This special issue reports innovative simulation techniques addressing stochasticity in engineering and digital systems, highlighting new AI approaches.
The increasing complexity and uncertainty in engineering, manufacturing, supply chain, and digital systems call for advanced simulation and artificial intelligence (AI) methodologies to support robust decision-making. This special issue presents 10 studies that demonstrate innovative approaches for addressing stochasticity, high-dimensional interdependencies, and dynamic environments. The featured works showcase advances in AI-enhanced simulation, data-driven optimization, multi-agent coordination, and risk-aware decision frameworks. Collectively, they emphasize computational efficiency and practical applicability, while pointing toward emerging research directions such as interpretable AI, scalable distributed systems, and real-time digital twins.
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Li et al. (2025) studied this question.
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