The random variables corresponding to the interarrival times of customers and the service times of the servers are commonly part of a(n) simulation.

Monte Carlo simulation

Step-by-step explanation:

In queue modeling questions, customers arrive with a random order in a certain time span and the service times are considered to be constant. This constitutes a randomness problem due to the interarrival times of the customers. Monte Carlo simulations are used for solving the parameters than entail variations or uncertainties. In order to solve this situation, Monte Carlo method uses a distribution pattern instead of a single variable by using the constraints given by the user and gives all the possible solutions with their possibilities. Thus, when all the possible inputs are introduced into a Monte Carlo algorithm, all the possible outcomes and probabilities are given as outcomes. Since, all the interarrival times can be introduced into a Monte Carlo algorithm, all the queue possibilities can be known. Thus, this problem type can be solved with Monte Carlo simulation due to the randomness.  

62.5 paisa

step-by-step explanation:

4000*70 = 280000

280000-(2000*90) = 100000

4000-2400 = 1600

100000/1600 = 62.5

( give brainliest)