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Network Optimisation for Underground Mine Planning
Speakers:
Professor Emeritus Doreen Thomas (AM) (The University of Melbourne)
Associate Professor Marcus Brazil (The University of Melbourne)
Summary:
Efficient methods to model and optimise the design of open-cut mines have been developed and used since the 1970s. Designing the infrastructure of an underground mine is a much more difficult problem, but one that has a similar potential for optimisation and strategic planning. In this seminar we give an overview of the work of our team at the University of Melbourne in addressing this problem over a period of more than twenty years. We trace the progression of this work from mathematical foundations based on the properties of Steiner trees, through to the design and implementation of optimisation algorithms for industry supported by consultancies and Linkage grants, to the establishment of a spin-off company MineOptima through which we developed commercial software products which continue to be used in the mining industry to this day.
The aim of this seminar is to give some insight into the process of moving from basic research to the establishment of software tools that meet the needs of industry.
Biographies:
Professor Emeritus Doreen Thomas (AM) was Head of the School of Electrical, Mechanical and Infrastructure Engineering at the University of Melbourne. She holds a DPhil (Mathematics), University of Oxford. She was a founding director of a spin-off company MineOptima, through which her mining software has been commercialised. She has been recognised with a national teaching award for her contribution to engineering education and mentorship. She is a Fellow of the Australian Academy of Technology and Engineering and an Honorary Fellow of Engineers Australia.
Marcus Brazil is an Associate Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne. He was an undergraduate at The University of Melbourne, and received a Ph.D. in Mathematics (in the field of Computational and Geometric Group Theory) from La Trobe University in 1995. Currently, his main research interest is in Optimal Network Design with applications to Telecommunications, Wireless Sensor Networks, VLSI Physical Design, Underground Mining, and Infrastructure for Electric Vehicles. He also combines this with more theoretical work, particularly in the area of Steiner Trees. His research and consultancy work in optimisation for underground mine design has been particularly successful, having received strong support from a number of industry bodies such as BHP Billiton, Newmont Australia and Rand Mining. He was also a founder and director of the company MineOptima.
Переглядів: 34

