School of Mathematics and Physics

Dr Mahdi Abolghasemi

Lecturer
Mathematics
Priestley Building (67), Room 555
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Researcher biography

Dr. Abolghasemi has a multi-disciplinary background in Engineering, Business, Statistics, and Machine Learning. He is passionate about higher education and has studied and worked on three continents. His research interests and expertise lie in time series forecasting, predictive analytics, decision making and machine learning, with applications in supply chain management and renewable energies optimisation. Dr. Abolghasemi has consulted dozens of companies and developed forecasting and optimisation models for companies such as Sanitarium, Coca Cola, CSR, AEMO, Waterloo Wind Farm, Urban Utility, and Australian Renewable Energy Agency(ARENA). Mahdi's research is closely tied to evidence-based analytics for real-world problems.

Mahdi has published over 40 articles in top-tier academic journals and presented at numerous national and international conferences. He serves on the editorial board of the International Journal of Forecasting and is a regular reviewer for the International Journal of Production Economics, the International Journal of Production Research, and several international conferences. Mahdi is a member of national and international professional societies such as the International Institute of Forecasters, the Australian Mathematical Society, and INFORMS. He is also the founding chair and host of an international scientific podcast, "Forecasting Impact," which reaches audiences in over 120 countries around the world.

Featured projects Duration
Predicting and then optimising decisions via deep learning
Forecasting and optimising decisions via Bayesian optimisation
Time Series Hierarchical Forecasting with integer programming
Predicting cyber-attacks using Machine learning

Publications

Book Chapter (1)
Journal Articles (12)
Conference Papers (3)

Book Chapter

Abolghasemi, Mahdi (2023). The intersection of machine learning with forecasting and optimisation: theory and applications. Palgrave Advances in the Economics of Innovation and Technology. (pp. 313-339) Cham, Switzerland: Springer. doi: 10.1007/978-3-031-35879-1_12

Journal Articles

Abolghasemi, Mahdi (2023). Book review. International Journal of Forecasting, 39 (4), 1949-1951. doi: 10.1016/j.ijforecast.2022.12.002
Abolghasemi, Mahdi, Abbasi, Babak and HosseiniFard, Zahra (2023). Machine learning for satisficing operational decision making: a case study in blood supply chain. International Journal of Forecasting. doi: 10.1016/j.ijforecast.2023.05.004
Abolghasemi, Mahdi, Tarr, Garth and Bergmeir, Christoph (2022). Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting, 40 (2), 597-615. doi: 10.1016/j.ijforecast.2022.07.004
Abolghasemi, Mahdi, Rostami-Tabar, Bahman and Syntetos, Aris (2022). The value of point of sales information in upstream supply chain forecasting: an empirical investigation. International Journal of Production Research, 61 (7), 1-16. doi: 10.1080/00207543.2022.2063086
Abolghasemi, Mahdi, Hyndman, Rob J., Spiliotis, Evangelos and Bergmeir, Christoph (2022). Model selection in reconciling hierarchical time series. Machine Learning, 111 (2), 739-789. doi: 10.1007/s10994-021-06126-z
Spiliotis, Evangelos, Abolghasemi, Mahdi, Hyndman, Rob J., Petropoulos, Fotios and Assimakopoulos, Vassilios (2021). Hierarchical forecast reconciliation with machine learning. Applied Soft Computing, 112 107756, 107756. doi: 10.1016/j.asoc.2021.107756
Abolghasemi, Mahdi, Hurley, Jason, Eshragh, Ali and Fahimnia, Behnam (2020). Demand forecasting in the presence of systematic events: cases in capturing sales promotions. International Journal of Production Economics, 230. doi: 10.1016/j.ijpe.2020.107892
Zamani, Mohsen, Abolghasemi, Mahdi, Hosseini, Seyed Mohammad Seyed and Pishvaee, Mir Saman (2020). Considering pricing and uncertainty in designing a reverse logistics network. International Journal of Industrial and Systems Engineering, 35 (2), 158-182. doi: 10.1504/IJISE.2020.107554
Abolghasemi, Mahdi, Beh, Eric, Tarr, Garth and Gerlach, Richard (2020). Demand forecasting in supply chain: the impact of demand volatility in the presence of promotion. Computers & Industrial Engineering, 142 106380, 106380. doi: 10.1016/j.cie.2020.106380
Alizadeh, Reza, Lund, Peter D., Beynaghi, Ali, Abolghasemi, Mandi and Maknoon, Reza (2016). An integrated scenario-based robust planning approach for foresight and strategic management with application to energy industry. Technological Forecasting and Social Change, 104, 162-171. doi: 10.1016/j.techfore.2015.11.030
Abolghasemi, Mahdi, Khodakarami, Vahid and Tehranifard, Hamid (2015). A new approach for supply chain risk management: mapping SCOR into Bayesian network. Journal of Industrial Engineering and Management, 8 (1), 280-302. doi: 10.3926/jiem.1281
Abolghasemi, Mahdi and Alizadeh, Reza (2014). A Bayesian framework for strategic management in energy industry. International Journal of Science, Engineering and Technology, 3 (11), 1360-1366.

Conference Papers

Keegan, Mitchell and Abolghasemi, Mahdi (2023). Approximating solutions to the Knapsack Problem using the Lagrangian Dual Framework. 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, 28 November - 1 December 2023. Singapore, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8388-9_37
Hamza, M., Abolghasemi, M. and Alvandi, A.O. (2021). Forecasting sales with Bayesian networks: a case study of a supermarket product in the presence of promotions. Modeling and Simulation Conference (MODSIM), Sydney, Australia, 5-10 December 2021. Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2021.M9.hamza
Abolghasemi, Mahdi and Khodakarami, Vahid (2014). Measuring down stream supply chain performance using Bayesian network. 44th International Conference of Computer and Industrial Engineering, Istanbul, Turkey, 14-16 October 2014. Los Angeles, CA, United States: Computers and Industrial Engineering.

Areas of research

Statistics, probability and data science