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.

Este ejercicio forma parte del curso

Discrete Event Simulation in Python

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Instrucciones del ejercicio

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.

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Prueba este ejercicio completando el código de muestra.

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()

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