Відео

Exact and Heuristic Solution Methods to Optimize Maintenance & Flight Schedules of Military Aircraft
Переглядів 364 години тому
Title: Exact and Heuristic Solution Methods to Optimize Maintenances and Flight Schedules of Military Aircraft Speaker: Prof. Olga Battaïa (KEDGE Business School Campus Bordeaux, France) Summary: In this work, we address the long-term Military Flight and Maintenance Planning (MFMP) problem, developed in collaboration with the French Air Force. We present a mathematical model to optimize this co...
Supervised dimensionality reduction for instance space analysis
Переглядів 114 години тому
Speaker: Danielle Notice Lancaster University Instance space analysis extends the algorithm selection framework by enabling the visualisation of problem instances via dimensionality reduction (DR). The lower dimensional projection can also be used as input to predict algorithm performance, or to perform algorithm selection. In this presentation we consider two supervised DR methods - partial le...
Surrogate model-based algorithms for expensive black-box optimization
Переглядів 877 годин тому
Speaker: Juli Mueller U.S. National Renewable Energy Laboratory Summary: Computationally expensive black-box optimization tasks arise in a wide variety of applications, including the calibration of simulation models against observation data in climate, combustion, and cosmology, as well as in various design and scheduling tasks to name a few. These problems are characterised by the fact that an...
Dimensionality reduction techniques for optimization problems
Переглядів 20128 днів тому
Speaker: Professor Coralia Cartis (University of Oxford) Summary: Modern applications such as machine learning involve the solution of huge scale nonconvex optimization problems, sometimes with special structure. Motivated by these challenges, we investigate more generally, dimensionality reduction techniques in the variable/parameter domain for local and global optimization that rely crucially...
Solving nonconvex discrete diversity location problems with cutting planes
Переглядів 52Місяць тому
Speaker: Dr Hoa Bui (ARC Training Centre for Transforming Maintenance through Data Science; Curtin Centre for Optimisation and Decision Science) Summary: The problem of diversity location problems involves choosing a subset of locations from a broader set to optimise the sum of distances. While significant progress has been made in exact methods for related variants, such as max-min and max-mea...
Forecasting the future and the future of forecasting
Переглядів 115Місяць тому
Speaker: Professor Rob J Hyndman (Monash University) Summary: People have been forecasting for thousands of years. They forecast whether it will rain tomorrow, how much wheat will be harvested, how long it will take for dinner to cook, how many widgets their company will sell next month, what the unemployment figure will be in a year’s time, or how much superannuation they will have when they r...
SAT-DreamOpt - Dynamic resource management for GEO high-throughput satellites
Переглядів 40Місяць тому
Speaker: Professor Vicky Mak-Hau Summary: Modern satellite communication systems are designed to serve dispersed users with changing operational requirements. GEO satellites provides reliable, beyond line of sight communications for a variety of applications including television broadcast, remote area connectivity and maritime and aeronautical broadband services. To provide these services, the ...
Optimization in Combinatorial and Non-Convex ML: Positive and Negative Results
Переглядів 41Місяць тому
Speaker: Dr Jean Honorio Summary: Several modern machine learning (ML) problems are combinatorial and non-convex, for which theoretical guarantees are quite limited. My long-term research goal is to uncover the general foundations of ML and optimization that drives empirical success. I aim to develop a set of optimization-theoretic frameworks and tools to bridge the aforementioned gaps, to furt...
Optimization with Superquantile Constraints - A Fast Computational Approach
Переглядів 842 місяці тому
Speaker: Prof. Ying Cui Summary: We present an efficient and scalable second-order computational framework for solving large-scale optimization problems with superquantile constraints. Unlike empirical risk models, superquantile models have non-separable constraints that make typical first-order algorithms difficult to scale. We address the challenge by adopting a hybrid of the second-order sem...
Frank Neumann
Переглядів 2282 місяці тому
Speaker: Prof. Frank Neumann Summary: In the classical setting evolutionary algorithms (EAs) are used to compute a single solution of high quality with respect to the objective function or a set of trade-off solutions in the field multi-objective optimization where one deals with multiple, usually conflicting objectives. Traditionally, diversity preservation is introduced to prevent premature c...
Learning to Optimise: A Perspective from Darwinian Evolution
Переглядів 2602 місяці тому
Speaker: A/Prof. Yi Mei Summary: Solving complex optimisation problems is hard, especially when facing the real-world challenges such as large problem size, dynamic/uncertain environment, and multiple conflicting objectives. In recent years, “learn to optimise” becomes a trendy research topic, aiming to employ machine learning techniques to automatically design optimisation algorithms. This tal...
Optimisation is Fun!
Переглядів 872 місяці тому
Optimisation is Fun!
Inventory Management: Zahra Namazian
Переглядів 1092 місяці тому
Inventory Management: Zahra Namazian
OPTIMA PhDs in Industry
Переглядів 593 місяці тому
OPTIMA students work with industry on their complex business challenges. This video highlights those projects.
How to make optimal decisions (that are unfair, biased and non-objective)
Переглядів 2245 місяців тому
How to make optimal decisions (that are unfair, biased and non-objective)
Efficient optimisation algorithms for Model Predictive Control with limited online resources
Переглядів 1067 місяців тому
Efficient optimisation algorithms for Model Predictive Control with limited online resources
Optimal forecast reconciliation with time series selection
Переглядів 2967 місяців тому
Optimal forecast reconciliation with time series selection
Synthetic data for a better understanding of Machine Learning models and algorithms
Переглядів 15210 місяців тому
Synthetic data for a better understanding of Machine Learning models and algorithms
Predict-then-Optimise Strategies for Water Flow Control
Переглядів 6711 місяців тому
Predict-then-Optimise Strategies for Water Flow Control
The tractability of hard scheduling problems: a parametrized complexity approach
Переглядів 7511 місяців тому
The tractability of hard scheduling problems: a parametrized complexity approach
Combating Misinformation: Enhancing Large Language Models with Psychologically Informed ML
Переглядів 7311 місяців тому
Combating Misinformation: Enhancing Large Language Models with Psychologically Informed ML
Using optimisation for testing autonomous vehicles
Переглядів 69Рік тому
Using optimisation for testing autonomous vehicles
Beyond optimal solutions for real-world problems
Переглядів 171Рік тому
Beyond optimal solutions for real-world problems
OPTIMA Debate - Can machine learning models solve all optimisation problems?
Переглядів 212Рік тому
OPTIMA Debate - Can machine learning models solve all optimisation problems?
Competitive Algorithms for Online Joint Replenishment and Friends
Переглядів 131Рік тому
Competitive Algorithms for Online Joint Replenishment and Friends
Hypervolume-based Representation and Scalarization: Results and Challenges
Переглядів 194Рік тому
Hypervolume-based Representation and Scalarization: Results and Challenges
Knapsack problems as nondeterministic sequential decision processes and its solution method
Переглядів 137Рік тому
Knapsack problems as nondeterministic sequential decision processes and its solution method
Addressing Challenges of Cutting Natural Stone Panels
Переглядів 41Рік тому
Addressing Challenges of Cutting Natural Stone Panels
Creating a smart water grid - bringing the environment, people and money together
Переглядів 102Рік тому
Creating a smart water grid - bringing the environment, people and money together

КОМЕНТАРІ

  • @soen2368
    @soen2368 5 місяців тому

    thank you in general 3 lens system oversimplified task. today this is even for middle age student not hard task. PC too powerful for it.

  • @allenwang3331
    @allenwang3331 5 місяців тому

    Good to see my proof discussed in 31:00 :) It was my master's project and the proof took a whole year to get through.

  • @optimaarc
    @optimaarc 2 роки тому

    optima.org.au/partner-with-us/

  • @optimaarc
    @optimaarc 2 роки тому

    Please share.