Dr Michelle Blom

  • Room: Level: 06 Room: 14
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • AI Algorithms
  • Algorithms for Electoral Analysis
  • Optimisation for Supply Chains

Personal webpage



Dr Michelle Blom is a Research Fellow in the School of Computing and Information Systems at The University of Melbourne. Michelle completed her PhD in the Department of Computer Science and Software Engineering at the University of Melbourne in 2011. Her thesis explored the use of argumentation-based approaches for automated decision-making and planning. Her research involves developing new algorithms and heuristics for planning and scheduling in a range of domains, including: the scheduling of production in large open-pit mining supply chains, across both short- and long-term horizons; and the transport of inventory across complex logistics networks subject to dynamic, and adversarial, disruption. An additional area of interest is the development of algorithms to support the analysis and auditing of election outcomes.

Recent publications

  1. Blom, M.; Pearce, AR.; Stuckey, PJ. Short-term planning for open pit mines: a review. International Journal of Mining, Reclamation and Environment. TAYLOR & FRANCIS LTD. 2019, Vol. 33, Issue 5, pp. 318-339. DOI: 10.1080/17480930.2018.1448248
  2. Blom, M.; Stuckey, PJ.; Teague, VJ. Ballot-Polling Risk Limiting Audits for IRV Elections. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11143 LNCS, pp. 17-34. DOI: 10.1007/978-3-030-00419-4_2
  3. Blom, M.; Stuckey, PJ.; Teague, VJ. Computing the Margin of Victory in Preferential Parliamentary Elections. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11143 LNCS, pp. 1-16. DOI: 10.1007/978-3-030-00419-4_1
  4. Blom, M.; Shekh, S.; Gossink, D.; Miller, T.; Pearce, AR. Inventory routing for defense: Moving supplies in adversarial and partially observable environments. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology. SAGE Publications. 2018, pp. 154851291879805-154851291879805. DOI: 10.1177/1548512918798056
  5. Blom, M.; Pearce, AR.; Stuckey, PJ. Multi-objective short-term production scheduling for open-pit mines: a hierarchical decomposition-based algorithm. Engineering Optimization. TAYLOR & FRANCIS LTD. 2018, Vol. 50, Issue 12, pp. 2143-2160. DOI: 10.1080/0305215X.2018.1429601
  6. Conway, A.; Blom, M.; Naish, L.; Teague, V. An analysis of New South Wales electronic vote counting. Proceedings of the Australasian Computer Science Week Multiconference on - ACSW '17. ACM Press. 2017. DOI: 10.1145/3014812.3014837
  7. Blom, M.; Pearce, AR.; Stuckey, PJ. Short-term scheduling of an open-pit mine with multiple objectives. CONSTRAINT PROGRAMMING 2015 (POSTER). TAYLOR & FRANCIS LTD. 2017, Vol. 49, Issue 5, pp. 777-795. DOI: 10.1080/0305215X.2016.1218002
  8. Blom, M.; Pearce, A.; Stuckey, P. A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks over Multiple Time Periods. Management Science. INFORMS (Institute for Operations Research and Management Sciences). 2016, Vol. 62, Issue 10, pp. 3059-3084. DOI: 10.1287/mnsc.2015.2284
  9. Blom, M.; Stuckey, PJ.; Teague, V.; Tidhar, R. Efficient Computation of Exact IRV Margins. Frontiers in Artificial Intelligence and Applications. IOS Press. 2016, Vol. 285, pp. 480-488. DOI: 10.3233/978-1-61499-672-9-480
  10. Blom, M.; Burt, C.; Lipovetzky, N.; Pearce, A.; Stuckey, P. Scheduling Tools for Open-Pit Mining Operations. . 2015.
  11. Blom, ML.; Burt, CN.; Pearce, AR.; Stuckey, PJ. A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines. INFORMS Journal on Computing. INFORMS. 2014, Vol. 26, Issue 4, pp. 658-676. DOI: 10.1287/ijoc.2013.0590
  12. Blom, M. Arguments and Actions: Decoupling Preference and Planning through Argumentation. . 2011.
  13. Blom, ML.; Pearce, AR. Relaxing Regression for a Heuristic GOLOG. Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the 5th Starting AI Researchers' Symposium. IOS PRESS. 2011, Vol. 222, pp. 37-49. DOI: 10.3233/978-1-60750-676-8-37
  14. Blom, ML.; Pearce, AR. An Argumentation-Based Interpreter for Golog Programs. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09). IJCAI-INT JOINT CONF ARTIF INTELL. 2009, pp. 690-695.
  15. Blom, M. An Argumentative Knowledge-Based Model Construction Approach for Bayesian Networks. . 6th European Workshop on Multi-Agent Systems (EUMAS-2008). 2008.
  16. Blom, M. Optimising the Interpretation of Golog Programs with Argumentation. . 6TH EUROPEAN WORKSHOP IN MULTI-AGENT SYSTEMS (EUMAS 2008). 2008.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile