Simuleringsmetoderne

Simuleringsmetoderne are a family of computational techniques used to imitate the behavior of complex systems by building mathematical and logical models. These models describe states, relationships, and processes, and are executed in computer simulations that progress through time, either in discrete steps, at defined events, or in continuous evolution. The goal is to observe system performance, test scenarios, and estimate outcomes when real-world experimentation may be impractical or risky.

Key approaches include deterministic simulation, where outcomes are fixed for given inputs, and stochastic simulation, which incorporates randomness. The major categories commonly studied are discrete-event simulation (DES), agent-based simulation (ABS), and system dynamics (SD). Monte Carlo methods are used to approximate uncertain quantities through repeated random sampling. Additional techniques, such as numerical methods for differential equations (e.g., finite difference, finite element), are applied to physical systems described by continuous dynamics.

A typical simulation workflow comprises model construction, verification (ensuring correct implementation), validation (assessing agreement with real data), and experimentation. Sensitivity analysis and uncertainty quantification help researchers understand how results depend on inputs and assumptions. Calibration may be performed to align the model with observed behavior.

Applications span engineering, manufacturing, logistics and supply chains, finance, health care, energy, and environmental planning. Advantages of simulation include the ability to explore unsafe or costly scenarios, conduct what-if analyses, and support decision-making. Limitations involve dependence on model assumptions and data quality, potential computational demands, and the need for careful interpretation of results.