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Optimization methods are used in many areas of study to find solutions that maximize or minimize some study parameters, such as minimize costs in the production of a good or service, maximize profits, minimize raw material in the development of a good, or maximize production.
Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. Simulation Optimization: Methods and Applications informs-sim.org informs-sim.org
Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective function must be estimated using statistical estimation techniques (called output analysis in simulation methodology).
Lastly, simulation modeling involves running the model multiple times under different conditions or scenarios and analyzing the results. In contrast to this approach, optimization involves solving the problem using mathematical techniques or algorithms and verifying the solution.
The key difference between modeling and simulation is that optimization modeling provides a definite recommendation for action in a specific situation, while simulation allows users to determine how a system responds to different inputs so as to better understand how it operates.