How to run ?

Create a virtual environment

Activate virtual environment

Linux

source venv/bin/activate

Windows

venv\Scripts\activate.bat

Install requirements
```
pip3 install -r requirements.txt
```
Make sure to migrate first
```
python3 manage.py migrate
```
Generate/Load data or use the existing db.sqlite3 file.
- Refer to the Data Ingestion section below for more details.
Cancel the flights
- Use the following command to cancel x% of total flight randomly
```
python3 manage.py cancel --percent 0.005
```
- For more controlled cancellation, open the database in DB Browser for SQLite and change the status of the flight to Cancelled in the flight_flight table.
Run reallocation
- Use the following command to run the reallocation
```
python3 manage.py allocate --save "result.csv" --config "settings.yml"
```
- This accepts two optional arguments
  - --save : to save the result in a csv file, default is result.csv
  - --config : to specify the config file, default is settings.yml
- For more information on the config file refer to the Buisness Rules section below.
Run Post analysis
- Three file should be generated, result.csv for pnr-flight allocation, result.txt for summary and result.flight_stats.csv for per flight stats.
- This contains default solutions.
- Alternatively Use the post_analysis.ipynb notebook to analyse the results.
- The second cell contains the file name of the result file, change it accordingly.
- Run all!!

Data Ingestion

Data Mapping

This section describes how we mapped the given data to the database tables.

Assumptions:

The flight_number is unique for each flight.
Aircraft's are unique ( from given data mapped using tail number )
The term Flight is used to refer Inventory
Multiple class belongs to a single cabin
PNR are mapped to class
- Default cabin to class mapping have been defined
SSR, value added service etc are mapped to passengers and pnr scores are calculated on the time of ingestion

How to run ?

The settings.yml file contains some necessary information for data ingestion. Please strictly follow the format and naming conventions.
- For more information on the config file refer to the Buisness Rules section below.
Run the flight/management/data/generate_basic.ipynb to generate some p reprocessed csv files.
Run the following command to finally ingest data.

python3 manage.py flush --noinput && python3 manage.py load_data

Data Generation

This sections describes how we generated data and how to generate data in same way.

The settings.yml file contains some necessary information for data generation. Please strictly follow the format and naming conventions.
- For more information on the config file refer to the Buisness Rules section below.
- The config["data_generation"]["aircrafts"] defines the number of aircrafts to be generated
  - Prn ,Flight counts probabilistic proportional to the number of aircrafts
  - For refference, we created 1100 aircrafts
  - Accordingly it generated 81k+ flight, 5.9M+ pnr and 13.6M+ passengers.
  - Causing 2.5GB+ of data ( takes 1-1:30 hours to generate )
Run the flight/management/data/generate.ipynb to generate some csv
- This will generate the following files
  - flight/management/data/in-cabin.csv
  - flight/management/data/in-class.csv
  - flight/management/data/in-ssr.csv
  - flight/management/data/in-group.csv
  - flight/management/data/in-aircraft.csv
Table : airport
- source : flight/management/commands/in-airport.csv
  - url
- model : Airport
- table_name: flight_airport
- command:
```
python3 manage.py populate --filename flight/management/data/in-airports.csv --delete
```
Table : Cabin Types, Class Types, SSRs, Groups
- source :
  - flight/management/commands/in-cabin.csv,
  - flight/management/commands/in-class.csv,
  - flight/management/commands/in-ssr.csv,
  - flight/management/commands/in-group.csv
- model : Cabin, Class, SSR, Group
- table_name: flight_cabin, flight_class, flight_ssr, flight_group
- command:
```
python3 manage.py populate_baisc
```
- For table cleanup use
```
python3 manage.py populate_baic --clean
```

Table : Aircraft

model : Aircraft
command:

python3 manage.py populate_aircraft --clean

Table : Flight, FlightScheuleDate, FlightScheule
- model : Flight, FlightScheuleDate, FlightScheule
- command:
```
python3 manage.py populate_flight --clean
```
Table : PNR, PnrFlightMapping, PnrPassenger
- model : PNR, FlightPnrMapping
- table_name: flight_pnr, flight_flightpnrmapping
- command:
```
python3 manage.py populate_pnr
```
- For table cleanup use
```
python3 manage.py populate_pnr --clean
```

