A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended
This work addresses a real-world logistics problem for port operators, but it is incremental as it applies existing constraint programming techniques to a specific domain.
The paper tackles the problem of scheduling bulk cargo train unloading operations in ports over a 24-hour period, accounting for complex constraints like train splitting and conveyor routing, and proposes a constraint programming model with large neighborhood search that efficiently finds feasible schedules.
In this paper, we propose a model to schedule the next 24 hours of operations in a bulk cargo port to unload bulk cargo trains onto stockpiles. It is a problem that includes multiple parts such as splitting long trains into shorter ones and the routing of bulk material through a configurable network of conveyors to the stockpiles. Managing such trains (up to three kilometers long) also requires specialized equipment. The real world nature of the problem specification implies the necessity to manage heterogeneous data. Indeed, when new equipment is added (e.g. dumpers) or a new type of wagon comes in use, older or different equipment will still be in use as well. All these details need to be accounted for. In fact, avoiding a full deadlock of the facility after a new but ineffective schedule is produced. In this paper, we provide a detailed presentation of this real world problem and its associated data. This allows us to propose an effective constraint programming model to solve this problem. We also discuss the model design and the different implementations of the propagators that we used in practice. Finally, we show how this model, coupled with a large neighborhood search, was able to find 24 hour schedules efficiently.