Simulation

Question 1

SImulation model properties

Answer 1

A model should be as abstract as possible and just as detailed as necessary for its purpose.

Question 2

Methods for simulation and modeling:

Answer 2

• Continuous
• Discrete
  
  • Event Driven
  • Time Controlled
• Other types: Monte Carlo Simulation, Spreadsheet simulation.

Question 3

Distinction from analytical methods.

Answer 3

• Processes (state sequences in time) are endogenously developed according to causal relationships and time mechanisms within the model
• Simulation allows for modeling of situations that are too complex for mathematics analytical methods (check the video for an example from PCB assembly)
• Values of variables are calculated step-by-step based on dependencies amongst each other and from given parameters
• Simulation is no optimization. However, it can contribute to finding the optimal solution.
• The outcome not necessarily leads to the optimal solution.
• Simplifying assumptions, e.g. linearity, types of distribution or independence, are not required. This results in more realistic models and enables more complex interactions.
• Due to a simulation model’s experiment ability different levels of detail and sensitivity analysis become possible, e.g. by changing structures or types of distribution.

Question 4

ntages of modeling and simulation

Answer 4

• Capability for modeling highly complex dynamic systems;
• Potential for improved understanding of system behavior (e.g. logical model structures, recognizing inconsistencies and missing information);
• Capability to offer alternatives to experimenting with real systems (e.g. when the manipulation of real systems is impossible or too expensive);
• Capability to derive conclusions associated with complex strategies (decision support);
• Potential for timely warning of undesirable consequences of planned changes in complex systems (e.g. risk of shortfalls);
• Capability to use models as model components (using sub-models);
• Chance for collecting reliable and comprehensive data that can be reused regardless of the purpose of the simulation study.

Question 5

Limitations of modeling and simulation

Answer 5

To correctly understand simulation, not just chances but also potential

risks need to be considered. This includes:

• Conceptual difference between model and reality
• Overestimation of the model in terms of its accuracy and significance
• Lack of transparency in model development
• Inadequate input data lead to poor quality simulation results
• Tendency towards modeling errors eventually causing severe consequential errors
• In long-term projects interim changes in the real system might lead to the invalidiy of an almost finished model
• Simulation outcome are computer-generated data, which therefore are erroneously considered objective

Question 6

Basic Simulation Tools:

Answer 6

• Time advance mechanism : Time advances mechanism is a science of dynamic system which depicts the time dependence of a point and it can be studied by two categories; the first one is discrete event simulation (DES) model (next event time) and the second one is discrete time simulation (DTS) model (time-step). Models under time advances mechanism are dynamic and facilitate stimulates time for the values.
• Random number generation: This tool is used to represent the randomness to a certain point in the simulation model. For example by using distribution functions the generator provides numbers for this distribution.
• Output data analysis: Simulation software provide normally with output analysis tools to investigate the results produced by a simulation run.

Question 7

Procedure and phases of a simulation project ( VDI Version)

Answer 7

Question 8

Model building, implementation, and validation

Answer 8