This hackathon is open to everyone, from undergraduate students through to senior academics, and industry.

Can you optimise the flow of one of Australia’s busiest airports?

Join the OPTIMA 2026 Hackathon, a one-day, hands-on challenge focused on improving aircraft turnaround scheduling at Melbourne Airport.

Work in teams of two to tackle a real-world optimisation problem and develop practical, high-impact solutions.

Why Take Part?

In this one-day hackathon, you will:

• Collaborate with OPTIMA researchers across disciplines

• Apply optimisation methods to a real industry problem

• Gain practical experience solving complex challenges

• Compete for prizes and present your ideas

How to Register

• Register as a team and include both names

• Register individually and we will match you with a teammate

Additional Information

• Monash University OPTIMA members can request a free Uber shuttle when registering

• Undergraduate or non-OPTIMA participants should include the name of the OPTIMA member who recommended them in the Notes section
  Can you optimise the flow of one of Australia’s busiest airports?

Speaker: Associate Professor Guido Tack
OPTIMA/Monash University

Summary:
Constraint solving is a way of describing decision problems using variables and rules, and then automatically finding solutions that satisfy those rules. It is widely used in applications such as scheduling, assignment, configuration, routing, and rostering. The systems used for solving these problems are usually designed for desktop and server machines with abundant memory. This talk explores whether constraint programming can be reengineered for microcontroller-class hardware instead, where processors are cheap and increasingly capable, but RAM is often limited to only a few hundred kilobytes.
I will present Thornbill-CP, a constraint programming solver designed for that environment, and discuss what changes are necessary when constraint solving is pushed into such a tight memory budget. The talk will explain briefly how traditional constraint solvers work, and then cover the main architectural ideas behind Thornbill-CP, early results from running it on ESP32 and RP-series devices, and the kinds of embedded applications that could benefit from on-device constraint solving.

Bio:
Guido is an Associate Professor in the Department of Data Science and Artificial Intelligence at the Faculty of Information Technology. His research focuses on combinatorial optimisation, in particular architecture and implementation techniques for constraint solvers, translation of constraint modelling languages, and industrial applications. Guido leads the development of the MiniZinc constraint modelling language and toolchain, and he is one of the leading developers of Gecode, a state-of-the-art constraint programming library. Guido’s broader research interests include programming languages and computational logic.

This event is Hybrid:
JOIN VIA ZOOM – MEETING ID: 873 1557 5255; PASSWORD: 778635
ATTEND IN PERSON – Location to be confirmed closer to the seminar date

SEMINAR: WED 24 June 2026 16:00-17:00 (AEST, Melbourne Time)

Speaker: Eleon Bach
Technical University of Munich

Summary:
Narrowing the gap between theory and practice is a longstanding goal of the algorithm analysis community. In this talk we discuss the new by the book algorithm analysis framework.
In contrast to earlier frameworks, by the book analysis not only models an algorithm’s input data, but also the algorithm itself. Results from by the book analysis are meant to correspond well with established knowledge of an algorithm’s practical behavior, as they are meant to be grounded in observations from implementations, input modeling best practices, and measurements on practical benchmark instances.
Furthermore, we apply our framework to the simplex method, an algorithm which is beloved for its excellent performance in practice and notorious for its high running time under worst-case analysis.
The simplex method similarly showcased the state of the art framework smoothed analysis (Spielman and Teng, STOC’01).
We prove that under input scaling assumptions, feasibility tolerances and other design principles used by simplex method implementations, the simplex method indeed attains a polynomial running time. This is joint work with Alexander Black, Sophie Huiberts and Sean Kafer.

Bio:
Eleon Bach is a PhD student at TU Munich. Their research focusses on the analysis of practical algorithms such as the simplex algorithm. Eleon’s research was featured by the Quanta magazine and WIRED.

—
This event is Zoom only:
JOIN VIA ZOOM WITH MEETING ID: 873 1557 5255; PASSWORD: 778635

SEMINAR: WED 08 APRIL 2026 17:00-18:00 (AEST, Melbourne)/ 09:00-10:00 (CEST)
Please note seminar time to accommodate different timezones.

