Abstract
We describe a constraint programming approach to establish the coal carrying capacity of a large (2,670 km) rail network in north-eastern Australia. Computing the capacity of such a network is necessary to inform infrastructure planning and investment decisions but creating a useful model of rail operations is challenging. Analytic approaches exist but they are not very accurate. Simulation methods are common but also complex and brittle. We present an alternative where rail capacity is computed using a constraint-based optimisation model. Developed entirely in MiniZinc, our model not only captures all dynamics of interest but is also easily extended to explore a wide range of possible operational and infrastructural changes. We give results from a number of such case studies and compare against an industry-standard analytic approach.
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Notes
- 1.
In industry terminology, headway refers to the minimum temporal separation between two trains traveling in the same direction on the same rail line. Meanwhile, service time is the time necessary to fully load or unload a train, including shunting.
- 2.
With more data the model could be made more accurate in this regard.
- 3.
In industry terminology, below-rail refers to infrastructure controlled by the network owner, such as the physical track and signals. By comparison above-rail refers to infrastructure such as trains, wagons and other so-called rolling stock.
- 4.
In industry terminology, a spur is a short branch usually leading to a private siding.
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Acknowledgements
We thank Eric Nettleton for useful discussions during the development of this work. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.
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Harabor, D., Stuckey, P.J. (2016). Rail Capacity Modelling with Constraint Programming. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_13
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DOI: https://doi.org/10.1007/978-3-319-33954-2_13
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