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 and research on parallel execution by use of the Statistical Synchronization Method approach.
