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Logistics optimization for a coal supply chain

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Abstract

The Hunter Valley coal export supply chain in New South Wales, Australia, is of great importance to the Australian economy. Effectively managing its logistics, however, is challenging, because it is a complex system, covering a large geographic area and comprising a rail network, three coal terminals, and a port, and has many stakeholders, e.g., mining companies, port authorities, coal terminal operators, rail infrastructure providers, and above rail operators. We develop a matheuristic logistics planning system which integrates, amongst other concerns, train scheduling, stockpile management, and vessel scheduling. Different components of the supply chain are modeled at different levels of granularity. An extensive computational study has generated insights into the bottlenecks in the logistics system, which are used to guide changes in operating policies and future investments. The planning system uses a solver-independent modeling technology. This allowed us to observe differences between the performance of constraint programming and mixed-integer programming in the context of a rolling-horizon approach, due to custom search heuristics.

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Notes

  1. We adjusted the parameters of our model accordingly to compute the results published in (Rocha de Paula et al. 2019). LNS was switched on.

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Acknowledgements

We would like to thank the strategic planning team at the HVCCC for many insightful and helpful suggestions, as well as to Opturion Ltd for providing their version of the CPX solver under an academic license.

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Correspondence to Gleb Belov.

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The research presented here was supported by ARC linkage Grant LP110200524.

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Belov, G., Boland, N.L., Savelsbergh, M.W.P. et al. Logistics optimization for a coal supply chain. J Heuristics 26, 269–300 (2020). https://doi.org/10.1007/s10732-019-09435-8

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