Discovery Early Career Researcher Award - Grant ID: DE170100234
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Exact and hybrid algorithms for the Aircraft Landing Problem. This project aims to develop algorithms with superior guaranteed performance. Aircraft Landing Problems (ALP) are an important class of decision problems. Optimal solution of an ALP is applicable in transportation and health care delivery, benefitting systems experiencing long delays. This project aims to address several of the Australian Government's Science and Research Priorities, focusing on food supply chains, effective operation ....Exact and hybrid algorithms for the Aircraft Landing Problem. This project aims to develop algorithms with superior guaranteed performance. Aircraft Landing Problems (ALP) are an important class of decision problems. Optimal solution of an ALP is applicable in transportation and health care delivery, benefitting systems experiencing long delays. This project aims to address several of the Australian Government's Science and Research Priorities, focusing on food supply chains, effective operation and resource allocation in transport, and better models of health care delivery and services.Read moreRead less
Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, i ....Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, integer models usually assume the data is known with certainty, which is often not the case in the real world. This project will develop new theory and algorithms to enhance the analysis of integer models, including those that incorporating uncertainty, while also enabling the use of parallel computing paradigms. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120100049
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
New integer programming based theory, formulations and decomposition techniques with applications to integrated problems. Optimisation problems permeate science and industry. By developing new techniques to solve larger and harder problems than is currently possible, more complex questions can be answered, and more accurate solutions obtained. Industries can use such tools to make better financial, resource management, operational, and/or strategic planning decisions.
Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and be ....Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and better use of renewable energy sources, with significant cost savings for railways and the wider community.Read moreRead less
Using Mathematics to Maximize the Efficiency of Shared Infrastructure in Australia's Coal Export Supply Chain. Port Waratah Coal Services operates the world's largest coal export terminal, servicing about 14 coal mining companies in the Hunter Valley, NSW. It is responsible for around $15 billion in annual export income for Australia. The coal supply chain is a complex operation, hampered by bottlenecks in critical shared infrastructure. Such limitations are estimated to cost Australia about $2 ....Using Mathematics to Maximize the Efficiency of Shared Infrastructure in Australia's Coal Export Supply Chain. Port Waratah Coal Services operates the world's largest coal export terminal, servicing about 14 coal mining companies in the Hunter Valley, NSW. It is responsible for around $15 billion in annual export income for Australia. The coal supply chain is a complex operation, hampered by bottlenecks in critical shared infrastructure. Such limitations are estimated to cost Australia about $2 billion pa in lost sales. This project will support the design of new infrastructure and processes to ensure an efficient supply chain. The new science resulting will benefit other coal operations in Australia, and potentially other bulk goods supply chains.Read moreRead less
Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project ....Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project aims to provide theoretically sound frameworks for solving large Markov decision processes, and exploit them to solve important combinatorial optimisation problems. This timely project can promote Australia's position in the development of such novel frameworks for many scientific and industrial applications.Read moreRead less
Determining features that separate groups of protein sequences. This project aims to develop mathematical approaches for determining features that distinguish one group of proteins from another, based on their amino acid sequences. The groups of sequences will reflect different outcomes, so that identifying the fundamental features can result in targeted interventions against the poorer outcome. A simple comparison at each position or of known features can fail to determine robust differentiator ....Determining features that separate groups of protein sequences. This project aims to develop mathematical approaches for determining features that distinguish one group of proteins from another, based on their amino acid sequences. The groups of sequences will reflect different outcomes, so that identifying the fundamental features can result in targeted interventions against the poorer outcome. A simple comparison at each position or of known features can fail to determine robust differentiators and so more complex methods are required. The project will, for example, help identify HIV vaccine targets by comparing early HIV transmission sequences from those in chronic infection. The methods will be applicable to viral proteins where high mutation rates make this task even more complex.Read moreRead less
Competitive supplier bidding in supply chains. This project will use mathematical modelling to contribute to better management practice in dealing with procurement. With the increasing use of auctions and sophisticated bidding procedures it is essential to improve our understanding of this important area.
Evaluating the long-term costs and benefits of community-based initiatives. The ultimate benefit from the research is a more efficient allocation of public funds to provide public services, i.e. an increase in the gain derived from the government budget. The relative advantages of alternative methods of delivering government services are subject to significant uncertainty, which means that policy decisions are often poorly informed. Improvements in the accuracy of predicting the costs and benefi ....Evaluating the long-term costs and benefits of community-based initiatives. The ultimate benefit from the research is a more efficient allocation of public funds to provide public services, i.e. an increase in the gain derived from the government budget. The relative advantages of alternative methods of delivering government services are subject to significant uncertainty, which means that policy decisions are often poorly informed. Improvements in the accuracy of predicting the costs and benefits of complex community-based initiatives will help policymakers identify the set of initiatives that provide the best outcomes for the community they serve, as well as informing the optimal specification of the individual initiatives.Read moreRead less
An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.Read moreRead less