Title: Optimal Call Allocation in Call Centres

Speaker: Hritika Gupta
University of Wollongong

Summary:
We address the problem of determining an optimal call allocation policy for call centres operating under a finite time horizon with skills-based routing. The problem is modelled as a Markov decision process, and the optimal policy is characterised using Bellman’s equation. Furthermore, we present a procedure to compute transient waiting time distributions for customers under threshold policies for the continuous-time case. This framework can assist call centres in meeting client service-level targets, such as ensuring that a specified proportion of calls are answered within a given time frame.

Bio:
Hritika is a Research Associate at the University of Wollongong. Her postdoctoral research focuses on the pricing of weather derivatives using Hidden Markov Models, as part of an ARC Discovery Project under the supervision of Prof Mariano Rodrigo.
Hritika’s PhD research, completed at the University of Melbourne, focused on optimising call allocation in call centers using advanced statistical methods. She also holds a Master’s in Mathematics and Statistics and a Bachelor’s in Statistics (Honors) from the University of Melbourne and the University of Delhi respectively.

—

This event is Zoom only:
JOIN VIA ZOOM WITH MEETING ID: 873 1557 5255; PASSWORD: 778635
SEMINAR: WED 01 APRIL 2026 16:00-17:00 (AEDT, Melbourne Time)

Speaker: Harry McArthur
University of Melbourne

Title: On Privacy—Utility Trade-offs with Synthetic Data Under Reconstruction Attacks

Summary:
We study how statistical agencies can publish useful population data while protecting individual privacy. Many real-world systems, like those used by the Australian Bureau of Statistics, achieve this goal by adding independent, bounded random noise to published tables prior to publication—but these methods remain vulnerable to reconstruction attacks that can reveal sensitive information. We consider an alternative approach based on synthetic data generation, which perturbs the underlying database itself rather than the published statistics. We show that this approach can provide better privacy—trade-offs when releasing large volumes of statistical information.

Bio:
Harry is an OPTIMA industry PhD student working jointly with the Australian Bureau of Statistics, supervised by Kate Smith-Miles and Peter Taylor.

—

This event is Hybrid:
JOIN VIA ZOOM – MEETING ID: 873 1557 5255; PASSWORD: 778635

REGISTER HERE TO ATTEND IN PERSON: https://go.unimelb.edu.au/pfj2
Please arrive early to find the location and find a seat
Level 1, Alison Hartcourt Seminar room 162, Peter Hall Building, University of Melbourne, 813 Swanston St, Parkville, 3053 (near corner of Swanston St/Monash Rd, Melbourne Uni tram stop 1)
This is a 4min walk from Melbourne Connect in the main Melbourne University campus (see google map)

SEMINAR: WED 11 MARCH 2026 16:00-17:00 (AEDT, Melbourne Time)

Join us for special OPTIMA Seminar presented by industry guest speaker, Prateek Aranha from Endgame Analytics, as he connects optimisation theory with real-world deployment.

Speaker: Prateek Aranha
Endgame Analytics

Title: Formulation to Frontline – Taking Models to Production

Summary:
A strong formulation is just the beginning. Translating it into a practical, user-facing tool requires a keen understanding of both user needs and business goals. This talk will share real-world examples of the challenges faced, and the trade-offs made to encourage adoption. It will also highlight the key architectural choices that allowed our models to scale sustainably.

Bio:
Prateek Aranha is a consultant in the Operations Research team at Endgame Analytics. He has applied optimisation methods to solve problems in a wide range of sectors including aviation, retail, transport, media and energy. He holds a Bachelor of Advanced Computing (Honours) from the Australian National University, where his research focused on scheduling maintenance operations in the electricity grid under uncertainty.

—

This event is Hybrid:
JOIN VIA ZOOM – MEETING ID: 873 1557 5255; PASSWORD: 778635

REGISTER HERE TO ATTEND IN PERSON: https://go.unimelb.edu.au/mwx2
Meet our speaker and enjoy some afternoon tea by arriving at 3:30pm
Level 8, Room 8109, Melbourne Connect, 700 Swanston Street, Carlton 3053

SEMINAR: WED 25 FEBRUARY 2026 16:00-17:00 (AEDT, Melbourne Time)

Title: A Two-Stage Optimisation Framework for Efficient Operating Room Management under Uncertainty

Speaker: Hajar Sadegh Zadeh
University of Melbourne

Summary:
This Research presents an integrated, data-driven framework for efficient operating room management under uncertainty. Using synthetic hospital data from 2018–2022, it combines advanced forecasting, preventive scheduling, and real-time decision-making to balance elective and emergency surgeries. State-of-the-art models—SARIMAX and a non-homogeneous Poisson process—efficiently predict daily and hourly emergency arrivals. A two-step stochastic optimisation model with Sample Average Approximation proactively allocates elective cases with buffer times for emergency case arrivals, while a Markov Decision Process enables adaptive on-the-day scheduling. Applied to a major Australian hospital, the framework reduces emergency cancellations and costs, enhances utilisation, and supports evidence-based surgical planning.

Bio:
Hajar is a PhD candidate in the School of Mathematics and Statistics at the University of Melbourne, supervised by Dr Mark Fackrell, A/Prof Joyce Zhang, and A/Prof Hamideh Anjomshoa.

Her research draws on operations research, statistical data science, machine learning, and stochastic optimisation, with a strong focus on analysing real-world data and developing practical decision-support tools. She has extensive experience in data analysis, forecasting, and model development, with an emphasis on translating analytical results into actionable insights, particularly in healthcare settings.

Her PhD is structured around four integrated stages: (i) exploratory data analysis to identify operational challenges in hospital operating rooms; (ii) forecasting emergency surgery arrivals; (iii) preventive scheduling of elective and emergency surgeries using a two-stage stochastic model; and (iv) real-time surgery scheduling using a Markov decision process. Her work aims to reduce cancellations and waiting times while improving the efficiency of Australian public hospitals.

—

This event is Zoom only:
JOIN VIA ZOOM WITH MEETING ID: 873 1557 5255; PASSWORD: 778635
SEMINAR: WED 18 MARCH 2026 16:00-17:00 (AEDT, Melbourne Time)

Title: Green energy networks, games, and getting optimal solutions

Speaker: Liam Timms
University of Melbourne

Summary:
Power banks present an increasingly accessible, adaptable, and eco-friendly way to use and store power. Multiple power banks can be coordinated to create a virtual power plant (VPP) that can satisfy larger power demands. However, coordinating a VPP introduces difficulties regarding how individual charging decisions interact. In this talk, we model VPP coordination as a non-cooperative game with individual power banks as agents. To solve this model and find optimal charging schedules, we develop an optimisation algorithm based on monotone operator splitting. This algorithm is distributed to reflect the independent agents making up the VPP, and decentralised to avoid the drawbacks of a centrally coordinating agent.

Bio: 
Liam Timms is a PhD student at the University of Melbourne, working on improved algorithms for distributed optimisation. Their research focuses on new algorithms for optimisation problems of ethical and environmental significance, grounded in mathematical theory and computational science. They are particularly interested in the mathematical framework of operator inclusions, which allows a wide array of complex decision-making problems to be addressed and solved with the same techniques.

—

This event is Hybrid:
JOIN VIA ZOOM – MEETING ID: 873 1557 5255; PASSWORD: 778635

ATTEND IN PERSON – MELBOURNE CONNECT, LEVEL 8, ROOM 8109, 700 SWANSTON STREET, CARLTON
Please arrive a little early to get settled in for a 4pm sharp start.

