A neural network approach to the vessel dispatching problem

The paper discusses the process of loading, transport and unloading of gravel by inland water transportation. At the loading port, the problem that needs to be solved is the assignment of load barges to pusher tugs for the planned period of one day. However, disturbances of planned schedules are very common. Whenever a disturbance in a daily schedule appears, the dispatcher urgently attempts to mitigate negative effects resulting from the disturbance. Real-time operations limit the amount of time that dispatchers in charge of traffic control have to make decisions and increase the level of stress associated with quick and adequate response. This paper aims to demonstrate the feasibility of a dispatch decision support system that could decrease the work load for the dispatcher and improve the quality of decisions. The proposed neural network with the ability to adapt or learn from examples of decisions can simulate the dispatcher's decision process.

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