Simpy Discrete Event Simulation

SimPy — Discrete-Event Simulation

Overview

SimPy is a process-based discrete-event simulation framework using standard Python generators. Model systems where entities (customers, vehicles, packets) interact with shared resources (servers, machines, bandwidth) over time, with event-driven scheduling and optional real-time synchronization.

When to Use

• Modeling queue-based systems with resource contention (servers, machines, staff)
• Manufacturing process simulation (production lines, scheduling, bottleneck analysis)
• Network simulation (packet routing, bandwidth allocation, latency analysis)
• Capacity planning (determining optimal resource levels for target throughput)
• Healthcare operations (ER patient flow, staff allocation, bed management)
• Logistics and transportation (warehouse operations, vehicle routing)
• For continuous-time ODE systems → use SciPy solve_ivp
• For agent-based modeling → use Mesa

Prerequisites

# pip install simpy
import simpy
import random

Quick Start

import simpy
import random

def customer(env, name, server):
    """Customer arrives, waits for server, gets served, departs."""
    arrival = env.now
    with server.request() as req:
        yield req  # Wait in queue
        wait = env.now - arrival
        yield env.timeout(random.expovariate(1/3))  # Service time
        print(f'{name}: waited {wait:.1f}, served at {env.now:.1f}')

def arrivals(env, server):
    for i in range(20):
        yield env.timeout(random.expovariate(1/2))  # Inter-arrival
        env.process(customer(env, f'C{i}', server))

env = simpy.Environment()
server = simpy.Resource(env, capacity=2)
env.process(arrivals(env, server))
env.run(until=50)

Core API

1. Environment & Processes

import simpy

# Standard environment
env = simpy.Environment(initial_time=0)

# Processes are Python generators that yield events
def machine(env, name, repair_time):
    while True:
        yield env.timeout(random.expovariate(1/10))  # Time to failure
        print(f'{name} broke at {env.now:.1f}')
        yield env.timeout(repair_time)
        print(f'{name} repaired at {env.now:.1f}')

# Start processes — returns a Process event
proc = env.process(machine(env, 'Machine-1', repair_time=2))

# Run until time limit or no events remain
env.run(until=100)
# env.run()  # Run until no more events

# Current simulation time
print(f'Final time: {env.now}')

# Processes can return values and be awaited
def subtask(env, duration):
    yield env.timeout(duration)
    return f'completed in {duration}'

def main_task(env):
    # Sequential: wait for one process
    result = yield env.process(subtask(env, 5))
    print(f'Subtask {result} at {env.now}')

    # Parallel: wait for ALL (AllOf)
    t1 = env.process(subtask(env, 3))
    t2 = env.process(subtask(env, 4))
    results = yield t1 & t2  # AllOf — resumes when both done
    print(f'Both done at {env.now}')

    # Race: wait for ANY (AnyOf)
    t3 = env.process(subtask(env, 2))
    t4 = env.process(subtask(env, 6))
    result = yield t3 | t4  # AnyOf — resumes when first completes
    print(f'First done at {env.now}')

env = simpy.Environment()
env.process(main_task(env))
env.run()

2. Resources

import simpy

env = simpy.Environment()

# Basic resource — capacity-limited (e.g., 2 servers)
server = simpy.Resource(env, capacity=2)
print(f'Capacity: {server.capacity}, In use: {server.count}, Queue: {len(server.queue)}')

# Priority resource — lower number = higher priority
priority_server = simpy.PriorityResource(env, capacity=1)

def vip_customer(env, res):
    with res.request(priority=1) as req:  # Higher priority
        yield req
        yield env.timeout(3)

def regular_customer(env, res):
    with res.request(priority=10) as req:  # Lower priority
        yield req
        yield env.timeout(3)

# Preemptive resource — high priority interrupts low priority
preemptive = simpy.PreemptiveResource(env, capacity=1)

def urgent_job(env, res):
    with res.request(priority=0, preempt=True) as req:
        yield req  # May interrupt current user
        yield env.timeout(1)

# Container — bulk material (fuel, water, inventory)
tank = simpy.Container(env, capacity=100, init=50)

def refuel(env, tank):
    yield tank.put(30)  # Add 30 units
    print(f'Tank level: {tank.level}/{tank.capacity}')

def consume(env, tank):
    yield tank.get(20)  # Remove 20 units
    print(f'Tank level: {tank.level}/{tank.capacity}')

