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  3. Discrete Event Simulation in Python
  • 1

    Introduction to Dynamic Systems and Discrete-Event Simulation Models

    Gratuito

    Let’s unravel the power of discrete-event simulations. To begin this course, you’ll learn to identify problems where discrete-event simulations can be helpful in supporting management and decision-making. You’ll also learn the main components of discrete-event models and how to interpret model outputs. Finally, you’ll build your first “queue” discrete-event model.

  • 2

    Developing Discrete-Event Models Using SimPy

    Discover the power of the SimPy package to streamline your discrete-event simulations. In chapter 2, you’ll learn how to build a SimPy model environment and how to add processes and resources. You’ll also learn the different types of resources available, as well as options to control and schedule events. To finish this chapter, you’ll build a complete SimPy model for an aircraft assembly line.

  • 3

    Mixing Determinism and Non-Determinism in Models

    Explore the types of processes that you can add to discrete-event models. You’ll learn to distinguish between deterministic and non-deterministic processes and how to represent them in models. You’ll also learn how to randomize events (or processes), which is critical to simulate non-deterministic events. Finally, you’ll build a SimPy model combining both deterministic and non-deterministic processes.

  • 4

    Model Application, Clustering, Optimization, and Modularity

    You’ll learn optimization methods to maximize the impact of your discrete-event models. You’ll learn how to perform simulation ensembles using Monte Carlo approaches and discover how to identify clusters in your model results to help you understand its behavior and identify critical processes and tipping points. You’ll also use objective functions to set targets for your model optimization efforts. To end this course, you’ll explore how to make your model scalable so that it can grow stable and in a controlled manner.

Initializing

Exercicio

Build your model: Generate aircraft orders

Now that the SimPy environment and resources have been created, let's link it with a generator that simulates aircraft purchase orders. There are 30 aircraft orders.

The assembly_line() function makes sequential resource requests for the different aircraft component manufacturing sections. The code below shows one such request.

# Open the resource step_1_fuselage request
with step_1_fuselage.request() as slot_request_1:
  request_1_time = env.now
  yield slot_request_1
  print(f"time: {env.now:7.4f} | Aircraft {aircraft_id:02d} 
                               | Enters: step_1_fuselage 
                               | Queued for {env.now-request_1_time} hours")
  yield env.timeout(processing_time[processing_time_step_names[0]])

Instruções

100 XP
  • Create a for-loop to make all order requests (total_num_orders) using a dummy variable named request_i.
  • Create a new process for each aircraft request during the for-loop; all processes are based on the generator assembly_process_request.
  • All 30 aircraft have been requested in one batch, so complete the code to reflect no waiting time between requests.