Discovery Early Career Researcher Award - Grant ID: DE200101791
Funder
Australian Research Council
Funding Amount
$427,082.00
Summary
Mathematically optimal R&D for coral reef conservation. This project aims to develop mathematical methodologies for optimising Research & Development (R&D) of technologies that will secure complex and uncertain ecosystems into the future. Current conventional management approaches will not prevent the degradation of threatened ecosystems like the Great Barrier Reef, so new technologies are needed. The biggest challenge in choosing these technologies is the long delay between development and depl ....Mathematically optimal R&D for coral reef conservation. This project aims to develop mathematical methodologies for optimising Research & Development (R&D) of technologies that will secure complex and uncertain ecosystems into the future. Current conventional management approaches will not prevent the degradation of threatened ecosystems like the Great Barrier Reef, so new technologies are needed. The biggest challenge in choosing these technologies is the long delay between development and deployment, in which time ecosystem function may collapse and complex, dynamic ecological and social systems will change. The mathematical methods and theory developed will inform a Great Barrier Reef case study, and will be ready for rapid application to other ecosystems as the urgent need arises.Read moreRead less
Mathematical Decision Support to Optimise Hospital Capacity and Utilisation. Hospital planners and executives regularly contend with challenging capacity related decisions. Decisions relating to prioritisation, allocation and sharing of resources have a profound impact on productivity, efficiency and patient outcomes. There is a lack of data-driven or quantitative decision support to make well-informed capacity related decisions of a strategic or tactical nature in a single hospital, or across a ....Mathematical Decision Support to Optimise Hospital Capacity and Utilisation. Hospital planners and executives regularly contend with challenging capacity related decisions. Decisions relating to prioritisation, allocation and sharing of resources have a profound impact on productivity, efficiency and patient outcomes. There is a lack of data-driven or quantitative decision support to make well-informed capacity related decisions of a strategic or tactical nature in a single hospital, or across a regional healthcare system. This project aims to deliver decision support for holistic hospital capacity assessment and planning optimisation. This will yield significant benefits for the health sector, providing a tool to optimise the allocation of resources and provision of infrastructure for regional hospital services.Read moreRead less
An integrated mathematical approach to synchronise and optimise hospital operations. This project aims to develop an integrated mathematical approach to synchronise and optimise patient scheduling systems of different departments to ensure that the hospital’s assets and related resources are used efficiently. The project’s aim is to investigate patient flow, process delay, and the interaction and inter-dependence of departments within the hospital to reduce access block (bottleneck) and subseque ....An integrated mathematical approach to synchronise and optimise hospital operations. This project aims to develop an integrated mathematical approach to synchronise and optimise patient scheduling systems of different departments to ensure that the hospital’s assets and related resources are used efficiently. The project’s aim is to investigate patient flow, process delay, and the interaction and inter-dependence of departments within the hospital to reduce access block (bottleneck) and subsequent overcrowding. This project aims to smooth the running of the hospital, improve the efficiency of patient throughput, reduce waiting times, and revolutionise hospital planning and scheduling.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100063
Funder
Australian Research Council
Funding Amount
$394,398.00
Summary
Nonmonotone Algorithms in Operator Splitting, Optimisation and Data Science. This project aims to develop the mathematical foundations for the analysis and development of optimisation algorithms used in data science. Despite their now ubiquitous use, machine learning software packages routinely rely on a number of algorithms from mathematical optimisation which are not properly understood. By moving beyond the traditional realms of Fejér monotone algorithms, this project expects to develop the m ....Nonmonotone Algorithms in Operator Splitting, Optimisation and Data Science. This project aims to develop the mathematical foundations for the analysis and development of optimisation algorithms used in data science. Despite their now ubiquitous use, machine learning software packages routinely rely on a number of algorithms from mathematical optimisation which are not properly understood. By moving beyond the traditional realms of Fejér monotone algorithms, this project expects to develop the mathematical theory required to rigorously justify the use of such algorithms and thereby ensure the integrity of the decision tools they produce. This mathematical framework is also expected to produce new algorithms for optimisation which benefit consumers of data science such as the health-care and cybersecurity sectors.Read moreRead less
Synchromodal container logistics for Australia. Synchromodal container logistics for Australia. This project aims to develop advanced mathematical optimization models and algorithms to create multi-modal logistics approaches for container movements in and out of Australia’s busy ports. The increasingly congested capital cities of Sydney, Brisbane and Melbourne need to find new ways of moving an increasing volume of containerized freight. Moving from trucks to rail is expected to reduce pollution ....Synchromodal container logistics for Australia. Synchromodal container logistics for Australia. This project aims to develop advanced mathematical optimization models and algorithms to create multi-modal logistics approaches for container movements in and out of Australia’s busy ports. The increasingly congested capital cities of Sydney, Brisbane and Melbourne need to find new ways of moving an increasing volume of containerized freight. Moving from trucks to rail is expected to reduce pollution and road congestion, but is only possible if highly efficient modes of operation can be developed. Research into system design and operational scheduling is expected to achieve the required efficiency for multi-modal logistics that will reduce air pollution and road congestion.Read moreRead less
Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transm ....Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transmission and designing intervention strategies for maximum effect on disease transmission. The innovative combination of modelling, inference and optimisation ensures that the mathematical methods developed will be broadly applicable to modelling elimination strategies for other infectious diseases.
