## Info

define the distribution (e.g., mean and standard deviation for a normal distribution)]. If these data are not carefully obtained and accurate, the results can be misleading, if not erroneous. Decision makers are cautioned about believing results from Monte Carlo simulations presented to several decimal places when there is often uncertainty in the first decimal place.

Example 17-3. The manager of a service center is contemplating the addition of a second service counter. He has observed that people are usually waiting in line. If the service center operates 12 hours per day and the cost of a checkout clerk is \$60.00 (burdened) per hour, simulate the manager's problem using the Monte Carlo method, assuming that the loss of good will is approximately \$50.00 per hour.

The first step in the process is to develop procedures for defining arrival rates and service rates. The use of simulation implies that the distribution expressions are either nonexistent for this type of problem or do not apply to this case. In either event, we must construct either expressions or charts for arrival and service rates.

The arrival and service rates are obtained from sample observations over a given period of time and transformed into histograms. Let us assume that we spend some time observing and recording data at the one service counter. The data recorded is the time between customer arrivals and the number of occurrences of these arrivals. The same procedure is repeated for servicing. We record the amount of time each person spends at the checkout facility and the number of times this occurs. These data are shown in Table 17-9 and transformed to histograms in Figures 17-7 and 17-8. From Table 17-9 and