Choosing Between System Dynamics and Discrete Event Simulation: Tools and Open‑Source Options

System Dynamics

System Dynamics models the internal feedback mechanisms of complex systems using differential equations. Variables are represented as stocks, flows, and auxiliary variables, and the model solves a set of ordinary differential equations (ODEs) to generate time‑series trajectories. This approach is useful for exploring policy impacts, long‑term trends, and emergent behavior in domains such as economics, ecology, and engineering.

Typical workflow

Define the problem and identify key stocks, flows, and feedback loops.

Translate the causal diagram into a system of ODEs.

Choose a numerical integration method (Euler, Runge‑Kutta, etc.) and a simulation horizon.

Calibrate parameters against historical data and validate the model.

Run scenario analyses and generate plots of variable trajectories.

Open‑source tools

OpenModelica

Description: Open‑source modeling and simulation environment that supports Modelica and System Dynamics constructs.

Key features: Graphical diagram editor, command‑line simulation ( omc), support for continuous and discrete events, and export to FMU for co‑simulation.

Insight Maker

Description: Browser‑based platform for building System Dynamics and agent‑based models.

Key features: Drag‑and‑drop stock‑flow diagramming, cloud storage of models, and built‑in charting for rapid result inspection.

Discrete Event Simulation (DES)

DES represents a system as a chronological sequence of instantaneous events that change the state of resources, queues, and entities. The simulation clock jumps from one event time to the next, making DES efficient for analyzing process flows, resource utilization, and bottlenecks in manufacturing, logistics, and healthcare.

Typical workflow

Identify entities (e.g., parts, patients) and resources (e.g., machines, staff).

Define event types (arrival, start service, end service) and their stochastic distributions (exponential, normal, etc.).

Implement a scheduler that maintains an event list ordered by timestamp.

Run the simulation for a specified horizon or until a stopping condition is met.

Collect performance metrics such as average waiting time, resource utilization, and throughput.

Open‑source tools

SimPy

Description: Python library for process‑oriented DES.

Key features: Generator‑based processes, resource objects (Resource, Container), built‑in statistical collectors, and easy integration with NumPy/Pandas for data analysis.

Example snippet:

import simpy

def patient(env, name, doctor):
    arrive = env.now
    print(f"{name} arrives at {arrive}")
    with doctor.request() as req:
        yield req
        wait = env.now - arrive
        print(f"{name} starts consultation after {wait:.2f} minutes")
        yield env.timeout(15)  # consultation duration

env = simpy.Environment()
doctor = simpy.Resource(env, capacity=1)
for i in range(5):
    env.process(patient(env, f"Patient{i}", doctor))
    env.timeout(5)  # inter‑arrival time
env.run(until=100)

JSim

Description: Open‑source simulation package supporting both DES and continuous system models.

Key features: Java‑based GUI, web interface, ability to import Modelica components, and support for stochastic event scheduling.

These open‑source environments provide flexible, scriptable, and cost‑free alternatives to commercial packages, enabling researchers and practitioners to build, validate, and analyze both continuous feedback systems and event‑driven processes without licensing constraints.