2. EVERYTHING ISDIFFICULTIF YOU CRY,EVERYTHING ISEASY IF YOU
TRY. 3. What is Simulation? Simulation means imitation of reality.
The purpose of simulation in the business worldis to understand the
behavior of a system. Before making many important decisions,
wesimulate the result to insure that we are doing theright thing.
4. When to use Simulation?? First, when experimentation is not
possible. Note thatif we can do a real experiment, the results
wouldobviously be better than simulation. Second condition for
using simulation is when theanalytical solution procedure is not
known. If analyticalformulas are known then we can find the
actualexpected value of the results quickly by using theformulas.
In simulation we can hope to get the sameresults after simulating
thousands of times. 5. Simulation is basically a data generation
technique. Sometimes it is time consuming to conduct realstudy to
know about a situation or problem. An example is the simulation of
the flow ofcustomers into and out of a bank, to help
determineservice requirements. The use of simulation frees
theprogrammer and user from having to observe a bankand keep track
of exactly when each customerarrives and leaves. Thus,
simulationisusedwhen actualexperimentation is not feasible. 6.
Example We read and hear about Air force pilots being trainedunder
simulated conditions. Since it would be impossible to train a
person whenan actual war is going on, all the conditions thatwould
prevail during a war are reconstructed andenacted so that the
trainee could develop the skillsand instincts that would be
required of him duringcombat conditions. Thus, war conditions are
simulated to impart training. 7. Example Contd All automobile
manufacturing companies have a test-track on which the vehicles
would be initially driven. The test-track would ideally have all
the bends, slopes,potholes etc., that can be found on the roadways
onwhich the vehicles would be subsequently driven. The test-track
is therefore, a simulated version of theactual conditions of the
various roadways.Simulation, in general, means the creation
ofconditions that prevail in reality, in order to draw
certainconclusions from the trials that are conducted in
theartificial conditions. A vehicle manufacturer, by driving the
vehicle on thetest-track, is conducting a trial in artificial
conditions inorder to draw conclusions regarding the road- 8. Types
of simulation Deterministic and probabilistic SimulationThe
deterministic simulation is used when process is verycomplex or
consists multiple stages with complicated (butknown) procedural
interactions between them.In probabilistic simulation, one or more
of the independentvariables is probabilistic i.e. it follows a
certain probabilitydistribution. Time dependent and Time
independent simulationIn time independent simulation it is not
important to knownexactly when the event is likely to occur. E.g.
we knowdemand of 3 units per day but dont know when during theday
the item was demanded.In time dependent it is important to know the
precise timewhen the event is likely to occur. In a queeing
situation theprecise time of arrival of customer must be known (to
know 9. Types of simulation Contd. Visual Interactive SimulationIt
uses computer graphic displays to present the consequences of
change in the value of input variation in the model. The decisions
are implemented interactively while the simulation is running. The
decision maker keep track of development of model on a graphic
interface and can alter the simulation as it progress. Business
GamesIt involves several participants who need to play a role in a
game that simulates a realistic competitive situation. Individual
or teams compete to achieve their goals in competition with the
other individual or team. Corporate and Financial SimulationIt is
used in corporate planning, especially the financial aspects. The
model integrate production, finance, marketing, and possibly other
functions, into one 10. Application of SimulationTechnique
Simulation is widely used for the following Simulation of Inventory
Problem Simulation of Queuing Problem Simulation of investment
problem Simulation of Maintenance Problem Simulation of PERT
Problem 11. Advantages of Simulation Solves problems that are
difficult or impossible tosolve mathematically Allows
experimentation without risk to actualsystem Compresses time toshow
long-term effects Serves as training toolfor decision makers 12.
Limitations of Simulation Does not produce optimum solution Model
development may be difficult Computer run time may be substantial
Monte Carlo simulation only applicable to random systems 13. Monte
Carlo Method of Simulation The principle behind this method of
simulation isrepresentative of the given system under analysisby a
system described by some known probabilitydistribution and then
drawing random samples forprobability distribution by means of
random number. In case it is not possible to describe a system
interms of standard probability distribution such asnormal,
Poisson, exponential, etc., an empiricalprobability distribution
can be constructed. 14. It can be usefully applied in cases where
the systemto be simulated has a large number of elements
thatexhibit chance (probability) in their behaviour. Simulation is
normally undertaken only with the helpof a very high-speed data
processing machine suchas computer. The user of simulation
technique must always bearin mind that the actual frequency or
probability wouldapproximate the theoretical value of probability
onlywhen the number of trials are very large i.e. whenthe
simulation is repeated a large no. of times. This can easily be
achieved with the help of acomputer by generating random numbers.
15. steps involved in Monte-Carlo simulation Step I. Obtainthe
frequency or probability of all the important variables from the
historical sources. Step II. Convert the respective probabilities
of the various variables into cumulative probabilities. Step III.
Generate random numbers for each such variable. Step IV. Based on
the cumulative probability distribution table obtained in Step II,
obtain the interval (i.e.; the range) of the assigned random
numbers. Step V. Simulate a series of experiments or trails. 16.
Example New Delhi Bakery House keeps stock of a popular brand of
cake. Previous experience indicates the daily demand as given below
17. Preparing Cumulative probabilityand assigning random numbers
18. Calculation of next demand 19. Next demand is calculated on the
basis ofcumulative probability (e.g., random number 21 liesin the
third item of cumulative probability, i.e., 0.36.Therefore, the
next demand is 25. ) Similarly, we can calculate the next demand
forothers. Total demand = 320 Average demand = Total demand / no.
of days The daily average demand for the cakes = 320 / 10 =32
cakes. 20. THANK YOUVERY MUCH
