for the solver. Again, a range of performance indicators will be used to determine the winner.

Crane Scheduling (CS): The third track features another simplified scenario which focuses mainly on the scheduling aspect of real-world crane operations. It features various arrival and handover locations and much more buffer locations to choose from. Blocks enter and leave the warehouse at arrival and handover locations, respectively, and two cranes can be manipulated to move blocks around. Both cranes share a single lane but this time can access every location. The scenario creates move requests which determine blocks that have to be picked up at arrival or dropped off at handover locations, and also creates predefined crane moves to fulfill these move requests. An external optimizer must create crane schedules to fulfill these requests. It can do so by scheduling the predefined moves (which use naïve stacking rules and thus are not optimal), but it can also create optimized crane moves and schedule those. Therefore, the second scenario integrates three optimization problems: stacking (if custom moves are created), assignment and scheduling.

The dynamic environments are implemented in form of a realtime simulation which provides the necessary change events. The simulation runs in a separate process and publishes its world state and change events via message queuing (ZeroMQ), and also listens for crane orders. Thus, control algorithms may be implemented as standalone applications using a wide range of programming languages. Exchanged messages are encoded using protocol buffers - again libraries are available for a large range of programming languages. As in the 2024 competition a website will be used that participants can use to create experiment and test their solvers. In addition, the simulation models are available at GitHub for offline testing and development at https://github.com/dynstack/dynstack . We gladly accept pull requests for new starter kits, existing algorithms and approaches, as well as additions to the bibliography on works that have used the competition for scientific research.