A computer simulation also known as a computer model or a computation model is a computer model that principally is meant to imitate an abstract model of a particular given system. Many natural system are conjured up from computer simulation e.g. in computational physics, chemistry and biology. Also in human Tertiary Sector In Indonesia system such as economical, psychological, sociological models and many other social sciences models. Basically, simulations are used to give a visible idea of a new technology, and further estimate the performance and speed of a system which might be difficult to do with use of analytical approaches.

Computer simulation involve generation of inputs from simulated objects e.g. flight simulators which run modeled items and at the same time an actual flight software to aid better understanding to the users. There are many different types of computer simulations, classified according to independent attributes, which include;

1. Stochastic or deterministic simulations; in this type of simulation, the next event is determined by ordering an array of the rates of all possible changes followed by taking a cumulative sum of the same array. The cell containing the number R (total event rate) is taken. This further makes a discrete cumulative probability distribution in which the next event can be chosen by picking a random number z ~U (0.R) and thus get the first event such that z is less than the rate associated with the event.

2. Continuous or discrete simulation model: Discrete event simulation manages events according to time e.g. number of cars arriving and leaving a petrol station at a particular time interval. The model maintains a queue of events in accordance with the set criterion. The simulator gets instruction from the queue and triggers new events as the previous ones are being processed. Most computers and fault-free simulation use this kind. On the other hand, continuous dynamic simulation is used where the state changes all the time e.g. the water level in a tank with inflows and outflows might keep on changing every time. It basically, performs differential equations. The model solves all the equations and uses the processed numbers to change the output of the final simulation. This kind is used in flight simulation, chemical modeling, car-race games and also electric circuit simulation.

3. Steady- state or dynamic simulation: this kind uses equations that show relationship between variables in the system and further gives an equilibrium state of the modeled system. For example steady state simulation can be used in pipeline aided simulations

4. Distributed simulation: this kind run on a network of linked computers via the internet. Simulations are dispersed to host computers and there are set protocols for this i.e. the Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS) and also the High Level Architecture (HLA).

5. Agent-based distribution: this is a special type of discrete type simulation which doesn’t rely on equations, but basically represented formally. In this independent entities like molecules, consumers, Porter’S Five Forces Starbucks trees. Cells e.t.c. are …

# Tag: simulation

## An Introduction to Computer Simulation

A computer simulation also known as a computer model or a computation model is a computer model that principally is meant to imitate an abstract model of a particular given system. Many natural system are conjured up from computer simulation e.g. in computational physics, chemistry and biology. Also in human Tertiary Sector In Indonesia system such as economical, psychological, sociological models and many other social sciences models. Basically, simulations are used to give a visible idea of a new technology, and further estimate the performance and speed of a system which might be difficult to do with use of analytical approaches.

Computer simulation involve generation of inputs from simulated objects e.g. flight simulators which run modeled items and at the same time an actual flight software to aid better understanding to the users. There are many different types of computer simulations, classified according to independent attributes, which include;

1. Stochastic or deterministic simulations; in this type of simulation, the next event is determined by ordering an array of the rates of all possible changes followed by taking a cumulative sum of the same array. The cell containing the number R (total event rate) is taken. This further makes a discrete cumulative probability distribution in which the next event can be chosen by picking a random number z ~U (0.R) and thus get the first event such that z is less than the rate associated with the event.

2. Continuous or discrete simulation model: Discrete event simulation manages events according to time e.g. number of cars arriving and leaving a petrol station at a particular time interval. The model maintains a queue of events in accordance with the set criterion. The simulator gets instruction from the queue and triggers new events as the previous ones are being processed. Most computers and fault-free simulation use this kind. On the other hand, continuous dynamic simulation is used where the state changes all the time e.g. the water level in a tank with inflows and outflows might keep on changing every time. It basically, performs differential equations. The model solves all the equations and uses the processed numbers to change the output of the final simulation. This kind is used in flight simulation, chemical modeling, car-race games and also electric circuit simulation.

3. Steady- state or dynamic simulation: this kind uses equations that show relationship between variables in the system and further gives an equilibrium state of the modeled system. For example steady state simulation can be used in pipeline aided simulations

4. Distributed simulation: this kind run on a network of linked computers via the internet. Simulations are dispersed to host computers and there are set protocols for this i.e. the Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS) and also the High Level Architecture (HLA).

5. Agent-based distribution: this is a special type of discrete type simulation which doesn’t rely on equations, but basically represented formally. In this independent entities like molecules, consumers, Porter’S Five Forces Starbucks trees. Cells e.t.c. are …

## Discrete-Event Simulation: An Overview

The operation of a system in discrete event simulation is based on sequence of events that are in order. An event occurring at a given time marks a change of state in the system. An instance like this can be depicted by an elevator scenario. If an elevator is modeled the prime event maybe pressing level 6 button which results in a change of the state i.e. the press triggers the lift to start moving, unless you want to play with people’s mind and let the pseudo code trigger the lift to open its door.

A simpler event simulator is an interaction between the customer of a bank and the teller. In such an example we have random variables that need to be inputted in the system i.e. customer-interarrival time and the teller service time Competitor Response Profile Definition (when idle and when being accessed). The events here are the customer queue and the tellers themselves. The change of state is the number of customers in the queue (from 0 to infinity) and the teller status – either working or idle.

A discrete event simulation has different components which include the clock; it keeps track of the current model time, under instantaneous events the clock will hop to the next event as the simulation progresses. A list of events; their will basically be a queue of events that will require their time to be simulated. These events will be categorized as pending events organized as priority queue regardless of their order. But what will happen when the events listed are scheduled dynamically as the simulation proceeds? This can be better explained by our bank example, let’s say the customer queue was empty and the teller was idle, then another event consisting of customer- departure will have to be created to occur at a time t+s, s being a number spawned from the service-time distribution.

Another important component is a random-number generator which is basically accomplished by pseudorandom number generator. Manufacturing Strategies In Supply Chain This generator is a necessity particularly if the systems need a rerun to produce other random numbers.

A typical example of a discreet event simulation system is the OMNET++ which is a C++ based discrete event simulation package which was developed with the aim of simulating computer distributed systems. OMNET++ is an open source package that was engineered to fit in research and education modeling, this is because it was made under a powerful platform that includes a perfect user interface which enables the user to visualize every modeling he undertakes, it also offers easy traceability and debuggability of the models.

Since its inception in September 1997, it has seen a greater appreciation by many institution that are in need of discrete event simulation system such as optical network simulation, hardware simulation, queuing system and also the ATM. A typical adopted application was the developments of a complete TCP/IP model by the University of Karlsruhe. This was particular enumerated to manage remote simulations on flock workstations …