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https://github.com/somjit101/covid-19-optimal-resource-allocation_and_request-classification

A solution developed to Map essential COVID-19 Relief resources to the needy across a city in the most cost-optimal way, and also to classify incoming SOS messages from those in need of help, for organizational and lesser response times.
https://github.com/somjit101/covid-19-optimal-resource-allocation_and_request-classification

cloud-service deep-learning google-or-tools google-universal-sentence-encoder graph-theory linear-programming linear-programming-solver mathematical-optimization natural-language-processing neural-network nlp optimization resource-allocation-algorithm rest-api text-classification

Last synced: 7 months ago
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A solution developed to Map essential COVID-19 Relief resources to the needy across a city in the most cost-optimal way, and also to classify incoming SOS messages from those in need of help, for organizational and lesser response times.

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# COVID-19-Optimal-Resource-Allocation_and_Request-Classification

A solution developed to map essential COVID-19 Relief resources to the needy across a city in the most cost-optimal way, and also to classify incoming SOS messages from those in need of help, for organizational and lesser response times.



## Capabilities

1. __Optimal Resource allocation : --__

   This functionality was designed to ingest dataset provided by the government containing the following data :

    * Available COVID-19 sanitary resources like - Hand sanitizers, Face masks, Gloves, Face Shields etc.

    * Emergency medical resources like COVID-19 hospital beds, Oxygen Tanks, etc. 

    * Available donations of dry ration items for COVID-relief like - Rice, lentils, vegetables, spices etc.

    * Quantity of Supply and Demand of the above resources across the city

    * The name of the locality/business/firm/entity where the above supply/demand is found, locatable on Google Maps.

   

   The tool then attaches a geographical tag (latitude and longitude) to each location. Then it builds a graph network with each location as a node and a supply/demand value associated with each. The cost of each edge is obtained from a configurable distance matrix as required. After the previous steps, the tool suggests a list of optimal resource transfers (according to their specific item category) to minimize the gap between demand and supply with the the following fields :

   * From Location

   * To Location

   * Quantity of Transfer

   * Cost of Transfer



   *This boils down to a LP (Linear Programming) problem and can be posed in the standard form.* 



2. __Automatic SOS Text Classification__



  During the COVID-19 pandemic, the end-users are given a free-text field to write and submit their grievances, medical emergencies and relief requests to the state government. This data is collected, pre-processed and each request is classified to one or more of the following configurable categories :

  * Travel

  * Food

  * Medical

  * Donations

  * Others etc.



   This classified list of citizen SOS requests lets the government authorities re-route the requests to the relevant departments to address them with minimal response time. 

   

   *Here, we use state-of-the-art NLP, Sequence encoding and Deep Learning Techniques to achieve the fucntionality*

   

   

##### This Solution was developed and demonstrated to the Dept. of Rural Department and Panchayat Raj, Government of Karnataka, India to be implemented in and around the city of Bengaluru, India.