# Store — FIFO object storage
warehouse = simpy.Store(env, capacity=10)

def producer(env, store):
    for i in range(5):
        yield env.timeout(2)
        yield store.put(f'Item-{i}')

def consumer(env, store):
    while True:
        item = yield store.get()
        print(f'Got {item} at {env.now}')
        yield env.timeout(3)

# FilterStore — selective retrieval
parts = simpy.FilterStore(env, capacity=20)

def picker(env, store):
    # Get specific item matching condition
    item = yield store.get(lambda x: x['color'] == 'red')
    print(f'Found red item: {item}')

3. Events & Synchronization

import simpy

env = simpy.Environment()

# Basic event — manual trigger for signaling between processes
signal = env.event()

def waiter(env, event):
    print(f'Waiting at {env.now}')
    value = yield event  # Blocks until triggered
    print(f'Got signal "{value}" at {env.now}')

def sender(env, event):
    yield env.timeout(5)
    event.succeed(value='go')  # Trigger with value

env.process(waiter(env, signal))
env.process(sender(env, signal))
env.run()
# Output: Waiting at 0, Got signal "go" at 5

# Timeout — most common event
yield env.timeout(delay=5)

# Process interruption
def interruptible(env, name):
    try:
        yield env.timeout(10)
    except simpy.Interrupt as interrupt:
        print(f'{name} interrupted: {interrupt.cause} at {env.now}')

def interruptor(env, proc):
    yield env.timeout(3)
    proc.interrupt('maintenance')

proc = env.process(interruptible(env, 'Worker'))
env.process(interruptor(env, proc))

# Barrier synchronization — wait for N processes
class Barrier:
    def __init__(self, env, n):
        self.env = env
        self.n = n
        self.count = 0
        self.event = env.event()

    def wait(self):
        self.count += 1
        if self.count >= self.n:
            self.event.succeed()
        return self.event

def phase_worker(env, name, barrier):
    yield env.timeout(random.uniform(1, 5))  # Phase work
    print(f'{name} reached barrier at {env.now:.1f}')
    yield barrier.wait()  # Wait for all workers
    print(f'{name} passed barrier at {env.now:.1f}')

env = simpy.Environment()
barrier = Barrier(env, n=3)
for i in range(3):
    env.process(phase_worker(env, f'W{i}', barrier))
env.run()

4. Monitoring & Statistics

import simpy

# Inline statistics collection
class Stats:
    def __init__(self):
        self.wait_times = []
        self.queue_lengths = []

    def report(self):
        if self.wait_times:
            avg_wait = sum(self.wait_times) / len(self.wait_times)
            max_wait = max(self.wait_times)
            print(f'Avg wait: {avg_wait:.2f}, Max wait: {max_wait:.2f}')
            print(f'Customers served: {len(self.wait_times)}')

def customer(env, name, server, stats):
    arrival = env.now
    with server.request() as req:
        yield req
        wait = env.now - arrival
        stats.wait_times.append(wait)
        stats.queue_lengths.append(len(server.queue))
        yield env.timeout(random.expovariate(1/3))

env = simpy.Environment()
server = simpy.Resource(env, capacity=2)
stats = Stats()

def gen(env, server, stats):
    for i in range(100):
        yield env.timeout(random.expovariate(1/2))
        env.process(customer(env, f'C{i}', server, stats))

env.process(gen(env, server, stats))
env.run(until=200)
stats.report()

# Resource monitoring via monkey-patching
def patch_resource(resource, data):
    """Patch resource to log request/release events."""
    original_request = resource.request
    original_release = resource.release

    def monitored_request(*args, **kwargs):
        req = original_request(*args, **kwargs)
        data.append((resource._env.now, 'request', resource.count, len(resource.queue)))
        return req

    def monitored_release(*args, **kwargs):
        result = original_release(*args, **kwargs)
        data.append((resource._env.now, 'release', resource.count, len(resource.queue)))
        return result

    resource.request = monitored_request
    resource.release = monitored_release

log = []
patch_resource(server, log)
# After simulation: analyze log for utilization, queue dynamics

5. Real-Time Simulation

import simpy.rt

# Real-time environment — synchronized with wall clock
env = simpy.rt.RealtimeEnvironment(factor=1.0)  # 1 sim unit = 1 second
# factor=0.1 → 10x faster (1 sim unit = 0.1 seconds)
# factor=60 → 1 sim unit = 1 minute

