Customer service operates on a First Come – First Served principle, depending on whether there is an available channel. The application measures the following values: the average waiting time of customers in the service queue; - the average service time of customers; - the average time in the system (waiting + service); - server utilization in percentage; - and throughput (customers per unit of time).
The data of the simulated systems are stored in an SQLite database named samples.db. The list of already stored systems is displayed on the application's main screen, named AppMulti_Channel_Mass_Service, and by clicking on an item from the list, it is selected for further work.
From the application's main screen, the following functions are available: New Sample – to enter data for a new system simulation; Edit – to modify and execute a selected system; and Delete – to remove a system.
In addition to the menu items on the home screen, the following functions are included: Help; - Init DB initial loading of the database; - Copy DB copying the database; - Save DB saving the database; - Settings; - and Links to the author's other apps.
Data entry for a new system for simulation and for editing and running a selected system is done from the screen named Sample Activity. Here you enter: - the system name; - the number of servers; - the number of clients to simulate and both probability distributions (of arriving and serviced clients).
There are two fields for visualizing the distributions: Interarrival PMF format value:prob,... and Service time PMF format value:prob,... The data entry itself is done in dialog tables (Edit; Interarrival PMF Edit; and Service time PMF) with two columns: interval and probability each. After pressing the Save button, the entered data is displayed in the aforementioned fields.
From Sample Activity, the functions for generating the two distributions are included with the Generate Input and Generate Service buttons, as well as performing the simulation with the RUN SIMULATION button.
After the simulation is executed, the result is displayed on the Simulation screen. From there, the Print function can be selected to save the simulation result as a .txt file. Print includes the Save File activity with a tree structure of the device's file directory, and upon selecting a folder, a Save button appears, which allows saving the simulation result.
The generation of the two distributions is carried out by the FlowActivity. From a dropdown list, the type of distribution is selected, its characteristic parameters are filled in, and with the Generate button, in a similar two-column table as when entering new distributions, the generated distribution data is displayed. Here, the data can also be edited and saved with the Save button, which sends it to Sample Activity in the corresponding visualization field.
The system prepared in this way through Sample Activity can be saved with the Save button, while the Load button fills in the data in Sample Activity if a system is saved, and New Sample clears the distribution fields when starting fresh.
Here’s a clear, quick overview of Weibull, Erlang, Uniform and Normal Distributions used in app generation of Interarrival PMF( Probability Mass Function) or Service time PMF flow:
Weibull Distribution:The Weibull distribution is a continuous probability distribution widely used in reliability engineering, survival analysis, and failure-time modeling.
Erlang Distribution:The Erlang distribution is a continuous probability distribution used mainly in queueing theory, telecommunications, and reliability analysis. It models the waiting time until the k-th event occurs in a Poisson process.
Normal Distribution: The Normal distribution is a continuous probability distribution that is symmetric and bell-shaped, widely used in statistics, natural sciences, engineering, and social sciences.
Uniform Distribution:A uniform distribution means all values in an interval are equally likely.
Truncated Normal Distribution:A truncated normal distribution is a normal distribution restricted to an interval [a,b][a, b][a,b].
Values outside this range are discarded, and the remaining probabilities are renormalized.
Shape parameter: k>0
Scale parameter:lambda > 0 λ>0
k<1: Decreasing failure rate (early failures)
k=1 Constant failure rate (equivalent to exponential distribution)
k>1: Increasing failure rate (wear-out failures)
Mean: μ\muμ
Standard deviation: σ>0\sigma > 0σ>0