24
2 Simulation-Based Optimization
For a straight forward model development, especially when combining different simulation paradigms, a structured concept helps to achieve sustainable
results. Therefore, the development of a conceptual model makes up the basis for
robust and realistic computer model that can be used for simulation experiments 12 .
Summarizing, it can be said that hybrid simulation is used in various forms to
assess problems from different dimensions and high complexity. The hybridization of simulation models based on mixing simulation paradigms gives a broader
flexibility to the modeler, e.g., to capture problems that refer to discrete and continuous structures at the same time. An example for such a problem is the depiction
of energy consumption in production systems. While the representation of the
energy flows requires the use the SD paradigm, events in the production flow, as
for example the flow of parts through the machines, causing changes in the energy
consumption of production machines, require the use of the DES paradigm. The
hybrid simulation allows the depiction of discrete and continuous processes in
one single model.
2.2
Optimization Methods
Optimization is “the process of searching for the best value that can be realized or
attained. In mathematical programming, this is the minimum or maximum value
of the objective over the feasible region” [Op2013, p. 1092]. Due to its general
formulation, optimization can be seen as a cross-application, which occurs in the
most diverse contexts [Li1992, p. 1]. To name just a few examples: in the process
industry, mixture problems are often considered, in which optimal mixing conditions must be determined considering various rules and restrictions [SM2009,
p. 22]. While the supply chain management (SCM) looks at optimization tasks
concerning the entire value chain through demand-based deliveries, faster adaptation to changes in the market, reduction of stocks and costs in the logistics
chain as well as shorter order processing times, optimization problems in the area
of transport and traffic are the performance increase of networks and routes, the
optimal fleet assignments and crew deployment planning or the optimization of
dynamic traffic situations, such as traffic densities in road traffic [SM2009, p. 23].
The scope of optimization is infinite, ranging from portfolio optimization analysis in finance, determining optimal energy costs and planning decisions in energy
12 The interrested reader is referred to Eldabi et al. for further reading on representation
methods for a hybrid simulation conceptual models [El+2016, pp. 1397–1398].
2 Simulation-Based Optimization
For a straight forward model development, especially when combining different simulation paradigms, a structured concept helps to achieve sustainable
results. Therefore, the development of a conceptual model makes up the basis for
robust and realistic computer model that can be used for simulation experiments 12 .
Summarizing, it can be said that hybrid simulation is used in various forms to
assess problems from different dimensions and high complexity. The hybridization of simulation models based on mixing simulation paradigms gives a broader
flexibility to the modeler, e.g., to capture problems that refer to discrete and continuous structures at the same time. An example for such a problem is the depiction
of energy consumption in production systems. While the representation of the
energy flows requires the use the SD paradigm, events in the production flow, as
for example the flow of parts through the machines, causing changes in the energy
consumption of production machines, require the use of the DES paradigm. The
hybrid simulation allows the depiction of discrete and continuous processes in
one single model.
2.2
Optimization Methods
Optimization is “the process of searching for the best value that can be realized or
attained. In mathematical programming, this is the minimum or maximum value
of the objective over the feasible region” [Op2013, p. 1092]. Due to its general
formulation, optimization can be seen as a cross-application, which occurs in the
most diverse contexts [Li1992, p. 1]. To name just a few examples: in the process
industry, mixture problems are often considered, in which optimal mixing conditions must be determined considering various rules and restrictions [SM2009,
p. 22]. While the supply chain management (SCM) looks at optimization tasks
concerning the entire value chain through demand-based deliveries, faster adaptation to changes in the market, reduction of stocks and costs in the logistics
chain as well as shorter order processing times, optimization problems in the area
of transport and traffic are the performance increase of networks and routes, the
optimal fleet assignments and crew deployment planning or the optimization of
dynamic traffic situations, such as traffic densities in road traffic [SM2009, p. 23].
The scope of optimization is infinite, ranging from portfolio optimization analysis in finance, determining optimal energy costs and planning decisions in energy
12 The interrested reader is referred to Eldabi et al. for further reading on representation
methods for a hybrid simulation conceptual models [El+2016, pp. 1397–1398].