Dr Hamish Waterer

© 2019 Rail infrastructure forms a critical part of the mining supply chain in Australia due to the high weight to volume ratio of the product and the long distances between the mines and the ports. Across Australia, rail infrastructure has been steadily expanding to account for the growth in export volumes and the movement of mining operations further inland, and so the efficient and effective management of this critical infrastructure is vitally important. Maintenance plays a crucial role in this management as it ensures that the infrastructure assets are in a condition that allows safe, reliable, and efficient transport. In this paper we consider the annual planning of maintenance for Australia's largest coal rail network, the Central Queensland Coal Network (CQCN), that is owned, operated, and managed, by Aurizon Holdings Pty Ltd. The current planning approach at Aurizon uses the concept of a maintenance access window (MAW) which provides a train-free time window across geographically contiguous track locations that define a maintenance zone. These train-free time windows facilitate the scheduling of specific maintenance tasks at specific track locations within zones closer to day of operation and forms the basis for a planning framework. A MIP model is introduced which facilitates the planning of different maintenance resources across this network to schedule MAWs. The model takes into account maintenance requirement forecasts as well as the availability of resources. Candidate solutions are compared using a proxy for network throughput capacity. Due to the long computation times required to solve the MIP model at the annual planning horizon a matheuristic is developed and two variants are tested. On average 80% less computational time is required to find a good solution (average gap of 5%) using the matheuristic compared to solving the MIP model directly (average gap of 1.5%). The MIP model and associated matheuristic provides a suitable framework for semi-automated maintenance planning and is being integrated into the current suite of decision support tools used by Aurizon.

© 2016 INFORMS. Anew mixed-integer linear programming (MILP) formulation for nonpreemptive single machine scheduling problems is presented. The model is a generalisation of the classical time indexed (TI) model to one in which at most two jobs can be processing in each time period. Like the TI model, the new model, called the bucket indexed (BI) model, partitions the planning horizon into periods of equal length, or buckets. Unlike the TI model, the length of a period is a parameter of the BI model and can be chosen to be as long as the processing time of the shortest job. The two models are equivalent if a period is of unit length, but when longer periods are used in the BI model, it can have significantly fewer variables and nonzeros than the corresponding TI model. A computational study using weighted tardiness instances, and weighted completion time instances with release dates, reveals that the BI model significantly outperforms the TI model on instances where the minimum processing time of the jobs is large. Furthermore, the performance of the BI model is less vulnerable to increases in average processing time when the ratio of the largest processing time to the smallest is held constant.