We follow a object oriented approach for creating a general Queueing System and getting simulated wait time data. Further, we are also going to change the linear model to incorporate other ML techniques and hopefully create a better predictor.

Check out our code repository here.(Currently in work).

Tutorials

Please visit our examples for various application.

Following are few examples exaplained briefly.

Simulation Models

We follow a object oriented approach for creating a general Queueing System. Three main classes are declared in the components folder:

station : A single queueing system. You can define the number of servers (even dynamic i.e. changing with time).

Also you can provide custom departure time distribution by providing the inverse distribution function ( pass a function { float -> float } to station class that generates the random service times).

You'll have to use one the following constructor:
```
station (long init_mxN, C_type C_para, event_type dept_para, float t=0, int init_n=0)

station (long init_mxN, C_type C_para, float init_dept, float t=0, int init_n=0)

station (long init_mxN, int init_C, float init_dept, float t=0, int init_n=0)

station (long init_mxN, int init_C, event_type init_dept, float t=0, int init_n=0)
```
Here C_type is Function type float -> int and event_type is Function type float -> float.

Example of simple queueing system and more information on how to simulate can be found in examples folder.
tandem : You can pass in a vector of station objects to group multiple queueing system in series.
graph : Currently in work but the idea is to create a directed acyclic graph of station objects.

If you have already generated distribution for arrival or service times you can use,

std::vector<float> read_csv(std::string filename,int index); //index is the column to be read.

to read the data out of a csv file. and then the data can be used in our model in the following way:

std::vector<float> service_times = read_csv("lognormal.csv",1);
std::vector<float> interarrivaltimes = read_csv("lognormal.csv",2);

station MG1(1,1,
[service_times](float t) -> float
{
    float U = random;
    int index = (int)(U*service_times.size());
    try
    {
        return service_times[index];
    }
    catch(const std::exception& e)
    {
        // std::cerr << e.what() << '\n'; 
        return service_times[index-1];
    }
});

Project by-

Nalin Shani Nalin.nosp@m..Sha.nosp@m.ni.me.nosp@m.118@.nosp@m.mech..nosp@m.iitd.nosp@m..ac.i.nosp@m.n

Achintya Eeshan me118.nosp@m.0094.nosp@m.@iitd.nosp@m..ac..nosp@m.in

B.Tech IIT Delhi Mechanical Engineering