3452. Queuing Analysis Methods for Decision Making


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Patrick Brown: 3452. Queuing Analysis Methods for Decision Making. 2008.



The ability to make good and timely decisions is an essential skill for all Engineering Managers. Basing management decisions on tribal knowledge or ‘gut’ feelings is no longer acceptable. Where possible, the decision making process must be assisted via quantitative methods. There are a variety of complex quantitative methods for decision-making. A few examples are linear programming, non-linear programming, probability theory, multivariable optimization, multidisciplinary optimization, Pareto analysis, decision trees, and Monte Carlo simulation. As such, queuing theory (the topic of this paper) is a subset of probability theory. Specifically, queuing theory is the mathematical study of waiting lines (queues). The theory enables mathematical analysis of several related processes, including arriving at the (back of the) queue, waiting in the queue (essentially a storage process), and being served by the server(s) at the front of the queue. The theory permits the derivation and calculation of several performance measures including the average waiting time in the queue or the system, the expected number waiting or receiving service, and the probability of encountering the system in certain states such as empty, full, having an available server, or having to wait a certain time to be served (utilization). One can even derive the probability that a customer will exit the queue (balk). Queuing analysis is the direct application of queuing theory. Despite its complex origin, queuing analysis yields quick and accurate forecasts of resource needs based on nothing more than a working knowledge of flow and service rates. Queuing analysis can occur on single lines and multiple lines, both in parallel and in series. In manufacturing, the output of one queue is often the input for the next.


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