Discrete-Event Simulation (DES)
Also known as: DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.
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When to use it
DES fits whenever a system can be described as discrete entities moving through resources governed by stochastic arrival and service times. It is the appropriate choice for queuing systems, emergency department and hospital ward flow, manufacturing and supply chain throughput analysis, call center staffing, and logistics routing — wherever the analyst needs to evaluate capacity, bottlenecks, or 'what-if' scenarios without experimenting on the real system. No normality assumption is required. The number of replications must be sufficient (typically at least 30, or ≥1000 for narrow confidence intervals) — with too few replications DES results are unreliable and an analytical queuing model may be preferable. Distributional inputs (arrival and service time distributions) must be estimated from historical data; when no data exist, expert elicitation or pilot runs are needed.
Strengths & limitations
- Handles complex, nonlinear processes — resource contention, variable routing, priority queues — that analytical queuing models cannot represent.
- Produces realistic confidence intervals through independent replications, making uncertainty explicit.
- Supports 'what-if' experimentation and scenario comparison without disrupting the real system.
- No distributional assumptions on outputs; any input distribution can be accommodated.
- Applicable across a wide range of domains — healthcare, manufacturing, logistics, service operations — without changing the core paradigm.
- Input uncertainty: results are only as reliable as the fitted arrival and service distributions.
- Warm-up bias: ignoring the warm-up period inflates or deflates steady-state estimates.
- Replications: too few replications (fewer than ~30) produce wide confidence intervals that make scenario comparisons unreliable.
- Computational cost: complex models with many entities and resources can require substantial run time to achieve narrow confidence intervals.
- Model validation requires historical data for verification; poorly mapped processes lead to misleading outputs.
Frequently asked
How many replications do I need?
A minimum of 30 independent replications is a common starting point for preliminary analysis, but narrow confidence intervals for decision-making typically require 100–1000 or more. Use a sequential pilot procedure: run an initial batch of 10–30 replications, estimate the variance, then compute the additional replications needed to achieve your target half-width.
How do I determine the warm-up period?
Welch's graphical method is the most widely used approach: plot a moving average of the performance metric across time, and identify the point at which it stabilizes. Alternatively, use the Mecklenburg-White test or simply run the model long enough that the warm-up represents less than 10% of total run length. Omitting the warm-up biases steady-state estimates.
When should I use DES rather than an analytical queuing model?
Analytical queuing models (M/M/c and variants) give exact or approximate closed-form results for simple, idealized systems. Use DES when the process has complex routing, multiple competing resource types, non-exponential service times, batch arrivals, or time-varying arrival rates — situations where analytical solutions are unavailable or require unrealistic simplifying assumptions.
What if I have no historical data for fitting distributions?
Expert elicitation (e.g., minimum, most-likely, maximum estimates to fit a triangular distribution) is an accepted fallback for preliminary models. Conduct sensitivity analyses across plausible distribution families to understand how input uncertainty propagates to output uncertainty. Collect real data as the model matures.
Sources
- Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127
- Law, A.M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill. ISBN: 978-0073401324
How to cite this page
ScholarGate. (2026, June 1). Discrete-Event Simulation (DES). ScholarGate. https://scholargate.app/en/simulation/discrete-event-simulation
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