# Strict mode raises RuntimeError if simulation can't keep up
env_strict = simpy.rt.RealtimeEnvironment(factor=1.0, strict=True)

# Non-strict mode (default) allows slower-than-real-time execution
env_relaxed = simpy.rt.RealtimeEnvironment(factor=1.0, strict=False)

def periodic_task(env, interval):
    while True:
        print(f'Tick at sim time {env.now:.1f}')
        yield env.timeout(interval)

env = simpy.rt.RealtimeEnvironment(factor=1.0)
env.process(periodic_task(env, 2.0))
env.run(until=10)
# Prints "Tick" every ~2 real seconds

Key Concepts

Resource Selection Guide

Process Interaction Mechanisms

Common Workflows

1. Manufacturing Line Simulation

import simpy
import random

def part(env, name, machines, buffer, stats):
    """Part flows through sequential machines with intermediate buffer."""
    for i, machine in enumerate(machines):
        with machine.request() as req:
            yield req
            process_time = random.triangular(1, 3, 2)
            yield env.timeout(process_time)

    if buffer.level < buffer.capacity:
        yield buffer.put(1)
        stats['produced'] += 1

def part_generator(env, machines, buffer, stats):
    i = 0
    while True:
        yield env.timeout(random.expovariate(1/2))
        env.process(part(env, f'Part-{i}', machines, buffer, stats))
        i += 1

random.seed(42)
env = simpy.Environment()
machines = [simpy.Resource(env, capacity=1) for _ in range(3)]
output_buffer = simpy.Container(env, capacity=100, init=0)
stats = {'produced': 0}
env.process(part_generator(env, machines, output_buffer, stats))
env.run(until=480)  # 8-hour shift
print(f'Parts produced: {stats["produced"]}')
print(f'Buffer level: {output_buffer.level}')

2. Multi-Server Queue with Priority

import simpy
import random

def patient(env, name, priority, er, stats):
    arrival = env.now
    with er.request(priority=priority) as req:
        yield req
        wait = env.now - arrival
        stats['waits'].append((name, priority, wait))
        service = random.expovariate(1/15)  # ~15 min avg
        yield env.timeout(service)

def patient_arrivals(env, er, stats):
    i = 0
    while True:
        yield env.timeout(random.expovariate(1/5))  # ~5 min between arrivals
        pri = random.choices([1, 2, 3], weights=[0.1, 0.3, 0.6])[0]
        env.process(patient(env, f'P{i}', pri, er, stats))
        i += 1

random.seed(42)
env = simpy.Environment()
er = simpy.PriorityResource(env, capacity=3)
stats = {'waits': []}
env.process(patient_arrivals(env, er, stats))
env.run(until=480)

# Analyze by priority
for pri in [1, 2, 3]:
    waits = [w for _, p, w in stats['waits'] if p == pri]
    if waits:
        print(f'Priority {pri}: avg wait {sum(waits)/len(waits):.1f}, n={len(waits)}')

3. Producer-Consumer with Monitoring

Text-only workflow (combines Core API modules 2, 3, 4):

1. Create simpy.Store with bounded capacity (Module 2: Resources)
2. Implement producer process that yield store.put(item) with production delay (Module 2)
3. Implement consumer process that yield store.get() with processing delay (Module 2)
4. Add event signaling for backpressure when store full (Module 3: Events)
5. Collect throughput, queue length, and idle time statistics (Module 4: Monitoring)
6. Run simulation and generate report

Key Parameters

Best Practices

1. Always use context managers for resources: with resource.request() as req: yield req ensures automatic release even on exceptions
2. Set random seeds for reproducibility: random.seed(42) before creating processes; use numpy.random for more distributions
3. Collect statistics inline: Append to lists during simulation, compute aggregates after env.run() — don't query mid-simulation
4. Use triangular distribution for process times: random.triangular(min, max, mode) is more realistic than uniform for service times
5. Anti-pattern — forgetting yield: env.timeout(5) without yield creates the event but doesn't pause the process. Always yield env.timeout(5)
6. Anti-pattern — reusing events: Events can only be triggered once. Create new env.event() for each signal cycle; for repeatable signals, create fresh events in a loop

