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Time advance
Mechanism
NIKHIL SHARMA | CO15333
Track
 WE are here ( Discrete system model)
Time advance mechanism
- Discrete event simulation
models are dynamic in nature.
- Time advance mechanism
advances simulated time from
one value to another.
- Simulation clock gives the
current value of simulated time
the.
 Next event time advance
 Fixed increment time advance
Time advance mechanism
Next event time advance
 Determine time of occurrence of future events from event list
 Clock advances to next (most Initialize simulated clock to 0
 imminent ) event , which is executed
+ Event execution may involve updating of the list
// obviously we are updating because of arrival and departure procedures
 Continue until stopping rule is satisfied
// As it is algorithm it must stop
//either all events arrived or departed then we are left with nothing
// No of customer delayed or served
 Clock jumps from one period of time to the next and period of inactivity are ignored
Time advance mechanism
Fixed event time advance
With this approach the simulation clock is advanced with increment of exactly
∆t time units.
for some appropriate ∆t after each update of the clock,
a check is made to determine
If any event should have occurred during the previous interval of length ∆t.
If one or more events were scheduled to have occurred during this interval then those events are considered to
occur at the end of the interval at system state.
← Next Event time
advance
Fixed Event time
advance →
COMPONENT OF A DES MODEL
It comes with certain common components.
Most discrete- event simulation models using next-event time –advance approach…
So the common component must be
System state
Simulation clock
Event list
Statistical counter
Initialization routine
Timing routine
Event routine
Library routines
Report generator and Main program
System state:
COLLECTION OF STATE VARIABLES NECESSARY TO DESCRIBE THE SYSTEM STATE
Simulation Clock:
A variable giving the current value of stimulation time.
Event list:
A list containing the next time when each type of event will occur.
Statistical counter :
TO ACCUMULATE QUANTITIES FOR OUTPUT
Initialization routine:
Initialize the clock at 0.
Timing routine:
Determine next event time, type } advancing the clock
Kindly check the changing
values
Event routine:
Carry out logic for each event type
Library routine:
Utility to generate random variants.
Report generator:
To summarize, report result at end
SO…
Time advance mehcanism

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Time advance mehcanism

  • 2. Track  WE are here ( Discrete system model)
  • 3. Time advance mechanism - Discrete event simulation models are dynamic in nature. - Time advance mechanism advances simulated time from one value to another. - Simulation clock gives the current value of simulated time the.  Next event time advance  Fixed increment time advance
  • 4. Time advance mechanism Next event time advance  Determine time of occurrence of future events from event list  Clock advances to next (most Initialize simulated clock to 0  imminent ) event , which is executed + Event execution may involve updating of the list // obviously we are updating because of arrival and departure procedures  Continue until stopping rule is satisfied // As it is algorithm it must stop //either all events arrived or departed then we are left with nothing // No of customer delayed or served  Clock jumps from one period of time to the next and period of inactivity are ignored
  • 5. Time advance mechanism Fixed event time advance With this approach the simulation clock is advanced with increment of exactly ∆t time units. for some appropriate ∆t after each update of the clock, a check is made to determine If any event should have occurred during the previous interval of length ∆t. If one or more events were scheduled to have occurred during this interval then those events are considered to occur at the end of the interval at system state.
  • 6. ← Next Event time advance Fixed Event time advance →
  • 7. COMPONENT OF A DES MODEL It comes with certain common components. Most discrete- event simulation models using next-event time –advance approach… So the common component must be System state Simulation clock Event list Statistical counter Initialization routine Timing routine Event routine Library routines Report generator and Main program
  • 8. System state: COLLECTION OF STATE VARIABLES NECESSARY TO DESCRIBE THE SYSTEM STATE Simulation Clock: A variable giving the current value of stimulation time. Event list: A list containing the next time when each type of event will occur.
  • 9. Statistical counter : TO ACCUMULATE QUANTITIES FOR OUTPUT Initialization routine: Initialize the clock at 0. Timing routine: Determine next event time, type } advancing the clock
  • 10. Kindly check the changing values
  • 11. Event routine: Carry out logic for each event type Library routine: Utility to generate random variants. Report generator: To summarize, report result at end
  • 12. SO…

Editor's Notes

  1. -Certainly we are starting with 0 time -Particularly saying we have Event list we look into it Event list may be in Arrival and Departure manner So when the event is picked then we certainly start the clock from 0 itself and clock advances to next most imminent( likely to be happen) event which is executed.
  2. -Certainly we are starting with 0 time -Particularly saying we have Event list we look into it Event list may be in Arrival and Departure manner So when the event is picked then we certainly start the clock from 0 itself and clock advances to next most imminent( likely to be happen) event which is executed.
  3. System state: We are dealing with particular time/scenario . So we have certain values in state variables to represent the state of system. Event list: Basically arrival time and departure time generally
  4. Stat Varaible to store the system statistic >
  5. Event routine : A sub program that will update the system state by changing it’s variable when a particular type of event will occur like we have two separate subroutines for arrival and departure. Library routine: A subprogram to deal with generating random variants to find the performance of the simulation.