| A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis B Fahimnia, J Sarkis, A Eshragh Omega 54, 173-190, 2015 | 315 | 2015 |
| Tactical supply chain planning under a carbon tax policy scheme: A case study B Fahimnia, J Sarkis, A Choudhary, A Eshragh International Journal of Production Economics 164, 206-215, 2015 | 209 | 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 | 99 | 2020 |
| Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms B Fahimnia, H Davarzani, A Eshragh Computers & Operations Research 89, 241-252, 2018 | 83 | 2018 |
| Average-reward model-free reinforcement learning: a systematic review and literature mapping V Dewanto, G Dunn, A Eshragh, M Gallagher, F Roosta arXiv preprint arXiv:2010.08920, 2020 | 42 | 2020 |
| A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem A Eshragh, JA Filar, M Haythorpe Annals of Operations Research 189 (1), 103-125, 2011 | 33 | 2011 |
| Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis A Eshragh, S Alizamir, P Howley, E Stojanovski PLoS ONE 15 (10), e0240153, 2020 | 32 | 2020 |
| A projection-adapted cross entropy (PACE) method for transmission network planning A Eshragh, J Filar, A Nazar Energy Systems 2 (2), 189-208, 2011 | 32 | 2011 |
| The Importance of Environmental Factors in Forecasting Australian Power Demand A Eshragh, B Ganim, T Perkins, K Bandara Environmental Modeling & Assessment, 2021 | 23 | 2021 |
| LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data A Eshragh, F Roosta, A Nazari, MW Mahoney Journal of Machine Learning Research, 2022 | 22 | 2022 |
| A new approach to distribution fitting: decision on beliefs A Eshragh, M MODARES Journal of Industrial and Systems Engineering 3 (1), 56-71, 2009 | 17 | 2009 |
| On transition matrices of Markov chains corresponding to Hamiltonian cycles K Avrachenkov, A Eshragh, JA Filar Annals of Operations Research 243 (1), 19-35, 2016 | 16 | 2016 |
| Hamiltonian cycles, random walks, and discounted occupational measures A Eshragh, J Filar Mathematics of Operations Research 36 (2), 258-270, 2011 | 16 | 2011 |
| A new approach to select the best subset of predictors in linear regression modelling: bi-objective mixed integer linear programming H Charkhgard, A Eshragh The ANZIAM Journal 61 (1), 64-75, 2019 | 11 | 2019 |
| Deep reinforcement learning for dynamic order picking in warehouse operations S Mahmoudinazlou, A Sobhanan, H Charkhgard, A Eshragh, G Dunn Computers & Operations Research, 107112, 2025 | 10 | 2025 |
| A hybrid statistical-machine learning approach for analysing online customer behavior: An empirical study S Alizamir, K Bandara, A Eshragh, F Iravani arXiv preprint arXiv:2212.02255, 2022 | 9 | 2022 |
| Uniform Fractional Part: A simple fast method for generating continuous random variates H Mahlooji, A Eshragh Jahromi, H Abouee Mehrizi, N Izady Scientia Iranica 15 (5), 613-622, 2008 | 9 | 2008 |
| Hamiltonian cycles and subsets of discounted occupational measures A Eshragh, JA Filar, T Kalinowski, S Mohammadian Mathematics of Operations Research, 2019 | 8 | 2019 |
| An analytical bound on the fleet size in vehicle routing problems: a dynamic programming approach A Eshragh, R Esmaeilbeigi, R Middleton Operations Research Letters, 2020 | 7 | 2020 |
| Fisher Information for a partially observable simple birth process NG Bean, A Eshragh, JV Ross Communications in Statistics-Theory and Methods 45 (24), 7161-7183, 2016 | 5 | 2016 |
Prof Behnam (Ben) FahimniaProfessor and Chair, University of SydneyVerified email at sydney.edu.au
