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.

中文翻译：

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煤炭供应链的物流优化

澳大利亚新南威尔士州的猎人谷煤炭出口供应链对澳大利亚经济至关重要。但是，有效地管理其物流是一项挑战，因为它是一个复杂的系统，覆盖大范围的地理区域，包括铁路网络，三个煤炭码头和一个港口，并且拥有许多利益相关者，例如矿业公司，港口当局，煤炭终端运营商，铁路基础设施提供商以及以上的铁路运营商。我们开发了一个数学物流计划系统，该系统除其他方面外还集成了火车调度，库存管理和船舶调度。供应链的不同组件以不同的粒度级别建模。广泛的计算研究已对物流系统的瓶颈产生了见解，用于指导运营政策和未来投资的变化。计划系统使用独立于求解器的建模技术。由于自定义搜索启发式方法，这使我们可以在滚动水平方法的情况下观察约束编程和混合整数编程的性能差异。