Sponsors and Donors

Gurobi builds, sells and supports a math-programming engine for solving linear programming and mixed-integer programming problems. Gurobi provides the tools that do the “heavy lifting” associated with modern optimization applications.

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.

Careers and Industry Meet and Greet

This session is twofold.

Industry networking

Industry practitioners and academics will network in a relaxed setting to discuss the take-home messages from the conference.

Careers fair

We will also host a career fair for industry to showcase their career opportunities to other industry practitioners, academics and young researchers. For this session, we will invite up to 100 additional talented Masters/PhD students, in addition to those attending the conference.

Industry Panel (Optimisation for Social Good)

OPTIMA Chief Investigator Alysson Costa will lead a panel of not-for-profit organisations to discuss their specific challenges that can be approached through optimisation techniques. It promises to be an insightful session that delves into challenges that affect many organisations and how those challenges can be tackled head-on.

NFP participants to be announced shortly

Industry Breakfast Briefing – Gurobi

This panel discussion is aimed directly at our industry guests and will be hosted over a hot buffet breakfast. Led by Gurobi with a panel of expert optimisation researchers, this session will highlight successful case studies and share advances in optimisation techniques that can improve efficiencies in industry.

Case Study Workshops

We will select three real-world industry case studies proposed by our industry attendees. These will be presented by the industry practitioner and through a series of brainstorming sessions several solutions will be raised. These sessions will be interesting and informative, They will suit industry practitioners in understanding the real problems that other industries face and discover how these problems can be approached. These sessions will also suit our academics and young researchers in brainstorming new ideas and proposing real solutions.

If you are interested in submitting a case study please “Submit an abstract” to find the submission link.

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.

Evolutionary Multiobjective Optimization

Carlos M. Fonseca will lead a case study in Evolutionary Multiobjective Optimization. More information will be announced shortly.

Carlos graduated in Electronic and Telecommunications Engineering from the University of Aveiro, Portugal, in 1991, and obtained his doctoral degree from the University of Sheffield, U.K., in 1996. He is an Associate Professor at the Department of Informatics Engineering of the University of Coimbra, Portugal, and a member of the Adaptive Computation (AC) group of the Centre for Informatics and Systems of the University of Coimbra (CISUC). Formerly, he was a Research Associate with the Department of Automatic Control and Systems Engineering of the University of Sheffield, U.K., and a Lecturer at the Department of Electronic Engineering and Informatics, Faculty of Science and Technology, University of Algarve, Portugal. His current research interests include multiobjective optimization, evolutionary algorithms, experimental assessment of algorithms, dynamical systems, and engineering design optimization.

Optimisation Technologies

• Bing Wang – Improving the Performance of Bilevel Evolutionary Algorithms Using Variable Associations
• David Ferguson – Sharp Opturion Optimisation
• Fatemeh Jalalvand – Seeking Higher Performance in a Multi-Objective Simulation-Based Optimization Method
• 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 – Towards Instance-Specific QAOA: Predicting Optimal Layer Depths for MaxCut
• Qingyang Li – Synthesizing Mixed-Integer Linear Programming Models from Natural Language Descriptions
• Maria Laura Santoni – Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
• 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

• Grey Vaughn – 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

Infrastructure, Logistics and Transport, Optimisation Technologies

• Amin Karimi – Stochastic Fleet Sizing and Routing in Aerial Delivery
• Sekar Sakti – Synchromodal Transport Planning and Pricing Under Dynamic and Stochastic Environments
• Sergey Polyakovskiy – Infrastructure, Logistics and Transport, Optimisation Technologies
• 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

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

Retail

• Anna-Lena Sachs – Inventory Decisions in Retailing: A Field Experiment
• Mudassar Rauf – Self-Learning Multi-Criteria Inventory Classification Technique
• Seyed Sina Mohri – Optimizing Online Order Collection: An Adaptive Large Neighbourhood Search Algorithm for Crate Building in Grocery Stores