With Gurobi’s decision intelligence technology, you can make optimal business decisions in seconds. From workforce scheduling, portfolio management, and marketing optimization, to supply chain design, and everything in between, Gurobi identifies your optimal solution, out of trillions of possibilities. Gurobi has operations across the Americas, Europe, and Asia. Over 2,500 customers across 40+ industries run on Gurobi, including SAP, Air France, and the NFL, as well as half of the Fortune 10. For more information, visit https://www.gurobi.com/ or email sales-apac@gurobi.com.

The Melbourne Centre for Data Science is building a vibrant, research-intensive, and a collaborative interdisciplinary environment to lead advancements in data science for the benefit of society.

The Optimization Firm develops software tools for optimization (BARON) and machine learning (ALAMO) for more than 1,000 customers, including world-leading universities, research institutions, and companies spanning the energy, finance, healthcare, materials, and technology sectors.

The Australian Mathematical Sciences Institute (AMSI) is a not-for-profit peak body for the mathematical sciences with a breadth of members including Australian universities, professional societies, government agencies and industry. We champion mathematics, statistics, and data science for Australia’s advancement through advocacy, facilitation, and collaboration.

The common purpose we share with our members and partners is the radical improvement of mathematical capacity and facility, both in Australia and Asia-Pacific. AMSI’s impact is underpinned by our convening power across the mathematical sciences ecosystem.

AMSI’s activities fall largely into three strategic areas – school education, research and higher education, and industry engagement. In school education, we focus on teacher professional development to foster teaching excellence and inspire students to study mathematics to year 12. Research and higher education delivers advanced research training and research workshops to continue the development of undergraduate and postgraduate maths students, in preparation for careers in academia and industry. Our industry division is home to the national PhD internship program, APR.Intern, which has successfully grown to an all-discipline all-sector program working with Australian universities and industry.

OPTIMA-CON 24 has received a generous Flagship Activity Grant from the Theo Murphy Initiative (TMI) Australia.

TMI was established by the Royal Society to further scientific discovery. The fund is administered by the Australian Academy of Science and provides grant funding to support career development opportunities for Australia’s early- and mid-career researchers (EMCRs) in science, technology, engineering, and mathematics (STEM). Applicants will need to demonstrate how the proposed activity will support EMCRs knowledge development in the relevant STEM related field(s) of research.

OPTIMA-CON 24 has received grant funding from The Australian Mathematical Society which is the national society of the mathematics profession in Australia.

OPTIMA-CON 24 has received grant funding from our conference host the Melbourne Convention and Exhibition Centre.

MCEC offers flexible spaces, innovative menus, cutting-edge tech, and a dedicated team to help you deliver an unforgettable experience.

Alysson is an expert in mathematical programming with a focus on mixed-integer programming modelling and decomposition solution algorithms. He has held a Senior Lectureship position since 2017. He obtained PhD in Business Administration (Quantitative Methods) from HEC – University of Montreal. In 2008 he commenced at the Applied Mathematics Department of the University of São Paulo. He has been a chief investigator in several projects funded by CAPES, CNPq and FAPESP, the Spanish Ministry of Education, the European Commission (Marie-Curie Actions) and the ARC. He has 40 journal publications on theory and multidisciplinary applications of optimisation in several applied engineering areas. He has supervised nine PhD students and one post-doc and is the current primary supervisor of three PhD students. Alysson has provided mathematical modelling and algorithmic development input to research on a wide range of application areas, resulting in published outputs with more than 50 co-authors (Scopus).

OzHarvest is a for impact organisation with a driving to purpose to ‘Nourish our Country.’ Best known for their iconic yellow food rescue vans which save over 250 tonnes of good food every week, delivering it direct to over 1900 charities that feed people in need. OzHarvest creates positive change with our three education programs, our advocacy work for food security in Australia.

Erica is modelling fire risk using various tools to better estimate fire risk across a range of landscapes. Her current projects estimate fire risk under current and future climate change scenarios and model potential management options for reducing risk to biodiversity and assets.

Across the city there are many actors and projects working to reorient power, materials, resources and knowledges in service to the regeneration of our city. Yet we still have a long way to go to shift extractive and accumulated capital and transition our policy and politics towards a more equitable and long term view. Our ways of collaborating, distributing resources and harnessing the collective intelligences of our place are not set up well to accelerate these pockets of potential.

Advanced Approaches to Vehicle Routing Problems with a Focus on Driver Preferences

Vehicle Routing Problems (VRPs) represent a cornerstone of operational research, particularly within the domain of city logistics. These complex and multifaceted optimisation challenges are pivotal for enhancing efficiency and reducing operational costs in numerous industries. However, existing VRP solvers frequently encounter difficulties in effectively incorporating driver preferences, scaling to the complexities and dimensions of real-world scenarios. This proposal outlines an innovative workshop dedicated to exploring new methodologies in addressing VRPs, with a special focus on a cluster first, route second (CFRS) approach that integrates driver preferences derived from historical data.

Objective
The primary aim of this workshop is to delve into the intricacies of VRPs, particularly the CFRS VRP, and to showcase novel strategies that not only consider logistical optimisation but also integrate driver preferences into the routing decisions. By bringing together industry professionals, academics, and researchers, the workshop intends to foster a rich dialogue on the challenges and solutions related to modern VRP solvers. The workshop will highlight the importance of incorporating human factors into algorithmic solutions, demonstrating how this integration can lead to more efficient, practical, and sustainable logistics operations.

Cure at What Cost?

Dr Jennifer Soon will lead this case study.

The treatment landscape of cancer is rapidly evolving with medical advances providing new opportunities for improved cancer outcomes. In the space of 15 years, the median overall survival of patients with advanced cutaneous melanoma has increased from 6-10 months to now in excess of 6 years. This is mainly due to the implementation of high-cost novel therapies such as checkpoint inhibitors (or immunotherapy) and targeted therapies. These medicines are now being used to treat earlier stages of disease; however, the optimal sequencing and combination of these treatments is unclear.

Furthermore, questions are now being raised as to whether these treatments, which can have chronic and life-changing side effects, can be de-escalated to minimise overtreatment and optimise patient outcomes. Clinical trials alone do not provide the answers to these questions. Economic modelling can be used to help predict the impact of novel therapies and other changes to treatment pathways on patient outcomes and resource utilisation. This information will inform health policymakers and leaders who need to make decisions on resource allocation, workforce planning, and medicine reimbursement.

Dr Jennifer Soon is a Medical Oncologist with a special interest in health policy and health services research leveraging data science methodologies. Following on from a Melanoma and Translational Research Fellowship with Professor Grant McArthur at Peter MacCallum Cancer Centre in 2020, she has embarked on a PhD project that marries these interests and explores the interaction between health economics and the pursuit of outcomes that matter to patients. Her current research focuses on de-intensification or de-escalation of melanoma management, horizon scanning, and leveraging a large population-level data linkage to model the impact of novel therapies and strategies in melanoma.

Stating the Problem

Applying optimisation in practice is often riddled with difficulties. From the selection of an available solver, through its integration into an existing workflow, to how to configure it for the best performance, there are many decisions to be made. However, the difficulties typically start well before that. Specific solvers require problems to be modelled in specific ways, and the performance of the solver will also depend on the characteristics of the problem model. Developing a good model may be time-consuming and/or require a high degree of expertise, and the data required to instantiate the model may not be available or may be difficult to obtain. However, the main difficulty is often experienced at an even earlier stage: defining the actual optimisation problem to be solved!

This workshop is about stating an optimisation problem in the context of a practical situation, rather than considering how such a problem could be modelled or even solved. Starting from the outline of a hypothetical real-world scenario involving multiple stakeholders and/or some degree of uncertainty, the challenge posed to the participants is to produce a well-posed problem statement based on that scenario. The statement should define the data required to specify a concrete instance of the problem, the space of possible feasible solutions for a given instance, and how the performance of individual feasible solutions can be evaluated according to any number of criteria. Additionally, a data set defining a small problem instance, a corresponding feasible solution, and an explanation of its evaluation, should be produced for the purpose of illustration and, to some degree, validation of the problem statement. The problem statements developed by the participants during the workshop will be presented and discussed at the end.

This workshop is based upon work from COST Action Randomised Optimisation Algorithms Research Network (ROAR-NET), CA22137, supported by COST (European Cooperation in Science and Technology).

Carlos M. Fonseca is an Associate Professor at the Department of Informatics Engineering of the University of Coimbra, Portugal, and a member of the Adaptive Computation group of CISUC, the Centre for Informatics and Systems of the University of Coimbra. He obtained his doctoral degree from the University of Sheffield, U.K., where he also conducted post-doctoral research. His main research interests are in multiobjective optimisation, evolutionary computation, experimental evaluation of optimisation algorithms, and practical applications of optimisation. He currently focuses mainly on preference articulation in multiobjective optimisation and the computational modelling of optimisation problems. He was the General or Technical co-chair of major international conferences, a mentor in several Summer Schools, and Guest co-editor of three journal Special Issues (one to appear). He serves as Action Chair and Grant Holder Scientific Representative of COST Action ROAR-NET, a European research and innovation network aimed at making randomised optimisation algorithms widely competitive in practice by identifying and reducing obstacles to their adoption at the scientific, technical, economic, and human levels.

Optimisation Technologies

• Bing Wang – Improving the Performance of Bilevel Evolutionary Algorithms Using Variable Associations
• David Ferguson – Sharp Opturion Optimisation
• Fatemeh Jalalvand – Enhancing Optimization Methods for Real-World Applications: Integrating Machine Learning Techniques for Improved Performance
• Hemant Singh – Evolutionary Multi-Objective Optimization: Recent Developments and Practical Case Studies
• João Luiz Junho Pereira – Artificial Intelligence Simplifying Unknown Problems
• Kate Smith-Miles – Can I Trust This Algorithm? Show Me the Stress-Testing!
• Vivek Katial – Enhancing Quantum Optimisation by leveraging their features
• Qingyang Li – Synthesizing Mixed-Integer Linear Programming Models from Natural Language Descriptions
• Sardar M N Islam (Naz) – A Game Theory-Based New Approach to Optimisation Modelling for Optimal Financial Management
• Sarah Ryan – Transitioning from Off-Patent to Innovation Molecules Within the Agrochemical Industry
• Simranjit Kaur – Hidden Gems: Advanced Modelling Features in Gurobi
• Sameela Suharshani Wijesundara – Towards Domain Specific Languages for Combinatorial Optimisation Modelling

Advanced Manufacturing

• Andrew Gooley – Collaboration in the Industrial Transformation Training Centre Program – An Industry Perspective
• Elham Mardaneh – Efficient Multi-Campaign Maintenance Scheduling for Minimizing Equipment Shutdowns in the Oil and Gas Industry
• Iman Rahimi – Enhancing Multi-objective Optimisation of Open-pit Mining Problem via Boundary Updating
• Nico Andres Thio – An Introduction to Methodology and Challenges of Multifidelity Expensive Black-Box Problems
• Stephen Jarvis – BlueScope Steel – Paint Schedule Optimisation

Water and Resource Management

• Vaughn Grey – Optimising Long-Term Water Quality Monitoring of Rivers
• Philip Meng – Recent Applications of Optimisation Technologies at Water Corporation, Western Australia
• Rahul Rao – AGL Agua – Hydro Dispatch Optimisation
• Sarang Kulkarni – Optimisation of Groundwater Production Scheduling at Water Corporation, Western Australia
• Yi Zhen – Multi-Objective Optimization in Urban Water System

Defence and Cybersecurity

• Emely Pierau – Hyper-Spectral Image Data from Distributed Acoustic Sensing Systems for Intrusion Detection and Monitoring
• Hue Chi Lam – Multi-Objective Optimization with Multiple Decision Makers Based on User-Preferences
• Jason Looker – Stochastic Integrated Military Aircraft Fleet Management
• Jeffrey Christiansen – A Robust Optimisation Approach for a Two-Player Force-Design Game
• Sevvandi Kandanaarachchi – Predicting Dynamic Networks

Green Energy

• Dante Recalde – Building a Resilient Energy Future: Leveraging Optimization for Grid Modernization
• Hirad Assimi – Mining for a Greener Tomorrow: Underground Mining Truck Electrification
• Muhammad Haider Ali Khan – Framework of Open Source Tools for Optimising Green H2 Projects
• Rohan Horsely – Lochard Energy’s H2RESTORE Project – Optimisation in the Context of Uncertainty
• Soobok Yoon – The Optimal Infrastructure Design for Smart Charging System

Health and Wellbeing

• Alex de Sa – AutoML-Driven Small Molecule Pharmacokinetic and Toxicity Prediction
• Draga Doncila Pop – TrackTour: Cell Tracking with Linear Optimization and Guided Error Correction
• Tien Dung Pham – Optimizing Chromatography Processes Under Uncertainty: A Robust Approach Using Conformal Intervals
• Michael Kirley – Data-Driven Root Cause Analysis for Biopharmaceuticals Manufacturing
• Setyo Tri Windras Mara – Be Prepared for Sustainable Transport in Healthcare: A New Model for Multi-Modal Blood Delivery System

Logistics and Transport

• Amin Karimi – Stochastic Fleet Sizing and Routing in Aerial Delivery
• Sekar Sakti – Synchromodal Transport Planning and Pricing Under Dynamic and Stochastic Environments
• Sergey Polyakovskiy – Cutting and Packing as Part of Multi-Component Optimization Problems
• Tom Sandeman – Deploying Optimisation in Logistics Planning
• Yijie Su – The Large-Scale Impact of Micro-Consolidation Hubs in Urban Deliveries in Melbourne: A Hybrid Approach of Traffic Simulation and Optimisation

Optimisation in Action

• Harry McArthur – Balancing Privacy and Utility with Margin Consistent Noise
• Ryan Covey – A Trio of Optimisation Problems at the Australian Bureau of Statistics
• Leilah Taouk – The Development of a Fuel Management Schedule to Mitigate the Severity of Forest Fires
• Liam Timms – How to Estimate a Population Average by Talking to Your Neighbours: A Decentralised Approach to Optimisation
• Seyed Sina Mohri – Optimizing Online Order Collection: An Adaptive Large Neighbourhood Search Algorithm for Crate Building in Grocery Stores

Workforce Scheduling

• Hritika Gupta – Waiting Time Distributions of Customers in Call Centres
• Hajar Sadegh Zadeh – Optimising Operating Room Scheduling Considering Emergency Case Arrivals: A Stochastic Modelling Approach
• Vicky Mak – Optimisation Solution for Defence Workforce Planning

With Gurobi’s decision intelligence technology, you can make optimal business decisions in seconds. From workforce scheduling, portfolio management, and marketing optimization, to supply chain design, and everything in between, Gurobi identifies your optimal solution, out of trillions of possibilities. Gurobi has operations across the Americas, Europe, and Asia. Over 2,500 customers across 40+ industries run on Gurobi, including SAP, Air France, and the NFL, as well as half of the Fortune 10. For more information, visit https://www.gurobi.com/ or email sales-apac@gurobi.com.

The Melbourne Centre for Data Science is building a vibrant, research-intensive, and a collaborative interdisciplinary environment to lead advancements in data science for the benefit of society.

The Optimization Firm develops software tools for optimization (BARON) and machine learning (ALAMO) for more than 1,000 customers, including world-leading universities, research institutions, and companies spanning the energy, finance, healthcare, materials, and technology sectors.

The Australian Mathematical Sciences Institute (AMSI) is a not-for-profit peak body for the mathematical sciences with a breadth of members including Australian universities, professional societies, government agencies and industry. We champion mathematics, statistics, and data science for Australia’s advancement through advocacy, facilitation, and collaboration.

The common purpose we share with our members and partners is the radical improvement of mathematical capacity and facility, both in Australia and Asia-Pacific. AMSI’s impact is underpinned by our convening power across the mathematical sciences ecosystem.

AMSI’s activities fall largely into three strategic areas – school education, research and higher education, and industry engagement. In school education, we focus on teacher professional development to foster teaching excellence and inspire students to study mathematics to year 12. Research and higher education delivers advanced research training and research workshops to continue the development of undergraduate and postgraduate maths students, in preparation for careers in academia and industry. Our industry division is home to the national PhD internship program, APR.Intern, which has successfully grown to an all-discipline all-sector program working with Australian universities and industry.

OPTIMA-CON 24 has received a generous Flagship Activity Grant from the Theo Murphy Initiative (TMI) Australia.

TMI was established by the Royal Society to further scientific discovery. The fund is administered by the Australian Academy of Science and provides grant funding to support career development opportunities for Australia’s early- and mid-career researchers (EMCRs) in science, technology, engineering, and mathematics (STEM). Applicants will need to demonstrate how the proposed activity will support EMCRs knowledge development in the relevant STEM related field(s) of research.

OPTIMA-CON 24 has received grant funding from The Australian Mathematical Society which is the national society of the mathematics profession in Australia.

OPTIMA-CON 24 has received grant funding from our conference host the Melbourne Convention and Exhibition Centre.

MCEC offers flexible spaces, innovative menus, cutting-edge tech, and a dedicated team to help you deliver an unforgettable experience.

Alysson is an expert in mathematical programming with a focus on mixed-integer programming modelling and decomposition solution algorithms. He has held a Senior Lectureship position since 2017. He obtained PhD in Business Administration (Quantitative Methods) from HEC – University of Montreal. In 2008 he commenced at the Applied Mathematics Department of the University of São Paulo. He has been a chief investigator in several projects funded by CAPES, CNPq and FAPESP, the Spanish Ministry of Education, the European Commission (Marie-Curie Actions) and the ARC. He has 40 journal publications on theory and multidisciplinary applications of optimisation in several applied engineering areas. He has supervised nine PhD students and one post-doc and is the current primary supervisor of three PhD students. Alysson has provided mathematical modelling and algorithmic development input to research on a wide range of application areas, resulting in published outputs with more than 50 co-authors (Scopus).

OzHarvest is a for impact organisation with a driving to purpose to ‘Nourish our Country.’ Best known for their iconic yellow food rescue vans which save over 250 tonnes of good food every week, delivering it direct to over 1900 charities that feed people in need. OzHarvest creates positive change with our three education programs, our advocacy work for food security in Australia.

Erica is modelling fire risk using various tools to better estimate fire risk across a range of landscapes. Her current projects estimate fire risk under current and future climate change scenarios and model potential management options for reducing risk to biodiversity and assets.

Advanced Approaches to Vehicle Routing Problems with a Focus on Driver Preferences

Vehicle Routing Problems (VRPs) represent a cornerstone of operational research, particularly within the domain of city logistics. These complex and multifaceted optimisation challenges are pivotal for enhancing efficiency and reducing operational costs in numerous industries. However, existing VRP solvers frequently encounter difficulties in effectively incorporating driver preferences, scaling to the complexities and dimensions of real-world scenarios. This proposal outlines an innovative workshop dedicated to exploring new methodologies in addressing VRPs, with a special focus on a cluster first, route second (CFRS) approach that integrates driver preferences derived from historical data.

Objective
The primary aim of this workshop is to delve into the intricacies of VRPs, particularly the CFRS VRP, and to showcase novel strategies that not only consider logistical optimisation but also integrate driver preferences into the routing decisions. By bringing together industry professionals, academics, and researchers, the workshop intends to foster a rich dialogue on the challenges and solutions related to modern VRP solvers. The workshop will highlight the importance of incorporating human factors into algorithmic solutions, demonstrating how this integration can lead to more efficient, practical, and sustainable logistics operations.

Cure at What Cost?

Dr Jennifer Soon will lead this case study.

The treatment landscape of cancer is rapidly evolving with medical advances providing new opportunities for improved cancer outcomes. In the space of 15 years, the median overall survival of patients with advanced cutaneous melanoma has increased from 6-10 months to now in excess of 6 years. This is mainly due to the implementation of high-cost novel therapies such as checkpoint inhibitors (or immunotherapy) and targeted therapies. These medicines are now being used to treat earlier stages of disease; however, the optimal sequencing and combination of these treatments is unclear.

Furthermore, questions are now being raised as to whether these treatments, which can have chronic and life-changing side effects, can be de-escalated to minimise overtreatment and optimise patient outcomes. Clinical trials alone do not provide the answers to these questions. Economic modelling can be used to help predict the impact of novel therapies and other changes to treatment pathways on patient outcomes and resource utilisation. This information will inform health policymakers and leaders who need to make decisions on resource allocation, workforce planning, and medicine reimbursement.

Dr Jennifer Soon is a Medical Oncologist with a special interest in health policy and health services research leveraging data science methodologies. Following on from a Melanoma and Translational Research Fellowship with Professor Grant McArthur at Peter MacCallum Cancer Centre in 2020, she has embarked on a PhD project that marries these interests and explores the interaction between health economics and the pursuit of outcomes that matter to patients. Her current research focuses on de-intensification or de-escalation of melanoma management, horizon scanning, and leveraging a large population-level data linkage to model the impact of novel therapies and strategies in melanoma.

Stating the Problem

Applying optimisation in practice is often riddled with difficulties. From the selection of an available solver, through its integration into an existing workflow, to how to configure it for the best performance, there are many decisions to be made. However, the difficulties typically start well before that. Specific solvers require problems to be modelled in specific ways, and the performance of the solver will also depend on the characteristics of the problem model. Developing a good model may be time-consuming and/or require a high degree of expertise, and the data required to instantiate the model may not be available or may be difficult to obtain. However, the main difficulty is often experienced at an even earlier stage: defining the actual optimisation problem to be solved!

This workshop is about stating an optimisation problem in the context of a practical situation, rather than considering how such a problem could be modelled or even solved. Starting from the outline of a hypothetical real-world scenario involving multiple stakeholders and/or some degree of uncertainty, the challenge posed to the participants is to produce a well-posed problem statement based on that scenario. The statement should define the data required to specify a concrete instance of the problem, the space of possible feasible solutions for a given instance, and how the performance of individual feasible solutions can be evaluated according to any number of criteria. Additionally, a data set defining a small problem instance, a corresponding feasible solution, and an explanation of its evaluation, should be produced for the purpose of illustration and, to some degree, validation of the problem statement. The problem statements developed by the participants during the workshop will be presented and discussed at the end.

This workshop is based upon work from COST Action Randomised Optimisation Algorithms Research Network (ROAR-NET), CA22137, supported by COST (European Cooperation in Science and Technology).

Carlos M. Fonseca is an Associate Professor at the Department of Informatics Engineering of the University of Coimbra, Portugal, and a member of the Adaptive Computation group of CISUC, the Centre for Informatics and Systems of the University of Coimbra. He obtained his doctoral degree from the University of Sheffield, U.K., where he also conducted post-doctoral research. His main research interests are in multiobjective optimisation, evolutionary computation, experimental evaluation of optimisation algorithms, and practical applications of optimisation. He currently focuses mainly on preference articulation in multiobjective optimisation and the computational modelling of optimisation problems. He was the General or Technical co-chair of major international conferences, a mentor in several Summer Schools, and Guest co-editor of three journal Special Issues (one to appear). He serves as Action Chair and Grant Holder Scientific Representative of COST Action ROAR-NET, a European research and innovation network aimed at making randomised optimisation algorithms widely competitive in practice by identifying and reducing obstacles to their adoption at the scientific, technical, economic, and human levels.

Optimisation Technologies

• Bing Wang – Improving the Performance of Bilevel Evolutionary Algorithms Using Variable Associations
• David Ferguson – Sharp Opturion Optimisation
• Fatemeh Jalalvand – Enhancing Optimization Methods for Real-World Applications: Integrating Machine Learning Techniques for Improved Performance
• Hemant Singh – Evolutionary Multi-Objective Optimization: Recent Developments and Practical Case Studies
• João Luiz Junho Pereira – Artificial Intelligence Simplifying Unknown Problems
• Kate Smith-Miles – Can I Trust This Algorithm? Show Me the Stress-Testing!
• Vivek Katial – Enhancing Quantum Optimisation by leveraging their features
• Qingyang Li – Synthesizing Mixed-Integer Linear Programming Models from Natural Language Descriptions
• Sardar M N Islam (Naz) – A Game Theory-Based New Approach to Optimisation Modelling for Optimal Financial Management
• Sarah Ryan – Transitioning from Off-Patent to Innovation Molecules Within the Agrochemical Industry
• Simranjit Kaur – Hidden Gems: Advanced Modelling Features in Gurobi
• Sameela Suharshani Wijesundara – Towards Domain Specific Languages for Combinatorial Optimisation Modelling

Advanced Manufacturing

• Andrew Gooley – Collaboration in the Industrial Transformation Training Centre Program – An Industry Perspective
• Elham Mardaneh – Efficient Multi-Campaign Maintenance Scheduling for Minimizing Equipment Shutdowns in the Oil and Gas Industry
• Iman Rahimi – Enhancing Multi-objective Optimisation of Open-pit Mining Problem via Boundary Updating
• Nico Andres Thio – An Introduction to Methodology and Challenges of Multifidelity Expensive Black-Box Problems
• Stephen Jarvis – BlueScope Steel – Paint Schedule Optimisation

Water and Resource Management

• Vaughn Grey – Optimising Long-Term Water Quality Monitoring of Rivers
• Philip Meng – Recent Applications of Optimisation Technologies at Water Corporation, Western Australia
• Rahul Rao – AGL Agua – Hydro Dispatch Optimisation
• Sarang Kulkarni – Optimisation of Groundwater Production Scheduling at Water Corporation, Western Australia
• Yi Zhen – Multi-Objective Optimization in Urban Water System

Defence and Cybersecurity

• Emely Pierau – Hyper-Spectral Image Data from Distributed Acoustic Sensing Systems for Intrusion Detection and Monitoring
• Hue Chi Lam – Multi-Objective Optimization with Multiple Decision Makers Based on User-Preferences
• Jason Looker – Stochastic Integrated Military Aircraft Fleet Management
• Jeffrey Christiansen – A Robust Optimisation Approach for a Two-Player Force-Design Game
• Sevvandi Kandanaarachchi – Predicting Dynamic Networks

Green Energy

• Dante Recalde – Building a Resilient Energy Future: Leveraging Optimization for Grid Modernization
• Hirad Assimi – Mining for a Greener Tomorrow: Underground Mining Truck Electrification
• Muhammad Haider Ali Khan – Framework of Open Source Tools for Optimising Green H2 Projects
• Rohan Horsely – Lochard Energy’s H2RESTORE Project – Optimisation in the Context of Uncertainty
• Soobok Yoon – The Optimal Infrastructure Design for Smart Charging System

Health and Wellbeing

• Alex de Sa – AutoML-Driven Small Molecule Pharmacokinetic and Toxicity Prediction
• Draga Doncila Pop – TrackTour: Cell Tracking with Linear Optimization and Guided Error Correction
• Tien Dung Pham – Optimizing Chromatography Processes Under Uncertainty: A Robust Approach Using Conformal Intervals
• Michael Kirley – Data-Driven Root Cause Analysis for Biopharmaceuticals Manufacturing
• Setyo Tri Windras Mara – Be Prepared for Sustainable Transport in Healthcare: A New Model for Multi-Modal Blood Delivery System

Logistics and Transport

• Amin Karimi – Stochastic Fleet Sizing and Routing in Aerial Delivery
• Sekar Sakti – Synchromodal Transport Planning and Pricing Under Dynamic and Stochastic Environments
• Sergey Polyakovskiy – Cutting and Packing as Part of Multi-Component Optimization Problems
• Tom Sandeman – Deploying Optimisation in Logistics Planning
• Yijie Su – The Large-Scale Impact of Micro-Consolidation Hubs in Urban Deliveries in Melbourne: A Hybrid Approach of Traffic Simulation and Optimisation

Optimisation in Action

• Harry McArthur – Balancing Privacy and Utility with Margin Consistent Noise
• Ryan Covey – A Trio of Optimisation Problems at the Australian Bureau of Statistics
• Leilah Taouk – The Development of a Fuel Management Schedule to Mitigate the Severity of Forest Fires
• Liam Timms – How to Estimate a Population Average by Talking to Your Neighbours: A Decentralised Approach to Optimisation
• Seyed Sina Mohri – Optimizing Online Order Collection: An Adaptive Large Neighbourhood Search Algorithm for Crate Building in Grocery Stores

Workforce Scheduling

• Hritika Gupta – Waiting Time Distributions of Customers in Call Centres
• Hajar Sadegh Zadeh – Optimising Operating Room Scheduling Considering Emergency Case Arrivals: A Stochastic Modelling Approach
• Vicky Mak – Optimisation Solution for Defence Workforce Planning