Written By: Andre Jason
The task of decision making entails choosing between various alternatives. There are methods that will help this decision making process, which some of them are optimization and simulation Both methods, simulation or optimization, have been used in many industries such as energy, finance, manufacturing, transportation, and medical, to help them in the making of strategic, tactical, and operational decision. The one thing that needed to be remember is that these two methods are two different things. There are cases where simulation is better fit and there are cases where optimization is better, or in some cases both can be used together. But in many cases, the terms simulation and optimization are misused.
According to Chong and Zak (2010), optimization theory and methods deal with selecting the best alternative in the sense of the given objective function. In optimization, it only produces one solution. Optimization tends to be applied to solve tactical/operational issues and where the problem is not too complex. One of the application of optimization is in budgeting, where users want to minimizing cost or maximizing utility.
Whereas simulation is using model numerically to enter the desired input so that it can show how the input affect the output of the system (Law and Kelton, 1991). By using simulation, it allows the user to ask many “what if” question about changes in their system without actually changing the system themselves. The application area is numerous, some of them are to design and analyze manufacture system, to evaluate military system or tactic, to decide order policy, to analyze financial or economic system, etc.
Both methods have their own advantages and disadvantages compared to the other. Optimization offer high quality analytical solutions and powerful tactical and strategic applications, whereas simulation has the advantages by offers practical scenarios with minimal assumptions and also offer the ease to manage parameter of uncertainty to produces a long term strategy. For the disadvantages, optimization can oversimplify the problem during the modelling stage and less effective as the degree of parameters of uncertainty increase. For simulation, it needs to go through a difficult process to obtain a high quality solutions and usually associated with high cost data sets and modelling process.
By looking the advantages and disadvantages of each methods, can be seen that there is nothing better than one another. It is because the method needs to be applied where it fits the cases. For problem that is not complex, people tends to used optimization because it means there is less uncertainty, no need to make a complex mode. When the complex is full of uncertainty, people tend to use simulation method because the method can overcome uncertainties.
Reference
Chong, Edwin K. P. and Zak, Stanislaw H., 2001, an Introduction to Optimization Second Edition, John Wiley & Sons, Inc., USA
Law, Averill M., and Kelton, W. David, 1991, Simulation Modelling and Analysis, McGraw-Hill, Inc., USA