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In this paper, we propose a model to schedule the next 24 hours of operationsin a bulk cargo port to unload bulk cargo trains onto stockpiles. It is aproblem that includes multiple parts such as splitting long trains into shorterones and the routing of bulk material through a configurable network ofconveyors to the stockpiles. Managing such trains (up to three kilometers long)also requires specialized equipment. The real world nature of the problemspecification implies the necessity to manage heterogeneous data. Indeed, whennew 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 detailsneed to be accounted for. In fact, avoiding a full deadlock of the facilityafter a new but ineffective schedule is produced. In this paper, we provide adetailed presentation of this real world problem and its associated data. Thisallows us to propose an effective constraint programming model to solve thisproblem. We also discuss the model design and the different implementations ofthe propagators that we used in practice. Finally, we show how this model,coupled with a large neighborhood search, was able to find 24 hour schedulesefficiently.

A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended