Simulation OR

The Simulation Special Interest Group (SIG) provides a forum for academics, practitioners and software vendors to discuss the most recent advances in simulation theory and methodologies, to exchange ideas on simulation practice, to promote collaboration among its members, and to engage in impact-related activities.

Since 2002, the group has been running a bi-annual OR Society conference on simulation called the Simulation Workshop. Members of the group also contribute to the Journal of Simulation (ORS journal) in senior editorial roles.

The group organises a number of meetings and one-day workshops on cutting-edge simulation research and topics of relevance to SIG members.

For example, in the summer of 2018, the group facilitated the Simulation SIG Road Trip with Professor Paul Fishwick, which included meetings in seven UK cities.

The group encourages those who are interested in attending or organising future meetings and workshops to join the mailing list to get the conversation going.

Future Events

ORS Annual Conference OR68: From Data to Decisions

8–10 September 2026
University of Nottingham, United Kingdom

 

For those working with simulation, OR68 offers a space to explore how dynamic models are used to understand complex systems and test decisions before they are made. The conference brings together practitioners and researchers to share work across discrete-event, agent-based, and system dynamics simulation, with a focus on insight, validation, and real-world impact.

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Committee Details

Name
Role
Anastasia Anagnostou
Chair
Masoud Fakhimi
Secretary
Sion Cave
Committee Member
Lampros Stergioulas
Committee Member 
Simon Taylor
Committee Member 

Will Machine Learning Make Simulation Dispensable? Why this belief might be a misconception?

Dr Niclas Feldkamp joined the Simulation SIG to discuss whether machine learning is making simulation and modelling obsolete.

While AI and machine learning can analyse and predict complex processes from data, the session highlighted that simulation and machine learning are not competitors but complementary tools. Machine learning enhances data-driven predictions, while simulation provides deeper insights into complex systems. Together, they create a powerful synergy, reinforcing the ongoing relevance of simulation in an AI-driven world.

 
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