taguro Flashcards
is the process of representing a model which includes its
Construction and working
modeling
of a system is the operation of a model in terms of time or space, which helps analyze the performance of an existing or a proposed system
simulation
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
1940
The first special-purpose simulation languages were developed,
such as SIMSCRIPT by Harry Markowitz at the RAND Corporation.
1960
During this period, research was initiated on mathematical
foundations of simulation
1970
During this period, PC-based simulation software, graphical
user interfaces and object-oriented programming were developed.
1980
During this period, web-based simulation, fancy animated
graphics, simulation-based optimization, Markov-chain Monte Carlo methods were developed.
1990
a model used to predict the probability of a variety of outcomes when the potential for random variables is present.
monte carlo
2 TYPES OF SIMULATION
EQUATION-BASED SIMULATION
AGENT-BASED SIMULATION
Some simulation models are hybrids of different kinds of modeling methods
multiscale simulation
In the scientific literature, there is another large class of computer simulation called Monte Carlo (MC) Simulations. MC simulations are computer algorithms that use randomness to calculate the properties of a mathematical model and where the randomness of the algorithm is not a feature of the target model
MONTE CARLO SIMULATION
is a purposeful collection of inter-related components working together to achieve some common objective. A system’s components can be other systems, other components, entities, resources, jobs, events or even variables that describe the system’s states
system
by the addition of components or new relationships among existing components. Other aspects of complexity increase with the system’s variability, i.e. the predictable or unpredictable variation of the components or their interrelationships. System
complexity
results from the number of interrelationships between
components. The larger the number of components and the number of relationships, the higher is the degree of combinatorial complexity.
combinatorial complexity
derives from the algorithms required to express the
logic and the temporal interrelationships. These may call for a high degree of abstraction that generates mathematical complexity.
COMPUTATIONAL COMPLEXITY
results from the number and the nature of interrelationships
of the components through time. Communication between components is often bidirectional: for example, in a financial system, a physical flow may generate a financial counterpart in the reverse direction.
DYNAMIC COMPLEXITY
Simulation modeling and analysis is a learning process that iterates through a set of stages with the aim of understanding the behavior of a system.
SIMULATION
DEVELOPMENT
STAGE
studies the dynamic behavior of systems by treating them as having state changes at distinct points of time. The state of the system is described by a set of variables such as the number of customers in the system or the length of a waiting list.
DISCRETE EVENT
SIMULATION
system is one in which important activities of the system completes smoothly without any delay, i.e. no queue of events, no sorting of time simulation, etc. When a continuous system is modeled mathematically, its variables representing the attributes are controlled by continuous functions.
CONTINOUS
SYSTEM
simulation is a type of simulation in which state variables change continuously with respect to time. Following is the graphical representation of its behavior.
CONTINOUS
SIMULATION