SEMINAR: WED 04 MARCH 2026 16:00-17:00 (AEDT, Melbourne Time)

Title: Using LLMs to deliver optimisation solutions

Speaker: Christina Burt
Fortescue

Summary:
The goal of our GenAI project is to explore fundamentally different ways to deliver AI projects that are algorithmic in nature. The challenge that we face in the AI program is that highly technical teams require a lot of context in order to effectively build AI solutions. Gathering the right context through a discovery process is time consuming. Defining the right product is challenging. In this talk we will discuss the possibilities of utilising GenAI to automatically generate schedules and to automatically generate algorithms. We will share preliminary results that indicate promising outcomes.

Biography:
Dr Christina Burt leads a team of 6 Optimisation Specialists in Fortescue as a part of the wider AI program. She has developed a specialisation in decomposition algorithms over three postdocs at MASCOS, AIT (Vienna) and the University of Melbourne (Computing Science). She transitioned to industry with a London-based AI startup Satalia, before moving to Water Corporation (Perth) where her optimisation initiatives have enduring impact and are saving millions of dollars each year. She has been with Fortescue for 5 years, where she has developed in production solutions including truck dispatch and scheduling optimisation software.

This event is Hybrid (in-person and Zoom):
15:30 – 16:00 – Join us in-person before the seminar begins for a meet and greet with light afternoon refreshments
**REGISTER for in-person attendance here: https://go.unimelb.edu.au/p3n2
Location for in person: Melbourne Connect, Level 7, Manhari Room, 700 Swanston Street, Carlton, 3053

16:00 – 17:00 – Seminar start in-person and on Zoom

SEMINAR: WED 26 NOVEMBER 2025 15:30 -17:00 (AEDT, Melbourne Time)
ZOOM MEETING ID: 873 1557 5255; PASSWORD: 778635

Title: There are no integers in discrete optimisation

Speaker: Peter Stuckey
OPTIMA/Monash University

Abstract:
Discrete optimisation problems make decisions from a finite set of choices. They encompass many important problem classes such as scheduling, rostering and resource allocation. MiniZinc is a leading modelling language for discrete optimisation. It allows the expression of discrete optimisation problems succinctly using high level global constraints, and automatically translates them to run on constraint programming (CP), mixed integer programming (MIP), Boolean satisfiability (SAT), SAT modulo theories (SMT), and local search solvers. Integers are a key type in MiniZinc since they are used to represent all the finite decisions made during solving.  But in the latest development version of MiniZinc, we recommend never using integers in models. Why? Finding errors in discrete optimisation models can be very challenging. In the worst case when a solver simply returns no answer, we don’t know if this is because the problem we want to ask is too hard (for this solver) or the problem we actually asked (because of errors in the model) is too hard. Looking through solver traces of millions of events to find a problem is very hard work, and indeed there may be no error.  So strong typing is important for modelling languages, the more the compiler discovers modelling errors, the less challenging debugging is required. In this talk I will talk about how we ensure that no (pure) integers appear in MiniZinc models by using enumerated types and type extensions, as well as unit types, to clearly differentiate between the different kinds of integers appearing in a model.

Bio:
Peter Stuckey from Monash University has a world-leading research program in constraint programming. He led the G12 project, one of the largest projects at NICTA, peaking at 25 researchers, culminating in the Opturion spinout company, delivering optimisation solutions to commercial customers. He has been involved in 3 ARC Linkage Projects and other industry contract research projects, working in Energy, Security, Resources and Transport. His research expertise in optimisation broadly covers modelling languages and model transformation, solving using Artificial Intelligence (AI) and Operations Research (OR) technology, and using machine learning methods in concert with optimisation. For the global influence and uptake of his research, he was awarded the 2010 Google Australia Eureka Prize for Innovation in Computer Science and honoured in 2019 with a Fellowship of the prestigious Association for AI Advancement.

More information: research.monash.edu/en/persons/peter-stuckey

JOIN ON ZOOM – MEETING ID: 873 1557 5255; PASSWORD: 778635

WED 19 NOVEMBER 2025 16:00-17:00 (AEDT, Australian Eastern Daylight Savings Time)