Don´t simulate when

Don’t Simulate When

Jerry Banks & Randall Gibson

Simulation modeling has become an essential tool for analyzing anticipated performance, validating designs, demonstrating and visualizing operations, testing hypotheses, and performing many other analyses. It is the preferred tool in a variety of industries and in some industries, it is even required prior to all major capital investments. Inprevious articles we have discussed the "how's" of simulation modeling–how to get started, how to select software, how to manage a successful project, and so on. The underlying assumption of these earlier articles is that simulation modeling is the correct tool for the problem that you are trying to solve.

A question that is often overlooked but should be asked is as follows: Is simulationmodeling the right tool for the problem? This article discusses cases in which simulation modeling is inappropriate. In the past, simulation modeling was reserved for only very large or specialized projects that required one or more programmers or analysts with specialized training and much experience. The recent proliferation of simulation software has let to a significant increase inapplications–many by users without appropriate training or experience. It has also lead to an increasing dependence on simulation to solve a variety of problems.

Although many of these projects are successful, the tool can be, and sometimes is, misapplied. We're concerned that this can lead to unsuccessful projects, and that simulation modeling, or the simulation software, may be mistakenly held at fault. Anawareness of when quantitative problem requirements or when qualitative project dynamics indicate that simulation may not be appropriate should help avoid this mistake. In this article, we present some guidelines to consider before selecting the analysis tool for your next project.

DON'T SIMULATE WHEN:

Rule (1) The problem can be solved using "common sense analysis" Consider the followingexample: An automobile tag facility is being designed. Customers arrive at random to purchase their automobile tags at a rate of 100 per hour. The time for a clerk to serve a customer varies, but averages 5 minutes. What is the minimum number of clerks required?

Thus, to avoid an explosive situation, at least 9 clerks will be needed. The more clerks, the shorter will be the average timewaiting. This problem could have been analyzed by simulation, but that is unnecessary, and would take longer to program and run than the above solution.

Rule (2) The problem can be solved analytically (using a closed form) There are steady-state queuing models, probabilistic inventory models, and others that can be solved using equations–i.e., in a closed form–and this is a much less expensive methodto use compared to simulation. In the license tag example above, assume that all of the times are exponentially distributed. How long do the customers wait in the queue, wQ, on the average, if there are 10 clerks? This is called an M/M/c model where the first M indicates Markovian arrivals, the second M indicates Markovian servers, and c is the number of parallel servers. Markovian is another wayof saying that the values are exponentially distributed.

An equation can be used to determine the probability that the system is empty, from which the average number in the system can be determined. A graph was developed by F.S. Hillier and G.J. Lieberman to accomplish the same result. Using that graph, the average number in the system, L, is 10.77. Little's equation relates L and w, the timein the system. This is certainly a much faster analysis than using simulation.

Rule (3) It's easier to change or perform direct experiments on the real system This might seem obvious, but not always. We've seen cases where a model will be commissioned to solve a problem, and actually take more time and money to complete than a simple direct experiment would have required. Consider the...