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Simulation for Enterprise-Scale Systems Assignment #1 You may use any programming language and are only allowed to simulate uniform random variables in the system. You may use inversion method to generate random variables that are not uniformly distributed. Deliverable #3. Continuing on the previous setting of deliverable 1 (single server only). After a few weeks, you find that the customers indeed tend to arrive more frequently around noon. You may consider the arrival rate λ(t) is a function of t instead of a constant. Assume the the intensity function is given by λ(t) =    t 30 + 1, if 0 ≤ t ≤ 60. − t 30 + 5, if 60 < t ≤ 120. 0, otherwise. (1) Simulate 100 independent days for this Non-homogeneous Poisson Process with the intensity function. In average, what is the percentage of customers waiting in the queue for longer than 5 minutes in one day? Assume F is an exponential distribution with mean of 30 seconds. The unit of t is in minutes.

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import numpy as np
import matplotlib.pyplot as plt

person = np.zeros((2,120))
queue_time_history = np.zeros(120)

queue_person_ratio=np.zeros((1,100))

for j in range...

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