The techniques of LPP. Transportation, and Assignment are used for optimization of various types of problem faced in business situations. However, all the business situations can not be solved with the above techniques only. There may be some complex situations, where numbers of assumptions are also necessary. It may be quite often possible to simulate the given system and study the behavior. To simulate means to imitate. In general, simulation involves developing a model of real phenomenon and then performing experiments on the model evolved. It is to be noted that it is a descriptive and not optimizing technique. In simulation, a given system is copied and the variables and constants associated with it are manipulated in that artificial environment to examine the behavior of the system. For ex: aerodynamic testing, scaled down models of airplanes and placing them in work tunnels etc. Thus, also in a complex situation of business a given system is taken and simulated for obtaining the required results. It consists of four phases: 1. Definition of the problem and statement of objectives. v15 2. Construction of an appropriate model 3. Experimentation with the model constructed. 4. Evaluation of the results of simulation. Monte Carlo Simulation: Although simulation can be of many types, our discussion will focus on the probabilistic simulation using the Monte Carlo method. Also called computer simulation, it can be described as a numerical technique that involves modeling a stochastic system with objective of predicting the system's behaviours. The chance element is a very significant feature of Monte Carlo simulation and this approach can be used when the given process has a random, or chance, component. In using the Monte Carlo method, a given problem is solved by simulating the original data with random number generators. Basically, its use requires two things, first, as mentioned earlier, we must have a model that represents an image of the reality of the situation. Here the model refers to the probability distribution of the variable in question. What is significant here is that the variable may not be known to explicitly follow any of the theoretical distribution like Poisson, Normal and so on. The distribution may be obtained by direct observation or from past records. To illustrate, suppose that a bakery keeps a record of the sale of the number of cakes of a certain type and the information relating to sales of 200 days is as below - Demand (No of cakes): 5 6 7 8 9 10 11 12 total (No of days) : 10 16 50 62 38 12 8 200 4 Illustration 13. State the major two reasons for using simulation to solve a problem A confectioner sells confectionery items. Past data of demand per week in hundred kilograms with frequency is given below: Demand/Week 0 5 10 15 20 Frequency 2 11 8 21 5 25 3 Using the following sequence of random numbers, generate the demand for the next 10 weeks. Also find out the average demand per week. Random numbers 35 52 13 90 23 73 34 57 35 83 56 67 66 60 94