Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/26696
Title: | Scheduling of maintenance windows in a mining supply chain rail network | Contributor(s): | Matthews, Jason (author); Waterer, Hamish (author); Kalinowski, Thomas (author) | Publication Date: | 2020-03 | Early Online Version: | 2019-03-30 | DOI: | 10.1016/j.cor.2019.03.016 | Handle Link: | https://hdl.handle.net/1959.11/26696 | Abstract: | 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. | Publication Type: | Journal Article | Grant Details: | ARC/LP140101000 | Source of Publication: | Computers & Operations Research, v.115, p. 1-15 | Publisher: | Pergamon Press | Place of Publication: | United Kingdom | ISSN: | 1873-765X 0305-0548 |
Fields of Research (FoR) 2008: | 010303 Optimisation 010206 Operations Research |
Fields of Research (FoR) 2020: | 490304 Optimisation 490108 Operations research |
Socio-Economic Objective (SEO) 2008: | 970101 Expanding Knowledge in the Mathematical Sciences | Socio-Economic Objective (SEO) 2020: | 280118 Expanding knowledge in the mathematical sciences | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
---|---|
Appears in Collections: | Journal Article School of Science and Technology |
Files in This Item:
File | Description | Size | Format |
---|
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.