The shift from simulation to numerical solving involves several key techniques: Numerical Methods for Solving Discrete Event Systems

An emerging alternative focuses on that solve these systems deterministically, often by reformulating them as Markov chains . This approach offers distinct advantages for smaller systems or scenarios where exact precision is non-negotiable. The Core Problem: Why Numerical Methods?

Discrete Event Simulation (DES) is the industry standard for modeling systems where state changes occur at specific, distinct points in time—like customers arriving at a bank or parts moving through a factory line. While powerful, traditional DES relies on randomness and Monte Carlo methods, which can require thousands of runs to achieve high precision.

Beyond Simulation: Numerical Methods for Discrete Event Systems