Note

for data ingestion use this order, first airports, then basic, routes, then pnr

Buisness Rules

Buiness rules are defined in settings.yml file ( or any other file specified in the --config argument of the allocate command )

Note: File extension must be `.yml`

Example config file

data_generation:
  safe: False # checks before inserting data in db if set True
  aircrafts: 1100
  carriers: 15
  flights_per_aircraft: 15
  flights_per_aircraft_margin: 3
  flight_schedule_prefix : "SCH-ZZ-"
  paid_service: 0.08
  paid_service_score: 200
  loyality_program_score: 2000
  loyality_program: 0.003 # PROBABILITY
  pax_score: 50
  ssr_score: 100
  default_ssr_probability: 0.001
search:
  max_hop : 2 # max limit 2 
  downgrade: True
  upgrade: True
  skip_quantum: False # reduce time ( tries to do the same thing in classical way ) when set True
  use_inventory: False # calculate on inventory instead of capacity
  use_cabin_only: True
  neighboring_search: True # searh using same state airport
  alpha : 4
  beta : 0.3
scores:
  connecting_score: 100
  default_ssr_score: 200
  default_scd1_score: 300
  default_scd2_score: 200
  cabins:
    F: 2000
    B: 1000
    P: 500
    E: 100
  loyalty:
    Silver: 1000
    Gold: 2000
    Platinum: 3000

There are 3 sections in the config file:
- data_generation : contains the parameters for data generation
  - Only used in data generation, can be skipped for pnr allocation
  - All fields are compulsory
- search : Contains the parameters for search or pnr reallocation
  - max_hop : maximum number of hops/connection allowed
  - downgrade : if set to True allows downgrading are allowed
  - upgrade : if set to True allows upgradings are allowed
  - skip_quantum : if set to True skips the quantum search and uses classical search
  - use_inventory : if set to True uses avilable inventory instead of avilable capacity
  - use_cabin_only : if set to True uses cabin only for search (If set to false pnrs should be mapped to class instead of cabin )
  - neighboring_search : if set to True uses neighboring airports for search, ( Neighboring airports are defined as airports in same state )
- scores : This part is important for PNR scoring
  - connecting_score : score for connecting flights
  - default_ssr_score : score for default ssr
  - default_scd1_score : score for default scd1
  - default_scd2_score : score for default scd2
  - cabins : score for each cabin (edit the class.csv for class level scoring)
  - loyalty : score for each loyalty program

High Level Schema

API Endpoints

Note

These api endpoints were used for our early development and testing.
Currently these apis are called internally (without http)
Still can be used for debugging and testing purposes.
Server must be running to use these apis.
Run python3 manage.py runserver to start the server.
Host address : http://127.0.0.1:8000/

API Doc

\canclled

Returns the list of cancelled flights
Method: GET
Filters by status = Cancelled
Sample Response:

{
"count": 23,
"data": [
    {
        "flight_id": "INV-ZZ-4593004",
        "departure_airport": "AMD",
        "arrival_airport": "HYD",
        "status": "Cancelled",
        "arrival_time": "2024-05-21T13:37:00Z",
        "departure_time": "2024-05-21T04:59:00Z"
    }, ...
]
}

\pnr_ranking?flight_id={id}

Returns the list of passengers in the flight with the given flight_id
Method: GET
Filters by flight_id
Sample Response:

{
"data": [
    {
        "pnr": "ZAYQ95",
        "score": 20000,
        "class": "R",
        "pax": 9
    },
    {
        "pnr": "BMHR27",
        "score": 16000,
        "class": "R",
        "pax": 7
    },...
]
}

\alt_flight?flight_id={id}

Returns the list of alternative flights for the given flight_id
Method: GET
Filters by flight_id and status = Scheduled
Sample Response:

{
"data": [
    {
        "flight_id": "INV-ZZ-9565992",
        "departure_airport": "HYD",
        "arrival_airport": "AMD",
        "status": "Scheduled",
        "arrival_time": "2024-05-22T05:58:00Z",
        "departure_time": "2024-05-21T08:26:00Z",
        "total_avilable_seats": 160,
        "delay": "3:27:00",
        "flight_time": "21:32:00",
        "avilable_seats": {
            "C": 35,
            "R": 56,
            "E": 51,
            "F": 18
        },
        "n_score": 0
    },...
],
"r_flights": [
    {
        "flight_id": "INV-ZZ-4593004",
        "departure_airport": "AMD",
        "arrival_airport": "HYD",
        "status": "Cancelled",
        "arrival_time": "2024-05-21T13:37:00Z",
        "departure_time": "2024-05-21T04:59:00Z"
    }
],
"c_flights": [
    [
        {
            "flight_id": "INV-ZZ-6352197",
            "departure_airport": "AMD",
            "arrival_airport": "MAA",
            "status": "Scheduled",
            "arrival_time": "2024-05-22T16:15:00Z",
            "departure_time": "2024-05-22T13:07:00Z",
            "total_avilable_seats": 141,
            "delay": "1 day, 8:08:00",
            "flight_time": "3:08:00",
            "avilable_seats": {
                "C": 34,
                "R": 53,
                "E": 39,
                "F": 15
            }
        },
        {
            "flight_id": "INV-ZZ-4861936",
            "departure_airport": "MAA",
            "arrival_airport": "HYD",
            "status": "Scheduled",
            "arrival_time": "2024-05-23T18:28:00Z",
            "departure_time": "2024-05-23T08:12:00Z",
            "total_avilable_seats": 78,
            "delay": "15:57:00",
            "flight_time": "10:16:00",
            "avilable_seats": {
                "C": 19,
                "R": 29,
                "E": 22,
                "F": 8
            }
        }
    ],...
],
"t_flights": [
    [
        {
            "flight_id": "INV-ZZ-6352197",
            "departure_airport": "AMD",
            "arrival_airport": "MAA",
            "status": "Scheduled",
            "arrival_time": "2024-05-22T16:15:00Z",
            "departure_time": "2024-05-22T13:07:00Z",
            "total_avilable_seats": 141,
            "delay": "1 day, 8:08:00",
            "flight_time": "3:08:00",
            "avilable_seats": {
                "C": 34,
                "R": 53,
                "E": 39,
                "F": 15
            }
        },
        {
            "flight_id": "INV-ZZ-4277207",
            "departure_airport": "MAA",
            "arrival_airport": "BOM",
            "status": "Scheduled",
            "arrival_time": "2024-05-23T15:50:00Z",
            "departure_time": "2024-05-23T09:56:00Z",
            "total_avilable_seats": 34,
            "delay": "17:41:00",
            "flight_time": "5:54:00",
            "avilable_seats": {
                "C": 10,
                "R": 7,
                "E": 14,
                "F": 3
            }
        },
        {
            "flight_id": "INV-ZZ-6250134",
            "departure_airport": "BOM",
            "arrival_airport": "HYD",
            "status": "Scheduled",
            "arrival_time": "2024-05-24T05:44:00Z",
            "departure_time": "2024-05-23T16:54:00Z",
            "total_avilable_seats": 218,
            "delay": "1:04:00",
            "flight_time": "12:50:00",
            "avilable_seats": {
                "C": 50,
                "R": 72,
                "E": 68,
                "F": 28
            }
        }
    ],...
]
}

data : list of no-hop flights
r_flights : list of related cancelled flights ( solution space same )
c_flights : list of 1-hop flights
t_flights : list of 2-hop flights

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README.md

README.md

How to run ?

Data Ingestion

Data Mapping

Assumptions:

How to run ?

Data Generation

Note

Buisness Rules

Note: File extension must be `.yml`

High Level Schema

API Endpoints

Note

API Doc

Files

README.md

Latest commit

History

README.md

File metadata and controls

How to run ?

Data Ingestion

Data Mapping

Assumptions:

How to run ?

Data Generation

Note

Buisness Rules

Note: File extension must be .yml

High Level Schema

API Endpoints

Note

API Doc

Note: File extension must be `.yml`