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https://github.com/g-s01/passenger-recovery-optimization

IIT Guwahati's Submission for Mphasis Problem Statement, Inter IIT Tech Meet 13.0
https://github.com/g-s01/passenger-recovery-optimization

dwave-quantum-annealing python3 quantum-computing streamlit

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IIT Guwahati's Submission for Mphasis Problem Statement, Inter IIT Tech Meet 13.0

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README 

        
# Passenger-Recovery-Optimization



Airlines routinely change their flight schedules for various reasons like seasonal demands, picking new routes, time changes needed based on daylight savings, changes to flight numbers,

operating frequency, timings, etc. Many passengers get impacted due to these schedule

changes and they need to be regularly re-accommodated to alternate flights. 



The project uses a advanced and robust **Hybrid Classical-Quantum Solution** to identify optimal alternate flight solutions for all impacted passengers based on certain constraints and considering various factors like PNR priority, time to reach the destination, and special passenger types without jeopardizing the flight experience for the passengers.



## Final Report

[Report Link](https://drive.google.com/file/d/1UR59ydUBvfly3OPW83lyi90H74wFzNZg/view?usp=drive_link)



## Business Rules Engine using Streamlit



We have provided a GUI, deployed using `Streamlit` to provide **flexibility** and easily modify the ruleset containing various scores for each attribute used to calculate PNR scores, flight grades, class allocation, etc.



## Deployment



### Installing the dependencies

**Make sure that you have `Python 3.10` installed in your system**



Run the following command in the terminal

```

pip install -r requirements.txt

```



### Setting up the environment file



Make a file named `.env` in the main folder, it's content should be: (It has already been made in this case though)

```

GMAPS_API_KEY=your_key

DWAVE_TOKEN=your_token

flight_mail=your_mail

flight_mail_password=your_password

```

`GMAPS_API_KEY` can be obtained from the `First Distancematrix accurate application` API of [Distance Matrix](https://distancematrix.ai/)



`DWAVE_TOKEN` can be obtained from [here](https://cloud.dwavesys.com/leap/)



### Running the program

Now, to run the script enter the following command in the terminal inside the folder where this folder is downloaded

```bash

streamlit run Landing_Page.py

```

(if it doesn't work then):



```bash

python3 -m streamlit run Landing_Page.py

```

(if it doesn't work then):



```bash

python -m streamlit run Landing_Page.py

```



## Streamlit Instructions:

1. Go to PNR Ranking Score tab in the Streamlit webapp and choose the dataset u want. The number in bracket indicates the number of Impacted PNRs.

2. Change the values if needed using slider and click **Proceed** (IMPORTANT)

3. Go to the Next page Cabins and Classes and do the same. Click **Proceed** when done.

4. Go to the next page flight quality score and do the same. Click **Proceed**.

5. Please note that you will have to click on **Rerun**. located at top right section everytime you click **Proceed**.

6. Then come to **Landing Page** and click Run Code. You can disable and enable accordingly if u need different city pairs or not.

7. Go to **Solution 1** , **Solution 2**, **Solution 3** and **Different City Pairs** to see the different solution files.

8. Click on **Statistics** to view the statistics.



## Solution Files:

1. `result_quantum_0.json`, `result_quantum_1.json`,`result_quantum_2.json` are the different solution files with **same** city pairs

2.  `exception_list_0.json`, `exception_list_1.json`,`exception_list_2.json` are the different solution files with **different** city pairs

3.  `non_assignments_0.txt`, `non_assignments_1.txt`,`non_assignments_2.txt` are the 3 non assigned list of PNRs corresponding to the 3 solutions.



*Note: These have been provided with the code for example inside `Example-soln-files`. However, on running, they are generated according to the dataset used in the parent folder.*



## Working



Each file contains widgets to adjust various parameters along with some default values.There is also a button indicating wheather we want to allow flights to adjacent city pairs in main page. On pressing the proceed button at the bottom of each page, all the values chosen are written into a python file as variables which can be imported for use in other programs.



### Landing_Page.py

- Used to generate different solution files and send email to the passengers.

- **Option to add different city pair solution**

  

![landing_page](./assets/Landing_Page_SS2.png)



 ### PNR_Score.py

- Used to modify scores for PNR priority calculation parameters like PAX, loyalty, classes, MCT, MAXCT, ETD, etc

- **writes to constants.py**

  

![PNR Scores](./assets/PNR_Ranking_SS2.png)



### Cabin_And_Classes.py

- Used to allow/disallow class changes during flight allocation and upgrade/downgrade rules

- **writes to classRules.py**

  

![Cabins and Classes](./assets/Cabin_Score_SS2.png)



### Flight_Score.py

- Used to modify parameters used to rank the flight and allocate grades like arrival delay, STD and citypairs

- **writes to flightScores.py**

  

![Flight Scores](./assets/Flight_Score_SS2.png)



### Solution_1.py

- Used to display PNR wise re-accomodation solution 

- **displays only after generating solution files**



Following this, we have 2 more pages corresponding to the other two solutions.

  

![Solution File](./assets/Solution_File_SS2.png)



### Statistics.py

- Used to display various plots which helps compare different solutions by our model.

- **displays only after generating solution files**

  

![Statistics](./assets/Statistics_SS2.png)



## Credits



* [Gautam Sharma](https://g-s01.github.io/)

* Sreehari Chandran

* Dhanesh Vijay

* Shivam Garg

* Aryan Lath

* Shivam Agrawal

* Ayush Savar

* Ayush Kumar

* Rahul Mareja

* Saundarya Keshari