×M2. Simulation-based analysis in decision-making context

Real-world problems are often too complex to model and rarely analytically tractable to solve. Simulation modeling is a powerful tool used to alleviate these difficulties by capturing intricate relationships and uncertainties associated with complex systems. Thanks to the increasing amount of computational power and a flood of easily accessible data, simulation models have been coupled with optimization algorithms (e.g., heuristics and metaheuristics), machine learning (e.g., reinforcement learning), and data science (e.g., predictive and prescriptive analytics) methods to enhance capabilities of simulation models and to address challenging decision-making problems.

The goal of this session is to bring recent theoretical and applied developments in simulation modeling (including, Agent-Based, and Discrete Event models) and their intersections with optimization, machine learning, and data science. Papers are invited in, but are not limited to, the following areas:

Key topics: Simulation modelling, Decision making, Simulation-based optimisation, Multi-method modelling