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.
This exercise is part of the course
Discrete Event Simulation in Python
Exercise instructions
- 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.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
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()