popular and found many applications in business and industry and at present forms a very important tool of operation researcher's tool kit. "Monte Carlo' is the code number given to the technique by the above two mathematicians The technique employs random numbers and is used to solve problems that involve probability and wherein physical experimentation is impracticable and formulation of mathematical model is impossible. It is a method of simulation by sampling technique. The steps involved in carrying out Monte Carlo simulation are 1. Select the measure of effectiveness objective function of the problem. It is either to be maximized or minimized. For example, it may be idle time of service facility in a queuing problem or number of shortages or the total inventory cost in an inventory control problem. 2. Identify the variables that affect the measure of effectiveness significantly For example, number of service facilities in a queuing problem or demand, lead time and safety stock in an inventory control problem 3. Determine the cumulative probability distribution of each variable selected in step 2. Plot these distributions with values of the variables along x-axis and cumulative probability values along the y-axis 4. Get a set of random numbers from a random number generator 5. Consider each random number as a decimal value of the cumulative probability distribution Enter the cumulative distribution plot along the y-axis Project this point horizontally till it meets the distribution curve. Then project the point of intersection down on the x-axis 6. Record the value (or values if several variables are being simulated generated in step 5 Substitute in the formula chosen for measure of effectiveness and find its simulated value 7. Repeat steps 5 and 6 until sample is large enough to the satisfaction of the decision maker Though in any real problem to be solved by simulation, the variables in the problem are probabilistic in nature, we first consider below a problem where they are fixed and constant and see how the simulation technique which involves repetitive experimentation can be used to analyse such a problem. The remaining problems involve variables that are probabilistic in nature b36 EXAMPLE 13.7-1 (Event-Type Simulation) Customers arrive at a service facility to get the required service. The interarrival and service times are constant and are 1.8 minutes and 4 minutes respectively. Simulate the system for 14 minutes. Determine the average waiting time of a customer and idle time of the service facility (Andhra Prod. Council, 1978)