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Large Neighborhood Search for Energy-Efficient Train Timetabling


Diarmuid Grimes, Barry Hurley, Deepak Mehta, Barry O'Sullivan

Publication Type: 
Refereed Conference Meeting Proceeding
The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach’s ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.
Conference Name: 
International Conference on Tools with Artificial Intelligence Proceedings: IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
Digital Object Identifer (DOI):
Publication Date: 
Conference Location: 
National University of Ireland, Cork (UCC)
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