Constrained optimization is a critical part of many complex systems, where resources like time, labor, equipment, or energy must be efficiently allocated under strict rules. Traditional methods, such as Mixed-Integer Linear Programming or Heuristics, often fall short in dynamic and decentralized environments due to their rigid and centralized nature.
Join Karthika Vijayan for a talk at the upcoming Sahaj Software DevDay in Pune, where we’ll explore how Agentic AI offers a compelling new way to solve these challenges. We’ll take a hard look at:
The limitations of traditional, centralized optimization methods and why they struggle in high-dimensional and dynamic systems.
How Agentic AI provides a more compelling alternative by modeling system components as autonomous agents capable of local reasoning and negotiation.
The power of this approach using a real-world case study of airline scheduling—a notoriously complex example of constrained optimization where aircraft, crew, and fuel must be scheduled under tight constraints.
How hybrid frameworks, which integrate agentic models with classical solvers, create systems that are not only scalable but also resilient to real-time changes and disruptions.
📝 What You’ll Take Away
You’ll walk away with a fresh perspective on tackling complex optimization challenges.
You’ll start to see the difference between centralized, rigid solutions and decentralized, resilient, agent-based systems.
You’ll get better at spotting problems that are a perfect fit for an agentic AI approach.
You’ll learn why a flexible, adaptive model matters more than a rigid, centralized one.
And most importantly, you’ll feel more confident in your ability to design systems that are not only efficient but also robust and capable of handling real-world complexity.
🤔 Who Should Attend
Anyone interested in solving complex optimization problems, from software developers and architects to data scientists and operations research specialists, who want to learn about a cutting-edge approach.
This is a continuation to Optimization Techniques: Linear Programming to Reinforcement Learning – Part 2.
