Babak Abbasi

Professor of Business Analytics,
AISSC Deputy Dean (Research and Innovation)

Professor Babak Abbasi‘s research focuses on industry‐motivated quantitative modeling and decision making under uncertainty applied to health care delivery improvement, supply chain coordination, resources allocation, service operations management, and manufacturing.

He was the recipient of RMIT Research Impact Award (Enterprise) in 2016 and RMIT Award for Excellence – Industry Engagement in Graduate Research in 2020.

Professor Babak’s teaching and research is in the area of Business Analytics, Decision Sciences, Operations Research, Machine Learning, Operations Management and Optimisation.

His research investigates leveraging the broader context in mathematical modelling, machine learning and stochastic optimisation to improve decisions making in the businesses and not-for-profit organisations. He has advanced optimisation models and solution algorithms for practical problems such as inventory decisions in blood management, resource allocation in hospital including intensive care units (ICUs) and emergency departments, inventory transshipment decisions, resource allocation for emergency responses, allocation and scheduling of caregivers for home healthcare systems, and donor communication decisions in charities and not-for-profit organisations.

Professor Babak’s research has been published in reputable journals such as Decision Sciences, European Journal of Operational Research and INFORMS Journal of Applied Analytics. He is currently an associate editor for Decision Sciences journal.

Professor Babak has been involved in the software development including:

  • SPSA-FSR (spFSR package in R): Feature Selection and Ranking by Simultaneous Pertur-bation Stochastic Approximation. Developed by: Vural Aksakalli, Babak Abbasi, Yong KaiWong, Zeren D. Yenice- Date 2018 – It had over 8600 downloads till Sept 2020.
  • Teaching Game – Empty Container Relocation Problem. Developed by Babak Abbasi and Jaehyun Shin.

The nature of Professor Babak’s research is industry‐motivated and data-driven. He has worked with several industry partners including Australian Red Cross Blood Life, KPMG, The City of Melbourne, Geoscience Australia, Fonterra, and The Florey Institute of Neuroscience and Mental Health.

Professor Babak’s current research focus is intersection of machine learning and optimisation to better capture uncertainty and ambiguity in business decision-making.

He has strong research collaboration networks and has been working with academics form other institutions including Yale University, University of Washington, Cornell University, Hong Kong Polytechnic University, University of Melbourne, University of Barcelona, University of Virginia and Michigan State University. He is a member the national committee for the Australian Society for Operations Research’s and was the co-chair of the ASOR scientific committee of the Australian Society of Operations Research 2018 conference.

Selected publications

  • Jahani, H., Abbasi, B., Sheu, J. B., & Klibi, W. (2024). Supply chain network design with financial considerations: A comprehensive review. European Journal of Operational Research, 312(3), 799-839.
  • Nasirian, A., Zhang, L., Costa, A., & Abbasi, B. (2023). Multiskilled Workforce Staffing and Scheduling: A Logic-Based Benders’ Decomposition Approach. Available at SSRN 4558349.
  • Jahantab, M., Abbasi, B., & Le Bodic, P. (2023). Farmland allocation in the conversion from conventional to organic farming. European Journal of Operational Research, 311(3), 1103-1119.
  • Abolghasemi, M., Abbasi, B., & HosseiniFard, Z. (2023). Machine learning for satisficing operational decision making: A case study in blood supply chain. International Journal of Forecasting.
  • Nikzad, E., Bashiri, M., & Abbasi, B. (2023). Home healthcare staff dimensioning problem for temporary caregivers: A matheuristic solution approach. Computers & Operations Research, 152, 106126.
  • Bruzda, J., Wojtasik, J., & Abbasi, B. (2023). Forecast Evaluation and Risk Management Strategies in Base Stock Inventory Systems with Fill Rate Commitments. Available at SSRN 4348800.
  • Layaoen, H. D. Z., Abareshi, A., Abdulrahman, M. D. A., & Abbasi, B. (2023). Sustainability of transport and logistics companies: an empirical evidence from a developing country. International Journal of Operations & Production Management, 43(7), 1040-1067.
  • Abbasi, B., Bruzda, J., & Gavirneni, N. (2022). Optimal operational service levels in vendor managed inventory contracts-an exact approach. Operations Research Letters, 50(5), 610-617.
  • Pahlevani, D., Abbasi, B., Hearne, J. W., & Eberhard, A. (2022). A cluster-based algorithm for home health care planning: A case study in Australia. Transportation Research Part E: Logistics and Transportation Review, 166, 102878.
  • Babagolzadeh, M., Zhang, Y., Abbasi, B., Shrestha, A., & Zhang, A. (2022). Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem. Transport Policy, 128, 38-51.
  • Akman, D. V., Malekipirbazari, M., Yenice, Z. D., Yeo, A., Adhikari, N., Wong, Y. K., … & Gumus, A. T. (2023). k-best feature selection and ranking via stochastic approximation. Expert Systems with Applications, 213, 118864.
  • Fadaki, M., Abbasi, B., & Chhetri, P. (2022). Quantum game approach for capacity allocation decisions under strategic reasoning. Computational Management Science, 19(3), 491-512.

Supervision

  • Intersection of Machine Learning in Optimisation for Decision Making (improving practical decision making by embedding machine learning in optimisation framework)
  • Enhancing the data-driven robust optimisation
  • Blood Supply Chain Improvement