Professor Babak Abbasi
Details
College: School of Accounting Information Systems & Supply Chain
Department: Accounting, Info Sys & Supply Chain
Campus: City Campus Australia
babak.abbasi@rmit.edu.au
Open to
- Masters Research or PhD student supervision
- Media enquiries
About
Professor Babak Abbasi is an Operations Research and Operations Management scholar whose research focuses on quantitative modelling, AI, machine learning and data-driven decision-making under uncertainty. His work combines rigorous analytical methods with real-world applications across healthcare, supply chains, service operations, energy and resource allocation.
He is the 2024 recipient of the ASOR Ren Potts Medal, recognising outstanding contributions to Operations Research in Australia. He has also received the RMIT Research Impact Award (Enterprise) and the RMIT Award for Excellence in Industry Engagement in Graduate Research.
Professor Abbasi has published in leading international journals, including FT50 journals, and serves as an Associate Editor of Decision Sciences. His research develops optimisation, stochastic modelling and machine-learning approaches to address complex operational problems, including hospital resource allocation, blood inventory management, home healthcare, supply-chain coordination and emergency response.
His research is strongly industry-oriented and data-driven, with collaborations involving organisations such as Australian Red Cross Lifeblood, KPMG, City of Melbourne, Geoscience Australia, Fonterra and the Florey Institute of Neuroscience and Mental Health.
His current research program focuses on advancing AI-enabled Operations Research and data-driven decision-making, with the aim of improving efficiency, resilience and sustainability in complex service and supply-chain systems.
Supervisor projects
- Project 1: The relevance of data ecosystems for firms transitioning towards a circular business model
- 9 Jan 2026
- Adaptive Blood Supply Chain Management under Compound Disasters: An Approximate Dynamic Programming Approach
- 18 Nov 2025
- Stochastic Decision Models for EV Battery Life Cycle Use to Support Renewable Integration
- 12 Dec 2024
- Optimisation Models for Workforce, Project, and Policy Planning in the Construction Sector
- 3 Jun 2024
- Developing an equitable and efficient supply chain network for post-disaster recovery phase
- 9 Feb 2024
- Blood Supply Chain
- 15 Mar 2023
- Platelet Inventory Management in Hospital Networks
- 29 Jul 2022
- The Dynamics of Human Judgement in Forecasting: Decision-Making in Model Selection and Forecast Combination
- 14 Feb 2022
- Modelling the Factors Affecting Port Time Uncertainty: Evidence from Multilevel Analysis of Global Container Ports
- 14 Jan 2022
- Factors Influencing Customer Loyalty in Mobile Telecommunications Products and Services in Australia
- 21 Feb 2020
- Mathematical Modelling and Solution Approaches to Tackle Challenges in Real-life Routing Problems
- 17 Jul 2019
- Digital Platform Durability: Examining the Impact of Structural Attributes
- 9 Jul 2019
- An Analytical Study on Farmland Allocation in The Conversion From Conventional to Organic Farming
- 24 Sep 2018
- An Empirical Investigation of the Effectiveness of eLearning Strategies in Higher Education: A Rasch model for Saudi Arabia
- 4 Jun 2018
- Optimisation Approaches For an Orienteering Problem with Applications to Wildfire Management.
- 3 May 2018
- Stochastic Optimisation of Bank Balance Sheet under Basel III: Incorporating Credit Contagion Risk
- 9 Apr 2018
- Towards More Sustainable Logistics: Antecedents and Outcomes of Environmental Performance for Transport and Logistics Companies
- 13 Feb 2018
- Modelling the Factors Affecting Urban Residential Fires - A Case Study of Melbourne
- 28 Mar 2017
- Optimal Capacity Decisions of Airlines under Supply-Demand Equilibrium
- 2 Sep 2016
- Scheduling and Staffing of Multiskilling of Workforce in the Context of Off-side Construction
- 27 Jul 2016
- Optimising Financial Performance Measures in a Supply Chain Network Redesign
- 22 Feb 2016
- Transshipment in Supply Chain Networks with Perishable Items
- 2 Mar 2015
- Ensuring Blood is Available when it is Needed Most
- 3 Mar 2014
Teaching interests
Professor Babak Abbasi’s teaching focuses on Business Analytics, Operations Research, Operations Management, Optimisation and Decision Sciences. His teaching integrates quantitative methods, data analytics and real-world business problems to help students develop analytical and evidence-based decision-making skills.
His teaching interests include machine learning and AI for business, optimisation and decision modelling, supply chain analytics, and data-driven resource allocation. He emphasises the practical application of analytical methods to complex decision-making problems across healthcare, supply chains, energy and other business environments.
Research interests
Professor Babak Abbasi’s current research focuses on the intersection of machine learning and optimisation, particularly developing methods to better capture uncertainty and ambiguity in business decision-making.
His research is applied across a range of sectors, including healthcare, agriculture, energy and logistics, addressing complex decision-making and resource allocation challenges.
He is also a member of the National Committee of the Australian Society for Operations Research (ASOR) and served as Co-Chair of the ASOR Scientific Committee for the 2018 ASOR Conference.
Selected Supervised Research Projects
- Machine Learning and Optimisation for Decision-Making — integrating machine learning with optimisation frameworks to improve practical decision-making.
- Data-Driven Robust Optimisation — developing robust optimisation approaches that leverage data to address uncertainty and ambiguity.
- Decision-Making and Resource Allocation in Healthcare — developing data-driven models to support resource allocation and operational decisions in healthcare systems and hospitals.
- Decision-Making in Agribusiness and Agriculture — applying optimisation and analytics to improve resource allocation and operational decision-making.
- Energy Systems Optimisation — developing data-driven approaches to support decision-making and resource allocation in energy systems.
- Blood Supply Chain Improvement — developing optimisation approaches to improve the efficiency, resilience and coordination of blood supply chains.
