| 2020 |
Kalinowski T, Matthews J, Waterer H, 'Scheduling of maintenance windows in a mining supply chain rail network', Computers and Operations Research, 115 (2020)
© 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 m... [more] © 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.
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| 2020 |
Gupte A, Kalinowski T, Rigterink F, Waterer H, 'Extended formulations for convex hulls of some bilinear functions', Discrete Optimization, 36 100569-100569 (2020)
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| 2018 |
Lidén T, Kalinowski T, Waterer H, 'Resource considerations for integrated planning of railway traffic and maintenance windows', Journal of Rail Transport Planning and Management, 8 1-15 (2018) [C1]
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| 2016 |
Boland N, Clement R, Waterer H, 'A bucket indexed formulation for nonpreemptive single machine scheduling problems', INFORMS Journal on Computing, 28 14-30 (2016) [C1]
© 2016 INFORMS. Anew mixed-integer linear programming (MILP) formulation for nonpreemptive single machine scheduling problems is presented. The model is a generalisation of the cl... [more] © 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.
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| 2015 |
Boland N, Savelsbergh M, Waterer H, 'A decision support tool for generating shipping data for the Hunter Valley coal chain', Computers and Operations Research, 53 54-67 (2015) [C1]
Strategic capacity planning is a core activity for the Hunter Valley Coal Chain Coordinator as demand for coal is expected to double in the next decade. Optimization and simulatio... [more] Strategic capacity planning is a core activity for the Hunter Valley Coal Chain Coordinator as demand for coal is expected to double in the next decade. Optimization and simulation models are used to suggest and evaluate infrastructure expansions and operating policy changes. These models require input data in the form of shipping stems, which are arrival streams of ships at the port, together with their cargo types and composition. Creating shipping stems that accurately represent future demand scenarios has been a time-consuming and daunting challenge. We describe an optimization-based decision support tool that facilitates and enhances this process, and which has become an integral part of the company's work flow. The tool embeds sampling to enable the generation of multiple shipping stems for a single demand scenario, employs targets, and desirable and permissable ranges to specify and control the characteristics of the shipping stems, and uses integer programming in a hierarchical fashion to generate shipping stems that best meet the set goals. © 2014 Elsevier Ltd.
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| 2015 |
Phillips AE, Waterer H, Ehrgott M, Ryan DM, 'Integer programming methods for large-scale practical classroom assignment problems', Computers and Operations Research, 53 42-53 (2015) [C1]
In this paper we present an integer programming method for solving the Classroom Assignment Problem in University Course Timetabling. We introduce a novel formulation of the probl... [more] In this paper we present an integer programming method for solving the Classroom Assignment Problem in University Course Timetabling. We introduce a novel formulation of the problem which generalises existing models and maintains tractability even for large instances. The model is validated through computational results based on our experiences at the University of Auckland, and on instances from the 2007 International Timetabling Competition. We also expand upon existing results into the computational difficulty of room assignment problems. © 2014 Elsevier Ltd.
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| 2015 |
Savelsbergh M, Waterer H, Dall M, Moffiet C, 'Possession assessment and capacity evaluation of the Central Queensland Coal Network', EURO Journal on Transportation and Logistics, 4 139-173 (2015) [C1]
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| 2014 |
Boland N, Kalinowski T, Waterer H, Zheng L, 'Scheduling arc maintenance jobs in a network to maximize total flow over time', DISCRETE APPLIED MATHEMATICS, 163 34-52 (2014) [C1]
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| 2014 |
Foster JD, Berry AM, Boland N, Waterer H, 'Comparison of mixed-integer programming and genetic algorithm methods for distributed generation planning', IEEE Transactions on Power Systems, 29 833-843 (2014) [C1]
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| 2013 |
Akartunali K, Boland N, Evans I, Wallace M, Waterer H, 'Airline planning benchmark problems-Part I: Characterising networks and demand using limited data', COMPUTERS & OPERATIONS RESEARCH, 40 775-792 (2013) [C1]
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| 2013 |
Akartunali K, Boland N, Evans I, Wallace M, Waterer H, 'Airline planning benchmark problems-Part II: Passenger groups, utility and demand allocation', COMPUTERS & OPERATIONS RESEARCH, 40 793-804 (2013) [C1]
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| 2013 |
Boland N, Kalinowski T, Waterer H, Zheng L, 'Mixed integer programming based maintenance scheduling for the Hunter Valley coal chain', JOURNAL OF SCHEDULING, 16 649-659 (2013) [C1]
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| 2012 |
Smith OJ, Boland NL, Waterer HA, 'Solving shortest path problems with a weight constraint and replenishment arcs', Computers & Operations Research, 39 964-984 (2012) [C1]
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| 2011 |
Archer R, Nates G, Donovan S, Waterer HA, 'Wind turbine interference in a wind farm layout optimization mixed integer linear programming model', Wind Engineering, 35 165-175 (2011) [C1]
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| 2009 |
Zink F, Waterer H, Archer R, Schaefer L, 'Geometric optimization of a thermoacoustic regenerator', INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 48 2309-2322 (2009) [C1]
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| 2007 |
Waterer H, 'Lot sizing with inventory gains', OPERATIONS RESEARCH LETTERS, 35 759-766 (2007) [C1]
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| 2002 |
Waterer H, Johnson EL, Nobili P, Savelsbergh MWP, 'The relation of time indexed formulations of single machine scheduling problems to the node packing problem', MATHEMATICAL PROGRAMMING, 93 477-494 (2002)
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| 2000 |
Philpott AB, Craddock M, Waterer H, 'Hydro-electric unit commitment subject to uncertain demand', EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 125 410-424 (2000)
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