• Discrete-event system modeling matured around generalized semi-Markov processes (GSMP) and regenerative-process theory, enabling rigorous analysis and simulation of complex queuing and networked systems with structured regeneration properties [3], [19], [18], [8], [1].

• Variance reduction and sensitivity estimation techniques improve efficiency and accuracy of stochastic simulations, including likelihood-ratio methods, infinitesimal perturbation analysis and randomization-based approaches for transient or gradient computations [11], [14], [4], [2], [16].

• Spectral and numerical approaches for stochastic process simulation to generate accurate sample paths efficiently, notably spectral representation for Gaussian processes and numeric integration of stochastic differential equations [7], [15].

• Experiment design, startup policies, and run-time management in simulation studies to quantify bias, variance, and truncation effects, enabling reliable estimates under different initialization and run-length assumptions [10], [1], [18].

• Optimization and control of stochastic and regenerative systems leveraging gradient-like estimators and infinitesimal perturbation analysis, including convergence proofs and sensitivity-based optimization [6], [20], [5], [14].