SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations

The article introduces SOCRATES, a novel simulation optimization method that utilizes correlated replicas and adaptive trajectory evaluations to improve the optimization of complex stochastic systems (F1, F3). This approach is proposed to be effective in enhancing simulation outcomes, particularly in fields such as engineering and operations management (F2, F5; A1). Additionally, the article highlights the potential role of large language models in further advancing the optimization process, suggesting that these models can contribute positively to managing complexity in stochastic environments (F4; A2). The integration of SOCRATES with language models underscores a growing trend in leveraging artificial intelligence to address challenges in simulation-based optimization. This development aligns with recent research emphasizing the impact of language models on various computational tasks (connected articles). Overall, SOCRATES represents a promising direction in simulation optimization, combining methodological innovation with AI-driven enhancements to address practical problems in relevant industries.