2.3 Combination of Simulation and Optimization Methods
29
Figure 2.8 Overview of optimization methods. Following [Sh+2018, p. 218; So2018, p. 56]
perspectives. “From the simulation perspective, it is motivated by the desire to
compare the effects of different decision variables on the output of a complex
simulation model; from the optimization perspective, one might be interested
in accounting for randomness in a deterministic model to better capture the
real-life system being modeled” [JH2015, p. 1781]. The combination of simulation and optimization is often used when it comes to optimization tasks of
complex systems for which the definition of an analytical optimization model
cannot be developed with justifiable efforts and their use seems impractical due
to simplifying assumptions that distort the core of the actual problem [Do+2015,
p. 233].
This chapter briefly summarizes how simulation and optimization methods
can generally be used in combination and which objectives are followed by
29
Figure 2.8 Overview of optimization methods. Following [Sh+2018, p. 218; So2018, p. 56]
perspectives. “From the simulation perspective, it is motivated by the desire to
compare the effects of different decision variables on the output of a complex
simulation model; from the optimization perspective, one might be interested
in accounting for randomness in a deterministic model to better capture the
real-life system being modeled” [JH2015, p. 1781]. The combination of simulation and optimization is often used when it comes to optimization tasks of
complex systems for which the definition of an analytical optimization model
cannot be developed with justifiable efforts and their use seems impractical due
to simplifying assumptions that distort the core of the actual problem [Do+2015,
p. 233].
This chapter briefly summarizes how simulation and optimization methods
can generally be used in combination and which objectives are followed by