Topic 3: Introduction to Modeling and Simulation

1940, 1960, 1970, 1980, 1990

History of Simulation

agent-based simulation (ABS), discrete-event simulation (DES)

Two common simulation methods applied in operational management systems:

Agent-based simulation (ABS)

A fast-developing modeling and simulation method that can be used to model and simulate industrial process and complex scientific systems.

Scoring Validation

A scoring model is used to determine whether a simulation model is valid or not.

Bratley et. al.

According to him, modeling and simulation is a process of driving a model of a system with suitable inputs and observing the correspondingly outputs.

Shannon

According to him, modeling and simulation is the process of designing a model of a conceptual system and using it to conduct experiments for the purpose of understanding the performance of the system and/or evaluating alternative management strategies and decision-making processes using simulation results [1, 2].

Discrete-event simulation (DES)

A more mature simulation method than agent-based simulation. It is one way to build up models in a top-down architecture and observe time-based behaviors within a system.

Independent Validation

An independent third party is employed to decide whether a simulation model is valid or not.

1980

During this period, PC-based simulation software, graphical user interfaces and object-oriented programming were developed.

1970

During this period, research was initiated on mathematical foundations of simulation.

1990

During this period, web-based simulation, fancy animated graphics, simulation-based optimization, Markovchain Monte Carlo methods were developed.

physical mechanisms, process-based systems

In engineering, modeling and simulation techniques are applied to two distinct types of system:

Mechanism simulation

It relates to the simulation of physical systems, through which movement, degree of freedoms (DOFs), velocities and component stresses can be simulated and analyzed for whole machine optimization.

Military applications, training & support, designing semiconductors, telecommunications, civil engineering designs & presentations, and E-business models

Modeling & Simulation can be applied to the following areas:

Easy to understand, Easy to test, Easy to upgrade, Easy to identifying constraints, Easy to diagnose problems

Modeling & Simulation ─ Advantages

Requires domain knowledge, Difficult to predict the result, Time-consuming, Difficult to translate, Expensive

Modeling & Simulation ─ Disadvantages

System entities, Input variables, Performance measures, and Functional relationships

Simulation models consist of the following components:

Identify the problem, Design the problem, Collect and start processing the system data, Develop the model using network diagrams, Validate the model, Create a document of the model, Select an appropriate experimental design, Induce experimental conditions on the model

Steps in Developing Simulation Models (8)

Prepare a problem statement, Choose input variables and create entities, Create constraints on the decision variables, Determine the output variables, Collect data from the real-life system, Develop a flowchart, Choose an appropriate simulation software, Verify the simulation model, Perform an experiment on the model, Apply results into the real-time system

Steps in Performing Simulation Analysis (10)

Simulation

The operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system. In other words, simulation is the process of using a model to study the performance of a system. It is an act of using a model for simulation.

1940

The period when a method named 'Monte Carlo' was developed by researchers (John von Neumann, Stanislaw Ulan, Edward Teller, Herman Kahn) and physicists working on a Manhattan project to study neutron scattering.

1960

The period when the first special-purpose simulation languages were developed, such as SIMSCRIPT by Harry Markowitz at the RAND Corporation.

Modeling

The process of representing a model which includes its construction and working. This model is similar to a real system, which helps the analyst predict the effect of changes to the system. In other words, modelling is creating a model which represents a system including their properties. It is an act of building a model.

performance assessment, proof, prediction, discovery, training, entertainment and education

The purpose of modeling and simulation (7)

Self-Validation

The simulation model development team itself makes the decision as to whether a simulation model is valid or not.

Co-Validation

The simulation team involves model users within model development process; the model validation process is integrated within the model development process.

Self-Validation, Co-Validation, Independent Validation, Scoring Validation

There are four primary strategies used to verify and validate simulation models: