Discrete Event Simulation (DES) models systems as sequences of events, such as arrivals, delays, or service completions. It is widely used in manufacturing, logistics, healthcare, and service industries to optimise processes and reduce bottlenecks.
Unlike continuous simulations, DES jumps directly between events, making it efficient and practical. With multi-method modeling, DES can be combined with agent-based and system dynamics approaches, enabling powerful hybrid simulations for deeper business insights.
Definition
Discrete Event Simulation (DES) represents systems as a chain of discrete events (e.g., arrivals, departures, service completions).
Mechanism
The simulation clock jumps from one event to the next, updating the system state only when something happens.
Efficiency
Idle periods are skipped, making DES computationally efficient compared to continuous simulations.
Applications
Manufacturing
Production line optimisation, machine breakdowns, and repair scheduling.
Logistics & Supply Chains
Warehouse operations, transport delays, and inventory flows.
Service Systems
Customer wait times in banks, airports, or retail stores.
Healthcare
Patient flow in hospitals and emergency rooms.
Advantages
- Decision Support: Test “what-if” scenarios before implementation.
- Flexibility: Adaptable to diverse industries and processes.
- Bottleneck Detection: Identifies inefficiencies like long queues or resource shortages.
- Hybrid Potential: With AnyLogic, DES can be combined with other paradigms for more realistic models.
Limitations
- Complexity: Requires detailed data and careful design.
- Computation: Large-scale models may be resource-intensive.
- Assumptions: Accuracy depends on realistic event timing and distributions.
Software
There are many different software packages in this category and I have listed the main ones we are familiar with. Our experience is mainly using AnyLogic.
| Software | Publisher | Website | Key Features/Notes |
|---|---|---|---|
| AnyLogic | The AnyLogic Company | anylogic.com | Multimethod (DES, agent-based, system dynamics); strong visualisation; hybrid modeling |
| Arena | Rockwell Automation | rockwellautomation.com/arena-simulation | Longstanding DES tool; strong in manufacturing and logistics |
| FlexSim | FlexSim Software Products | flexsim.com | 3D modeling with drag-and-drop interface; intuitive visualisation |
| Simio | Simio LLC | simio.com | Object-oriented modeling; supports DES and agent-based approaches |
| SIMUL8 | SIMUL8 Corporation | simul8.com | User-friendly, object-based simulation; popular in healthcare and services |
| ExtendSim | Andritz | extendsim.com | General-purpose simulation; supports DES, discrete rate, reliability modeling |
| WITNESS | Haskoning | www.haskoning.com/en/twinn/products/witness | Strong in manufacturing and industrial applications |
| Plant Simulation | Siemens PLM Software | plm.sw.siemens.com/en-US/tecnomatix/plant-simulation-software/ | Focused on production systems and logistics optimisation |
| ProModel | ProModel Corporation | promodel.com | Widely used in manufacturing, logistics, healthcare |
| SimEvents | MathWorks | mathworks.com/simevents | Adds DES capabilities to MATLAB/Simulink |
| GoldSim | GoldSim Technology Group | goldsim.com | combines DES with Monte Carlo simulation |
| Deswik | Deswik | deswik.com | Bespoke DES tool for mining; models sub-level caving, operational planning, and training |
| JaamSim | Open-source (community-driven) | jaamsim.com | Free, Java-based DES tool; supports 3D visualisation; good for teaching and prototyping |
| SimPy | Open-source (Python library) | simpy.readthedocs.io | Lightweight DES framework; integrates with Python ecosystem; ideal for research and custom workflows |
| SimJulia | Open-source (Julia library) | simjuliajl.readthedocs.io | DES framework in Julia; high performance; suited for academic and scientific modeling |