Case Study: Fusing Machine Learning into Operations Research Techniques to solve Complex Optimization Problems

Machine Learning (ML) and Operations Research (OR) have co-existed for long. There have been amazing applications driven by ML and OR that we come across in our day-to-day lives. These applications range from matching algorithms on dating websites to solving large scale vehicle routing problems for complex supply chains. But, have you ever wondered what happens when these two areas of mathematical science come together to solve complex real-world optimization problems?

Are you curious to know how OR-applications can benefit from the power of ML?

In this talk, we’ll go through a real-world case study where we used the power of (ML+OR) to create significant dollar savings in the area of airline flight schedules. I will also take you through cases where ML can help OR solutions shine further to solve more generic problems.

 
 

Outline/Structure of the Experience Report

Brief Introduction to the topic, presenter and context setting ( 3 mins)

Use Cases on how ML can be fused into OR (3 mins)

Case Study: (12 mins)

Problem Context

Complexity

Outline of the solution approach

Challenges Faced and Details of ML Algorithm

Results

Reasons why OR+ML is not widely used (2 mins)

Learning Outcome

Applying Machine Learning along with Operations Research to solve problems in the transportation domain.

Target Audience

Data Scientist, AI Enthusiasts, Data Science Managers, Operations Researchers

Prerequisites for Attendees

General understanding of ML and OR fundamentals and heaps of curiosity in the field of real-world problem-solving.

Slides

Video

schedule Submitted 1 year ago

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    • Rakshit Prabhakar
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      Rakshit Prabhakar - Smart AI Drones : As Emergency Responders During Accidents

      Rakshit Prabhakar
      Rakshit Prabhakar
      Innovator
      AIM
      schedule 1 year ago
      Sold Out!
      20 Mins
      Talk
      Beginner

      Growing population, growing vehicles and lack of awareness has led to the problem of accidents happening at low to high density vehicle and human population. A witness in an accident has no medical training and often becomes a bystander. Drones as emergency responders exploits the use of ICT with AI to as an add-on to emergency services. With a push of a button by a witness, the drones use the GPS of the phone to reach the spot, the AI model is trained to classify the accident and shares its results with the emergency responders including hospitals. The ambulance reaching the spot is prepared with the basic of what has to be done and the nearest hospital well prepared to act.

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