Simulation for Enterprise-Scale Systems
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
30 + 1, if 0 ≤ t ≤ 60.
30 + 5, if 60 < t ≤ 120.
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.
These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction
of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice.
Unethical use is strictly forbidden.
import numpy as np
import matplotlib.pyplot as plt
person = np.zeros((2,120))
queue_time_history = np.zeros(120)
for j in range...