Introduction to Management Science A Modeling And Case Studies Approach with Spreadsheets 6th Edition By Frederick Hillier – Test Bank
Chapter 11 Queueing Models
1) The goal of queuing analysis is to minimize customer waiting lines.
2) Queueing models enable finding an appropriate balance between the cost of service and the amount of waiting.
3) The cost of customer waiting is easy to estimate.
4) Most queueing models assume that the form of the probability distribution of interarrival times is an exponential distribution.
5) The only distribution of interarrival times that fits having random arrivals is the exponential distribution.
6) The lack-of-memory property refers to a customer’s willingness to wait in line even though there are other customers that will be served first.
7) The number of customers in a system is the number in the queue plus one.
8) Queueing models conventionally assume that the queue can hold an unlimited number of customers.
9) Queue discipline refers to the willingness of customers to wait in line for service.
10) A loading dock with two servers who work together as a team would be an example of a multiple-server system.
11) The most commonly used queueing models assume a service rate that is exponential.
12) The exponential distribution will always provide a reasonably close approximation of the true service-time distribution.
13) The standard deviation for the degenerative distribution equals zero.
14) Managers who oversee queueing systems are usually concerned with how many customers are waiting and how long they will have to wait.
15) A queueing system is said to be in a “steady state” when customers arrive at a constant rate, that is, without any variability.
16) The expected waiting time in line is equal to the expected number of customers in line divided by the arrival rate.
17) The utilization factor is the ratio of the arrival rate to the service rate.
18) All single-server queueing models require the utilization factor to be less than 1.