Logistics eCommerce model: Analyzing results

Let's consider an eCommerce business that needs optimization. The main groups of processes involved are:

"Request management",
"Packaging", and
"Shipment and delivery to client".

Each of these groups of processes involves many sub-processes and tasks. For now, you'll focus on creating the model at a high level, which can (and should) be refined as more information becomes available.

Preliminary research found that each process takes 2, 1, and 5 days to complete, respectively, with standard deviations of 0.2, 0.2, and 1 day.

You have built the SimPy model and generators. Time in the model is recorded in days. The following package has been imported for you: import matplotlib.pyplot as plt.

Let's run the model and analyze the results using cluster analysis.

Deze oefening maakt deel uit van de cursus

Discrete Event Simulation in Python

Cursus bekijken

Oefeninstructies

Run the SimPy model stored in a SimPy environment named env for 5 years (assume no leap years).
Create a histogram of the model results stored in record_processes_list with 50 bins.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

env = simpy.Environment()
env.process(all_processes(env, inputs, record_processes))

# Run the SimPy model
env.____(until=___)

record_processes_list = [record_processes['Time Manage Request'],
            			 record_processes['Time Packaging'],
            			 record_processes['Time Shipping']]

# Create a histogram with 50 bins
plt.____(record_processes_list, bins=____, label=['Request', 'Packaging', 'Shipments'])
plt.legend(loc='upper right')
plt.xlabel('Duration (days)')
plt.ylabel('Number of occurrences')
plt.show()

Code bewerken en uitvoeren