The paper "Explanation on the Relevance of Simulation Using Spreadsheet" is a perfect example of a business assignment. The calling population for any single channel queue is always infinite, which means that, whenever a unit leaves the entire calling population and enters service or joins the waiting line1, it will never change the rate of arrival of other units which need service. Arrival for each service occurs in a random fashion one at a time. Once they arrive and join the waiting line, they are immediately served. This is made very easy due to the fact that time for services is of random nature according to the probability distribution which never changes over time.
It is so important to understand that, for any multiple or single queue, the overall arrival rate must not be more than the waiting time or service time. There are some variations on the arrival rate to exceed service time, therefore making the system lack capacity, which intern leads to the essence of using FIFO or no priority method2 in serving each arrival. Having understood the whole concept of queuing, we are going to use an illustration of a University cafeteria to determine the number of tills to operate during lunchtime both in the current position, where 1 till is in operation in comparison to an instance where two tills are in operation.
With an assumption that 100 arrivals were reported every lunchtime for each week, simulation results could give an outcome that can be relied upon in coming up with appropriate decisions. Using the given probability table for both services and inter-arrival distribution, an excel spreadsheet shall clearly be used to simulate the data given which shall be used to interpret the results with various formulae applicable.
By applying the simulation formulae, we shall be able to come up with the concrete analytical conclusion of which of the two options can maximize profit, at the same time reducing costs associated with waiting on the queue, time wasted while waiting to be served and idle time.
Gafarian A. & J. Ancker (1999) Queuing with impatient customers who leave at random.
Law, A., M., &. Kelton, D., (1982) Simulation Modeling and Analysis. McGraw-Hill, New York
Mesut Gunes (2003) Simulation discrete event systematic simulation analysis.
Parkan, C., (1987). Simulation of a Fast-Food Operation where Dissatisfied Customer Renege. The Journal of the Operational Research Society, Vol. 38, No. 2, pp. 137- 14
Warren E., & Patrkan, C., (2006) Optional reneging decisions in queue. Decision, Sc 9.107-119