6.5 Monte Carlo Simulation
The Monte Carlo method can be used to generate distributions for the outcomes of a probabilistic system by rerunning a model repeatedly and tabulating the results. It also can be used to sample a probability distribution for the occurrence of an event. The basis of the method is the approximation of a problem solution by sampling from a random process.
This section provides three examples. The first shows how random values are generated and displayed. The second shows how Monte Carlo simulation can be used to sample from multiple random variables, with a final result that is based on a composite of the individual samples. The third example is a Monte Carlo simulation that applies the methods of the first and second examples to a determination of the net present value of a method for implementing RFID in a hospital setting.
6.5.1 Generating and Displaying Random Values
This example is about generating 1000 uniformly distributed random values between 100 and 200, obtaining their frequency distribution in bins of size 20 (i.e., in intervals of 20), and charting the result.
In MS Excel, RAND( ) generates uniformly distributed random numbers between 0 and 1. In cell A1, enter the formula =100*(1 + RAND( )); then drag it to, say, A1000 so that 1000 values are produced. Notice that the resulting values are between 100 and 200. If you wanted integer values between 100 and 200, you could have used =RANDBETWEEN(100,200). Suppose that the first value of RAND( ) ...