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Dr Stephan Onggo

Associate Professor in Data Analytics

Room 4.14 Aras an Phiarsaigh
stephan.onggo@tcd.ie
01 896 2936


Stephan Onggo is an Associate Professor in Data Analytics and a Senior Fellow of the Higher Education Academy. His teaching is in data analytics. His research interests lie in the areas of predictive analytics using simulation (data-driven simulation, hybrid modelling, agent-based simulation, discrete-event simulation) with applications in operations and supply chain management (e.g. healthcare, agriculture, manufacturing) and social dynamics (e.g. perception diffusion).

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He is the associate editor of the Journal of Simulation and the secretary for the Operational Research Society’s Simulation Special Interest Group. He has published in journals in analytics such as European Journal of Operational Research, Journal of Operational Research Society and Journal of Simulation. He is also interested in working in collaborating with the industry. His past collaborators include the European Commissions, Greater Manchester Police and the UK National Nuclear Lab. He supervises several student projects in collaboration with companies such as Rolls-Royce, Jaguar Land Rover, Ford Motor, BAE Systems, UK National Nuclear Lab, and Liverpool School of Tropical Medicine.

Stephan works with industrial partners in several projects. Stephan's past collaborators include the European Commissions, Greater Manchester Police and the UK National Nuclear Lab. He supervises several student projects in collaboration with companies such as Rolls-Royce, Jaguar Land Rover, FORD Motor, BAE Systems, UK National Nuclear Lab, and Liverpool School of Tropical Medicine. He is a member of the Operational Research Society and has served as chair in several conferences. He has served as a regular PC member of the Winter Simulation Conference which is the main international conference in his field. Currently, he is the secretary for the Operational Research Society’s Simulation Special Interest Group.

    Stephan's research interests lie in the areas of predictive analytics using simulation (data-driven simulation, hybrid modelling, agent-based simulation, discrete-event simulation) with applications in operations and supply chain management (e.g. healthcare, agriculture, manufacturing) and social dynamics (e.g. perception diffusion).

  • Simulation Methods
  • Simulation applications in operations and supply chain management

 

1. Applications of agent-based modelling and simulation in the agri-food supply chains
Utomo, S.U., Onggo, B.S.S., Eldridge, S. (2017). Applications of agent-based modelling and simulation in the agri-food supply chains, European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2017.10.041

2. A BPMN Extension to Support Discrete-Event Simulation for Healthcare Applications: An Explicit Representation of Queues, Attributes and Data-Driven Decision Points
Onggo, B.S.S., Proudlove, N. C., D’Ambrogio, A., Calabrese, A. and Bisogno, S. (2017). A BPMN Extension to Support Discrete-Event Simulation for Healthcare Applications: An Explicit Representation of Queues, Attributes and Data-Driven Decision Points, Journal of the Operational Research Society, https://doi.org/10.1057/s41274-017-0267-7

3. Towards fully-facilitated discrete event simulation modelling: Addressing the model coding stage
Proudlove, N.C., Bisogno, S., Onggo, B.S.S., Calabrese, A. and Levialdi-Ghiron, N.(2017). Towards fully-facilitated discrete event simulation modelling: Addressing the model coding stage, European Journal of Operational Research , 263(2):583-595

 

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1. Modeling Social Risk Amplification during Recall Crises
Liu, Y., Busby, J. and Onggo, B.S.S. (2017). Modeling Social Risk Amplification during Recall Crises, Proceedings of the Spring Simulation Conference.  23-26 April 2017, Virginia Beach, VA, The Society for Modeling & Simulation International, pp. 37-48.

2. Social Amplification of Risk Framework: An Agent-Based Approach
Onggo, B.S.S. (2017). In Jager W., Verbrugge R., Flache A., de Roo G., Hoogduin L., Hemelrijk C. (eds) Advances in Social Simulation 2015. Advances in Intelligent Systems and Computing vol 528. Springer, Cham

3. Hybrid Simulation: Historical Lessons, Present Challenges and Futures
Eldabi, T., Balaban, M., Brailsford, S., Mustafee, N., Nance, R., Onggo, B.S.S., and Sargent, R.(2016) . Hybrid Simulation: Historical Lessons, Present Challenges and Futures, Proceedings of the 2016 Winter Simulation Conference, 11-14 December 2016, Washington, DC (WSC16), edited by T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick Los Alamitos, California: IEEE Computer Society Press, pp. 1388-1403

  • BU7032 Data Analytics
  • BU7278 Digital Analytics