| Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion M Abolghasemi, E Beh, G Tarr, R Gerlach Computers & Industrial Engineering 142, 106380, 2020 | 132 | 2020 |
| An integrated scenario-based robust planning approach for foresight and strategic management with application to energy industry R Alizadeh, PD Lund, A Beynaghi, M Abolghasemi, R Maknoon Technological Forecasting and Social Change 104, 162-171, 2016 | 128 | 2016 |
| A new approach for supply chain risk management: Mapping SCOR into Bayesian network M Abolghasemi, V Khodakarami, H Tehranifard Journal of Industrial Engineering and Management (JIEM) 8 (1), 280-302, 2015 | 71 | 2015 |
| Demand forecasting in the presence of systematic events: Cases in capturing sales promotions M Abolghasemi, J Hurley, A Eshragh, B Fahimnia International Journal of Production Economics 230, 107892, 2020 | 64 | 2020 |
| Hierarchical forecast reconciliation with machine learning E Spiliotis, M Abolghasemi, RJ Hyndman, F Petropoulos, ... Applied Soft Computing 112, 107756, 2021 | 48 | 2021 |
| Machine learning applications in time series hierarchical forecasting M Abolghasemi, RJ Hyndman, G Tarr, C Bergmeir arXiv preprint arXiv:1912.00370, 2019 | 27 | 2019 |
| Model selection in reconciling hierarchical time series M Abolghasemi, RJ Hyndman, E Spiliotis, C Bergmeir Machine Learning, 1-51, 2022 | 19 | 2022 |
| Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions M Abolghasemi, G Tarr, C Bergmeir International Journal of Forecasting, 2022 | 11 | 2022 |
| State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd technical challenge M Abolghasemi, R Esmaeilbeigi arXiv preprint arXiv:2112.03595, 2021 | 11 | 2021 |
| How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function M Abolghasemi, B Abbasi, T Babaei, Z HosseiniFard arXiv preprint arXiv:2105.06618, 2021 | 9 | 2021 |
| Comparison and evaluation of methods for a predict+ optimize problem in renewable energy C Bergmeir, F de Nijs, A Sriramulu, M Abolghasemi, R Bean, J Betts, ... arXiv preprint arXiv:2212.10723, 2022 | 5 | 2022 |
| How to predict and optimise with asymmetric error metrics M Abolghasemi, R Bean arXiv preprint arXiv:2211.13586, 2022 | 5 | 2022 |
| Considering pricing and uncertainty in designing a reverse logistics network M Zamani, M Abolghasemi, SMS Hosseini, MS Pishvaee International Journal of Industrial and Systems Engineering 35 (2), 158-182, 2020 | 5 | 2020 |
| A Bayesian framework for strategic management in the energy industry M Abolghasemi, R Alizadeh International Journal of Science, Engineering and Technology 3 (11), 1360-1366, 2014 | 5 | 2014 |
| Humans vs large language models: Judgmental forecasting in an era of advanced AI M Abolghasemi, O Ganbold, K Rotaru arXiv preprint arXiv:2312.06941, 2023 | 3 | 2023 |
| The intersection of machine learning with forecasting and optimisation: theory and applications M Abolghasemi Forecasting with Artificial Intelligence: Theory and Applications, 313-339, 2023 | 3 | 2023 |
| Machine learning for satisficing operational decision making: A case study in blood supply chain M Abolghasemi, B Abbasi, Z HosseiniFard International Journal of Forecasting, 2023 | 3 | 2023 |
| The value of point of sales information in upstream supply chain forecasting: an empirical investigation M Abolghasemi, B Rostami-Tabar, A Syntetos International Journal of Production Research 61 (7), 2162-2177, 2023 | 3 | 2023 |
| Measuring downstream supply chain performance using Bayesian Networks. M Abolghasemi, V Khodakarami, H Tehranifard CIE44 & IMSS’14 Proceedings, 2195-2209, 2014 | 1* | 2014 |
| Approximating Solutions to the Knapsack Problem Using the Lagrangian Dual Framework M Keegan, M Abolghasemi Australasian Joint Conference on Artificial Intelligence 14471, 455–467, 2023 | | 2023 |
Garth TarrAssociate Professor, University of Sydney, AustraliaVerified email at sydney.edu.au