Common Recipes

Recipe: Simulation with Multiple Replications

import simpy
import random
import statistics

def run_single(seed, sim_time=480, n_servers=2):
    random.seed(seed)
    env = simpy.Environment()
    server = simpy.Resource(env, capacity=n_servers)
    waits = []

    def customer(env, server):
        arrival = env.now
        with server.request() as req:
            yield req
            waits.append(env.now - arrival)
            yield env.timeout(random.expovariate(1/3))

    def gen(env, server):
        while True:
            yield env.timeout(random.expovariate(1/2))
            env.process(customer(env, server))

    env.process(gen(env, server))
    env.run(until=sim_time)
    return sum(waits) / len(waits) if waits else 0

# Run 30 replications
results = [run_single(seed=i) for i in range(30)]
print(f'Mean avg wait: {statistics.mean(results):.2f}')
print(f'95% CI: ±{1.96 * statistics.stdev(results) / len(results)**0.5:.2f}')

Recipe: Interrupt-Based Maintenance

import simpy
import random

def machine(env, name, repair_crew):
    while True:
        try:
            # Operate until failure
            ttf = random.expovariate(1/50)  # Mean 50 time units to failure
            yield env.timeout(ttf)
            print(f'{name} failed at {env.now:.1f}')
        except simpy.Interrupt:
            print(f'{name} interrupted for maintenance at {env.now:.1f}')

        # Repair (needs repair crew)
        with repair_crew.request() as req:
            yield req
            repair = random.uniform(2, 5)
            yield env.timeout(repair)
            print(f'{name} repaired at {env.now:.1f}')

def maintenance_scheduler(env, machines_procs):
    """Periodic preventive maintenance every 40 time units."""
    while True:
        yield env.timeout(40)
        for proc in machines_procs:
            if proc.is_alive:
                proc.interrupt('scheduled maintenance')

env = simpy.Environment()
repair_crew = simpy.Resource(env, capacity=1)
procs = [env.process(machine(env, f'M{i}', repair_crew)) for i in range(3)]
env.process(maintenance_scheduler(env, procs))
env.run(until=200)

Recipe: Container-Based Supply Chain

import simpy
import random

def supplier(env, warehouse):
    """Deliver batch when level drops below reorder point."""
    while True:
        if warehouse.level < 20:  # Reorder point
            yield env.timeout(random.uniform(5, 10))  # Lead time
            amount = min(50, warehouse.capacity - warehouse.level)
            yield warehouse.put(amount)
            print(f'Delivered {amount} units at {env.now:.1f}, level={warehouse.level}')
        yield env.timeout(1)  # Check interval

def demand(env, warehouse, stats):
    while True:
        yield env.timeout(random.expovariate(1/2))
        qty = random.randint(1, 5)
        if warehouse.level >= qty:
            yield warehouse.get(qty)
            stats['fulfilled'] += qty
        else:
            stats['stockouts'] += 1

env = simpy.Environment()
warehouse = simpy.Container(env, capacity=100, init=80)
stats = {'fulfilled': 0, 'stockouts': 0}
env.process(supplier(env, warehouse))
env.process(demand(env, warehouse, stats))
env.run(until=500)
print(f'Fulfilled: {stats["fulfilled"]}, Stockouts: {stats["stockouts"]}')

Troubleshooting

Bundled Resources

• references/process_events_guide.md — Detailed event lifecycle (triggered→processed), composite events (AllOf/AnyOf), process interaction patterns (signaling, barriers, interruption, handshake), and advanced synchronization. Consolidated from original events.md (375 lines) + process-interaction.md (425 lines)
• references/resources_monitoring_guide.md — Complete resource type reference (Resource, Priority, Preemptive, Container, Store, FilterStore, PriorityStore), monitoring via monkey-patching (ResourceMonitor, ContainerMonitor classes), statistical collection patterns, CSV/matplotlib export, and real-time simulation (RealtimeEnvironment, time scaling, strict mode, HIL patterns). Consolidated from original resources.md (276 lines) + monitoring.md (476 lines) + real-time.md (396 lines). Scripts functionality (basic_simulation_template.py, resource_monitor.py) incorporated into Core API monitoring examples and Common Recipes

Related Skills

• matplotlib-scientific-plotting — Visualize simulation results (queue lengths, utilization over time)
• polars-dataframes — Analyze large simulation output datasets

References

• SimPy Documentation: https://simpy.readthedocs.io/
• SimPy GitHub: https://github.com/teamhide/simpy
• Banks et al., "Discrete-Event System Simulation" (textbook reference)