Como usar o diagrama de ambiente no Python?

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Environment Diagram In Python

Introduction to Dynamic Systems and Discrete-Event Simulation Models

Identify problems where discrete-event simulations can be helpful in supporting management and decision-making.
Understand the main components of discrete-event models and how to interpret model outputs.

Developing Discrete-Event Models Using SimPy

Learn how to build a SimPy model environment and add processes and resources.
Explore different types of resources available and options to control and schedule events.
Build a complete SimPy model for an aircraft assembly line.

Introduction to the SimPy package

import simpy

# create a SimPy environment
env = simpy.Environment()

# define processes and resources

# run simulation
env.run(until=100)

Building a car washer model with SimPy

import simpy

def car_washer(env):
    while True:
        # washing process
        yield env.timeout(10)

env = simpy.Environment()
env.process(car_washer(env))
env.run(until=100)

Modeling a car production line: Python generators

import simpy

def car_production(env):
    yield env.timeout(10)
    # production process

env = simpy.Environment()
env.process(car_production(env))
env.run(until=100)

Modeling a car production line: Create and run the model

import simpy

def car_production(env):
    while True:
        yield env.timeout(10)
        # production process

env = simpy.Environment()
env.process(car_production(env))
env.run(until=100)

SimPy package: Types of resources

import simpy

def car_production(env, resource):
    with resource.request() as req:
        yield req
        # production process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(car_production(env, resource))
env.run(until=100)

Identify appropriate SimPy resources

import simpy

def car_production(env, resource):
    with resource.request() as req:
        yield req
        # production process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(car_production(env, resource))
env.run(until=100)

Managing payment queues

import simpy

def petrol_station(env, resource):
    with resource.request() as req:
        yield req
        # petrol station process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(petrol_station(env, resource))
env.run(until=100)

Modeling a petrol station: Run the model and analyze the results

import simpy

def petrol_station(env, resource):
    with resource.request() as req:
        yield req
        # petrol station process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(petrol_station(env, resource))
env.run(until=100)

SimPy Package: Managing the scheduling of events

import simpy

def restaurant(env, resource):
    with resource.request() as req:
        yield req
        # restaurant process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(restaurant(env, resource))
env.run(until=100)

Restaurant model: Managing tables and waiting times

import simpy

def restaurant(env, resource):
    with resource.request() as req:
        yield req
        # restaurant process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(restaurant(env, resource))
env.run(until=100)

Restaurant model: Set up, run and analyze results

import simpy

def restaurant(env, resource):
    with resource.request() as req:
        yield req
        # restaurant process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(restaurant(env, resource))
env.run(until=100)

Building a discrete-event model with SimPy

import simpy

def example_process(env):
    while True:
        yield env.timeout(10)
        # process

env = simpy.Environment()
env.process(example_process(env))
env.run(until=100)

Build your model: Create an environment and resources

import simpy

# create a SimPy environment
env = simpy.Environment()

# define processes and resources

# run simulation
env.run(until=100)

Build your model: Generate aircraft orders

import simpy

def generate_orders(env):
    while True:
        yield env.timeout(10)
        # generate aircraft orders

env = simpy.Environment()
env.process(generate_orders(env))
env.run(until=100)

Build your model: Control assembly line response

import simpy

def assembly_line(env, resource):
    with resource.request() as req:
        yield req
        # assembly line process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(assembly_line(env, resource))
env.run(until=100)

Build your model: Run the model and examine results

import simpy

def assembly_line(env, resource):
    with resource.request() as req:
        yield req
        # assembly line process

env = simpy.Environment()
resource = simpy.Resource(env, capacity=1)
env.process(assembly_line(env, resource))
env.run(until=100)

Mixing Determinism and Non-Determinism in Models

Understand the types of processes in discrete-event models.
Distinguish between deterministic and non-deterministic processes and represent them in models.
Learn how to randomize events and simulate non-deterministic events.
Build a SimPy model combining both deterministic and non-deterministic processes.

Model Application, Clustering, Optimization, and Modularity

Learn optimization methods to maximize the impact of your discrete-event models.
Perform simulation ensembles using Monte Carlo approaches.
Identify clusters in model results to understand behavior and identify critical processes and tipping points.
Use objective functions to set targets for model optimization efforts.
Make your model scalable to grow stable and in a controlled manner.

Exercise: Build your model - Create an environment and resources

import simpy

# complete the dictionary with information about resources
resources = {
    'step_1_fuselage': 20,
    'step_2_wings': 8,
    'step_3_power_plant': 10,
    'step_4_landing_gear': 8
}

# create a SimPy environment
env = simpy.Environment()

# define resources with respective capacities
step_1_fuselage = simpy.Resource(env, capacity=3)
step_2_wings = simpy.Resource(env, capacity=2)
step_3_power_plant = simpy.Resource(env, capacity=2)
step_4_landing_gear = simpy.Resource(env, capacity=3)

# run simulation
env.run(until=100)

Hope you found this Python tutorial useful in understanding how to build a model and create an environment in Python. Stay tuned for more informative tutorials on Python programming.