The Goal Of Queueing Analysis Is To Minimize: A) The ✓ Solved
The goal of queueing analysis is to minimize: A) the
The goal of queueing analysis is to minimize:
A) the sum of customer waiting costs and service costs.
B) the sum of customer waiting time and service time.
C) service costs.
D) customer waiting time.
E) None of the answer choices is correct.
A single server queueing system has an average service time of 8 minutes and an average time between arrivals of 10 minutes. The arrival rate is:
A) 6 per hour.
B) 7.5 per hour.
C) 8 per hour.
D) 10 per hour.
E) 12.5 per hour.
The term queue discipline refers to:
A) the willingness of customers to wait in line for service.
B) having multiple waiting lines without customers switching from line to line.
C) the order in which customers are processed.
D) the reason waiting occurs in underutilized systems.
E) None of the answer choices is correct.
Which of the following is not an example of a commercial service system?
A) ATM cash machine
B) Brokerage service
C) Travel agency
D) Tool crib
E) Call center
As the ratio of arrival rate to service rate is increased, which of the following is likely?
A) Customers move through the system in less time because utilization is increased.
B) Customers move through the system more slowly because utilization is increased.
C) Utilization is decreased because of the added strain on the system.
D) The average number in the system decreases.
E) None of the answer choices is correct.
Computers can simulate years of operation in seconds.
A. True
B. False
Simulation is especially useful for situations too complex to be analyzed using analytical models.
A. True
B. False
Simulation enables a decision maker to experiment with a system and observe its behavior.
A. True
B. False
The main reason that a large number of replications of a simulation would be made is:
A) computers are usually used, and they can easily handle a large number of replications.
B) it is part of the scientific approach.
C) it is a form of sampling, and large samples give more accurate results than small samples.
D) it is more likely to provide optimal answers.
E) None of the answer choices is correct.
Which of the following would not be considered a main advantage of simulation?
A) It permits experimentation with the system.
B) It generates an optimal solution.
C) It compresses time.
D) It can serve as a training tool.
E) All of the answers choices are advantages.
Paper For Above Instructions
Queueing analysis is an essential aspect of operations management, primarily focusing on understanding and improving the efficiency of service systems. The primary goal is to minimize the sum of customer waiting costs and service costs. This minimizes the total expenditures associated with customer service while optimizing the waiting experience (Hillier & Lieberman, 2010). The costs associated with customer waiting can be conceptualized as opportunity costs and diminished customer satisfaction, which can significantly impact overall business performance.
In a typical queueing system, the arrival rate and service rate play crucial roles in determining system performance. For instance, if a service system has an average service time of 8 minutes and an average time between arrivals of 10 minutes, the arrival rate can be computed as 6 per hour. This is calculated by determining how many arrivals occur per given time frame (Bäuerle & Rieder, 2011). Understanding these rates allows managers to predict wait times and allocate resources more effectively.
Queue discipline is another critical concept in queueing theory, which refers to how customers are prioritized or processed within the system. The order in which customers are served can influence the perceived fairness and efficiency of the service provided. Different queue disciplines may include first-come-first-served (FCFS), last-come-first-served (LCFS), priority-based systems, or even random selection. Each approach has its advantages and disadvantages depending on the service context (Kleinrock, 1975).
When evaluating commercial service systems, one must differentiate between systems that provide a tangible product versus those providing an intangible service. Examples of commercial services include ATMs, brokerage services, travel agencies, and call centers. In contrast, a tool crib, which serves as a storage facility for tools, wouldn’t be classified as a commercial service system, as its primary function is not directly providing a service to customers (Chase et al., 2010).
The ratio of arrival rate to service rate is crucial in predicting system performance. As this ratio increases, customers typically move through the system more slowly because the system becomes increasingly utilized. High utilization rates can lead to congestion, increasing average wait times and contributing to customer dissatisfaction (Larson, 1987).
Computers have revolutionized queueing analysis through simulation techniques that allow managers to understand complex systems within mere seconds. Simulation is particularly useful for scenarios too intricate for analytical solutions. By creating a virtual model of the system, decision-makers can observe behaviors, test changes, and evaluate the impact of various strategies without real-world consequences. The efficiency of simulations is vital for modern operations management (Law & Kelton, 2000).
The main justification for conducting multiple replications of a simulation is rooted in the scientific method. A sizable sample increases the reliability and accuracy of results, as random variations can be minimized through larger data sets. This leads to better-informed decisions regarding changes to the operational process (Sargent, 2013).
However, it is worth noting that while simulation offers numerous advantages such as experimentation capabilities, time compression, and serving as a training tool, it does not inherently generate optimal solutions (Barton et al., 2004). Instead, simulation provides insights that can lead to improved outcomes but will require managerial judgement to implement effective changes. Therefore, any indications of optimal solutions derived from simulations should be approached with caution.
In conclusion, queueing analysis and simulation techniques are vital tools for understanding and optimizing service-oriented systems. By analyzing customer interactions and applying simulation methodologies, organizations can significantly enhance operational efficiency, reduce costs, and improve customer satisfaction.
References
- Bäuerle, N., & Rieder, U. (2011). Stochastic Queuing Networks. Springer.
- Barton, R. R., Mehta, S. D., & J. R. (2004). Introduction to Simulation. In Proceedings of the 2004 Winter Simulation Conference.
- Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2010). Operations Management. McGraw-Hill.
- Hillier, F. S., & Lieberman, G. J. (2010). Introduction to Operations Research. McGraw-Hill.
- Kleinrock, L. (1975). Queueing Systems Volume I: Theory. Wiley.
- Larson, R. C. (1987). A Conceptual Framework for the Analysis of Service Systems. Operations Research, 35(2), 206-218.
- Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis. McGraw-Hill.
- Sargent, R. G. (2013). Verification and Validation of Simulation Models. In Proceedings of the 2013 Winter Simulation Conference.
- Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling. John Wiley & Sons.
- Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies. McGraw-Hill.