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Risk and Reliability in Stochastic Optimisation and Equilibrium. This project seeks to develop theory and methodology in optimisation which take advantage of recent progress in understanding and treating risk in decision making. Problems of optimisation in the face of uncertainty must confront the risk inherent in having to make reliable decisions before knowing the outcomes of crucial random variables on which costs and constraints may depend. Recent theoretical developments, featuring ‘measure ....Risk and Reliability in Stochastic Optimisation and Equilibrium. This project seeks to develop theory and methodology in optimisation which take advantage of recent progress in understanding and treating risk in decision making. Problems of optimisation in the face of uncertainty must confront the risk inherent in having to make reliable decisions before knowing the outcomes of crucial random variables on which costs and constraints may depend. Recent theoretical developments, featuring ‘measures of risk’ beyond just-expected values and quantiles offer hope of major new advances. This project aims to achieve such advances not only in optimisation but also in models of equilibrium that likewise have to deal with uncertainty. Extending current theory and methodology to such multi-stage stochastic models is a challenge. Besides taking up this challenge for its own sake, a major goal of this research will be to use the results in solution algorithms.Read moreRead less
Statistical and mathematical modelling to improve health care outcomes in hospitals. The aim of this project is to develop new quantitative techniques based on mathematical and statistical modelling that improve the outcomes of health care in hospitals. Hospital outcomes for patients are sub-optimal due to adverse events such as hospital acquired infections and fully stretched facilities. Research from this project will lead to resource usage being optimised using operations research; the tra ....Statistical and mathematical modelling to improve health care outcomes in hospitals. The aim of this project is to develop new quantitative techniques based on mathematical and statistical modelling that improve the outcomes of health care in hospitals. Hospital outcomes for patients are sub-optimal due to adverse events such as hospital acquired infections and fully stretched facilities. Research from this project will lead to resource usage being optimised using operations research; the transmission of hospital acquired infections being better understood using mathematical models; and better monitoring of adverse events and analyses of studies using statistical tools. Opportunities will be provided for hospital staff to acquire knowledge of the significance of these outcomes .Read moreRead less
Designing Robust Reactive Scheduling System for Emergency Medical Services. The job shop approach to reactive scheduling system for Emergency Departments promises considerable benefits over existing approaches, and allows problems of large size and complexity to be solved with great accuracy. The application of research results in Australia has the potential to:
- provide benchmarks in ED scheduling;
- reduce patients waiting times and rejections by better planning of patient schedules of em ....Designing Robust Reactive Scheduling System for Emergency Medical Services. The job shop approach to reactive scheduling system for Emergency Departments promises considerable benefits over existing approaches, and allows problems of large size and complexity to be solved with great accuracy. The application of research results in Australia has the potential to:
- provide benchmarks in ED scheduling;
- reduce patients waiting times and rejections by better planning of patient schedules of emergency departments;
- leads to less costs for staff and resources;
- enhance the ability of managers to control operations in an efficient manner and minimise conflicts; and
- to increase efficiency of emergency departments, leading to lower operating and capital cost.
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Hybrid Job Shop Techniques for Dynamic Train Scheduling. The job shop approach to dynamic train scheduling promises considerable benefits over existing approaches, and allows problems of large size and complexity to be solved with great accuracy. The application of research results in Australia has the potential to:
- reduce train delays and costs by better planning of train schedules of railway systems;
- reduce transport costs to rail customers and increase rail's market share of freight ....Hybrid Job Shop Techniques for Dynamic Train Scheduling. The job shop approach to dynamic train scheduling promises considerable benefits over existing approaches, and allows problems of large size and complexity to be solved with great accuracy. The application of research results in Australia has the potential to:
- reduce train delays and costs by better planning of train schedules of railway systems;
- reduce transport costs to rail customers and increase rail's market share of freight movements;
- enhance the ability of operators to control operations in an efficient manner and minimise conflicts;
- to increase efficiency of Australian Railway System, leading to lower operating and capital costs;
- optimise the energy consumed.
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