| name | simpy |
| description | Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time. |
| license | MIT license |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
SimPy - Discrete-Event Simulation
Overview
SimPy is a process-based discrete-event simulation framework based on standard Python. Use SimPy to model systems where entities (customers, vehicles, packets, etc.) interact with each other and compete for shared resources (servers, machines, bandwidth, etc.) over time.
Core capabilities:
- Process modeling using Python generator functions
- Shared resource management (servers, containers, stores)
- Event-driven scheduling and synchronization
- Real-time simulations synchronized with wall-clock time
- Comprehensive monitoring and data collection
When to Use This Skill
Use the SimPy skill when:
- Modeling discrete-event systems - Systems where events occur at irregular intervals
- Resource contention - Entities compete for limited resources (servers, machines, staff)
- Queue analysis - Studying waiting lines, service times, and throughput
- Process optimization - Analyzing manufacturing, logistics, or service processes
- Network simulation - Packet routing, bandwidth allocation, latency analysis
- Capacity planning - Determining optimal resource levels for desired performance
- System validation - Testing system behavior before implementation
Not suitable for:
- Continuous simulations with fixed time steps (consider SciPy ODE solvers)
- Independent processes without resource sharing
- Pure mathematical optimization (consider SciPy optimize)
Quick Start
Basic Simulation Structure
import simpy
def process(env, name):
"""A simple process that waits and prints."""
print(f'{name} starting at {env.now}')
yield env.timeout(5)
print(f'{name} finishing at {env.now}')
env = simpy.Environment()
env.process(process(env, 'Process 1'))
env.process(process(env, 'Process 2'))
env.run(until=10)
Resource Usage Pattern
import simpy
def customer(env, name, resource):
"""Customer requests resource, uses it, then releases."""
with resource.request() as req:
yield req
print(f'{name} got resource at {env.now}')
yield env.timeout(3)
print(f'{name} released resource at {env.now}')
env = simpy.Environment()
server = simpy.Resource(env, capacity=1)
env.process(customer(env, 'Customer 1', server))
env.process(customer(env, 'Customer 2', server))
env.run()
Core Concepts
1. Environment
The simulation environment manages time and schedules events.
import simpy
env = simpy.Environment(initial_time=0)
import simpy.rt
env_rt = simpy.rt.RealtimeEnvironment(factor=1.0)
env.run(until=100)
env.run()
2. Processes
Processes are defined using Python generator functions (functions with yield statements).
def my_process(env, param1, param2):
"""Process that yields events to pause execution."""
print(f'Starting at {env.now}')
yield env.timeout(5)
print(f'Resumed at {env.now}')
yield env.timeout(3)
print(f'Done at {env.now}')
return 'result'
env.process(my_process(env, 'value1', 'value2'))
3. Events
Events are the fundamental mechanism for process synchronization. Processes yield events and resume when those events are triggered.
Common event types:
env.timeout(delay) - Wait for time to passresource.request() - Request a resourceenv.event() - Create a custom eventenv.process(func()) - Process as an eventevent1 & event2 - Wait for all events (AllOf)event1 | event2 - Wait for any event (AnyOf)
Resources
SimPy provides several resource types for different scenarios. For comprehensive details, see references/resources.md.
Resource Types Summary
| Resource Type | Use Case |
|---|
| Resource | Limited capacity (servers, machines) |
| PriorityResource | Priority-based queuing |
| PreemptiveResource | High-priority can interrupt low-priority |
| Container | Bulk materials (fuel, water) |
| Store | Python object storage (FIFO) |
| FilterStore | Selective item retrieval |
| PriorityStore | Priority-ordered items |
Quick Reference
import simpy
env = simpy.Environment()
resource = simpy.Resource(env, capacity=2)
priority_resource = simpy.PriorityResource(env, capacity=1)
fuel_tank = simpy.Container(env, capacity=100, init=50)
warehouse = simpy.Store(env, capacity=10)
Common Simulation Patterns
Pattern 1: Customer-Server Queue
import simpy
import random
def customer(env, name, server):
arrival = env.now
with server.request() as req:
yield req
wait = env.now - arrival
print(f'{name} waited {wait:.2f}, served at {env.now}')
yield env.timeout(random.uniform(2, 4))
def customer_generator(env, server):
i = 0
while True:
yield env.timeout(random.uniform(1, 3))
i += 1
env.process(customer(env, f'Customer {i}', server))
env = simpy.Environment()
server = simpy.Resource(env, capacity=2)
env.process(customer_generator(env, server))
env.run(until=20)
Pattern 2: Producer-Consumer
import simpy
def producer(env, store):
item_id = 0
while True:
yield env.timeout(2)
item = f'Item {item_id}'
yield store.put(item)
print(f'Produced {item} at {env.now}')
item_id += 1
def consumer(env, store):
while True:
item = yield store.get()
print(f'Consumed {item} at {env.now}')
yield env.timeout(3)
env = simpy.Environment()
store = simpy.Store(env, capacity=10)
env.process(producer(env, store))
env.process(consumer(env, store))
env.run(until=20)
Pattern 3: Parallel Task Execution
import simpy
def task(env, name, duration):
print(f'{name} starting at {env.now}')
yield env.timeout(duration)
print(f'{name} done at {env.now}')
return f'{name} result'
def coordinator(env):
task1 = env.process(task(env, 'Task 1', 5))
task2 = env.process(task(env, 'Task 2', 3))
task3 = env.process(task(env, 'Task 3', 4))
results = yield task1 & task2 & task3
print(f'All done at {env.now}')
env = simpy.Environment()
env.process(coordinator(env))
env.run()
Workflow Guide
Step 1: Define the System
Identify:
- Entities: What moves through the system? (customers, parts, packets)
- Resources: What are the constraints? (servers, machines, bandwidth)
- Processes: What are the activities? (arrival, service, departure)
- Metrics: What to measure? (wait times, utilization, throughput)
Step 2: Implement Process Functions
Create generator functions for each process type:
def entity_process(env, name, resources, parameters):
arrival_time = env.now
with resource.request() as req:
yield req
service_time = calculate_service_time(parameters)
yield env.timeout(service_time)
collect_statistics(env.now - arrival_time)
Step 3: Set Up Monitoring
Use monitoring utilities to collect data. See references/monitoring.md for comprehensive techniques.
from scripts.resource_monitor import ResourceMonitor
resource = simpy.Resource(env, capacity=2)
monitor = ResourceMonitor(env, resource, "Server")
monitor.report()
Step 4: Run and Analyze
env.run(until=simulation_time)
monitor.report()
stats.report()
monitor.export_csv('results.csv')
Advanced Features
Process Interaction
Processes can interact through events, process yields, and interrupts. See references/process-interaction.md for detailed patterns.
Key mechanisms:
- Event signaling: Shared events for coordination
- Process yields: Wait for other processes to complete
- Interrupts: Forcefully resume processes for preemption
Real-Time Simulations
Synchronize simulation with wall-clock time for hardware-in-the-loop or interactive applications. See references/real-time.md.
import simpy.rt
env = simpy.rt.RealtimeEnvironment(factor=1.0)
Comprehensive Monitoring
Monitor processes, resources, and events. See references/monitoring.md for techniques including:
- State variable tracking
- Resource monkey-patching
- Event tracing
- Statistical collection
Scripts and Templates
basic_simulation_template.py
Complete template for building queue simulations with:
- Configurable parameters
- Statistics collection
- Customer generation
- Resource usage
- Report generation
Usage:
from scripts.basic_simulation_template import SimulationConfig, run_simulation
config = SimulationConfig()
config.num_resources = 2
config.sim_time = 100
stats = run_simulation(config)
stats.report()
resource_monitor.py
Reusable monitoring utilities:
ResourceMonitor - Track single resourceMultiResourceMonitor - Monitor multiple resourcesContainerMonitor - Track container levels- Automatic statistics calculation
- CSV export functionality
Usage:
from scripts.resource_monitor import ResourceMonitor
monitor = ResourceMonitor(env, resource, "My Resource")
monitor.report()
monitor.export_csv('data.csv')
Reference Documentation
Detailed guides for specific topics:
references/resources.md - All resource types with examplesreferences/events.md - Event system and patternsreferences/process-interaction.md - Process synchronizationreferences/monitoring.md - Data collection techniquesreferences/real-time.md - Real-time simulation setup
Best Practices
- Generator functions: Always use
yield in process functions
- Resource context managers: Use
with resource.request() as req: for automatic cleanup
- Reproducibility: Set
random.seed() for consistent results
- Monitoring: Collect data throughout simulation, not just at the end
- Validation: Compare simple cases with analytical solutions
- Documentation: Comment process logic and parameter choices
- Modular design: Separate process logic, statistics, and configuration
Common Pitfalls
- Forgetting yield: Processes must yield events to pause
- Event reuse: Events can only be triggered once
- Resource leaks: Use context managers or ensure release
- Blocking operations: Avoid Python blocking calls in processes
- Time units: Stay consistent with time unit interpretation
- Deadlocks: Ensure at least one process can make progress
Example Use Cases
- Manufacturing: Machine scheduling, production lines, inventory management
- Healthcare: Emergency room simulation, patient flow, staff allocation
- Telecommunications: Network traffic, packet routing, bandwidth allocation
- Transportation: Traffic flow, logistics, vehicle routing
- Service operations: Call centers, retail checkout, appointment scheduling
- Computer systems: CPU scheduling, memory management, I/O operations
