This is a fundamental and quantitative way to understand complex systems/phenomena which is complementary to the traditional approaches of theory and experiment. Simulation (Sim.) is concerned with powerful methods of analysis designed to exploit high performance computing. This approach is becoming increasingly widespread in basic research and advanced technological applications, cross cutting the fields of physics, chemistry, mechanics, engineering, and biology.
Simulation means imitation of reality. The purpose of simulation in the business world is to understand the behavior of a system. Before making many important decisions, we simulate the result to insure that we are doing the right thing.
Simulation: is a representation of reality through the use of a model or other device, which will react in the similar manner as reality under a given set of conditions.
Analogue Simulation: Reality in physical form.
Computer simulation: Complex system in formulated into a mathematical model for which computer program are developed as problem is solved on high speed computers.
Simulation is used under two conditions.
• First, when experimentation is not possible. Note that if we can do a real experiment, the results would obviously be better than simulation.
• Second condition for using simulation is when the analytical solution procedure is not known. If analytical formulas are known then we can find the actual expected value of the results quickly by using the formulas. In simulation we can hope to get the same results after simulating thousands of times.
advantages and limitation of simulation techniques
Advantages:
1. Simulation allows experimentation with a model of the real system rather than the actual operating system.
2. Management can forsee the difficulties and bottleneck.
3. Relatively free from mathematics.
4. Comparatively flexible.
5. Easier to use than other techniques.
6. Training the operating and personal staff.
Limitations
1. Optimum resultcan not be produced.
2. Quantification of variable is not possible.
(how many variable affecting the system).
3. Difficult to make program because of difficult to know the interrelationship among many variables.
4. Comparatively costlier and time consuming method.
5. Too many tendency to rely on the simulation model.
Monte Carlo technique in simulation.
(a) Select the measure of effectiveness.
(b) Decide the variables, which influence the measure of effectiveness significantly.
(c) Determine the cumulative probability distribution of each variable.
(d) Choose a set of random number.
(e) Consider each number as a decimal value of the cumulative probability distribution.
(J) Insert the simulated value.
(g) Repeat step (e) and f) until sample is large enough for the safisfaction of decision maker.
Applications:
1. In industrial problems including the design of quening system, inventory control, communication networks, chemical processes, nuclear reactors and scheduling of production processes.
2. In business and economic problems including, price determination, forecasting etc.
3. In social problems including population growth etc.
4. In biomedical science such as fluid balance, brain activities etc.
5. In the design of weapon system, war strategies and tactics.
0 Comments