Simpy

---------|----------| | 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()

# Basic resource (e.g., servers)
resource = simpy.Resource(env, capacity=2)

# Priority resource
priority_resource = simpy.PriorityResource(env, capacity=1)

# Container (e.g., fuel tank)
fuel_tank = simpy.Container(env, capacity=100, init=50)

# Store (e.g., warehouse)
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):
    # Start tasks in parallel
    task1 = env.process(task(env, 'Task 1', 5))
    task2 = env.process(task(env, 'Task 2', 3))
    task3 = env.process(task(env, 'Task 3', 4))

    # Wait for all to complete
    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 logic
    arrival_time = env.now

    # Request resources
    with resource.request() as req:
        yield req

        # Service logic
        service_time = calculate_service_time(parameters)
        yield env.timeout(service_time)

    # Departure logic
    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

# Create and monitor resource
resource = simpy.Resource(env, capacity=2)
monitor = ResourceMonitor(env, resource, "Server")

# After simulation
monitor.report()

Step 4: Run and Analyze

# Run simulation
env.run(until=simulation_time)

# Generate reports
monitor.report()
stats.report()

# Export data for further analysis
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)  # 1:1 time mapping
# factor=0.5 means 1 sim unit = 0.5 seconds (2x faster)

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 resource
• MultiResourceMonitor - Monitor multiple resources
• ContainerMonitor - Track container levels
• Automatic statistics calculation
• CSV export functionality

Usage:

from scripts.resource_monitor import ResourceMonitor

monitor = ResourceMonitor(env, resource, "My Resource")
# ... run simulation ...
monitor.report()
monitor.export_csv('data.csv')

Reference Documentation

Detailed guides for specific topics:

• references/resources.md - All resource types with examples
• references/events.md - Event system and patterns
• references/process-interaction.md - Process synchronization
• references/monitoring.md - Data collection techniques
• references/real-time.md - Real-time simulation setup

Best Practices

1. Generator functions: Always use yield in process functions
2. Resource context managers: Use with resource.request() as req: for automatic cleanup
3. Reproducibility: Set random.seed() for consistent results
4. Monitoring: Collect data throughout simulation, not just at the end
5. Validation: Compare simple cases with analytical solutions
6. Documentation: Comment process logic and parameter choices
7. Modular design: Separate process logic, statistics, and configuration

Common Pitfalls

1. Forgetting yield: Processes must yield events to pause
2. Event reuse: Events can only be triggered once
3. Resource leaks: Use context managers or ensure release
4. Blocking operations: Avoid Python blocking calls in processes
5. Time units: Stay consistent with time unit interpretation
6. 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