Operations Research in the Age of AI: Friends or Competitors?

Artificial Intelligence (AI) and Machine Learning (ML) dominate today's tech headlines. At the same time, Operations Research (OR) continues to quietly drive decision-making in industries from logistics to finance. Are these two worlds in competition - or do they complement each other?

The short answer: they're better as friends than rivals.

Optimization vs. Machine Learning - Different Goals

At their core, OR and ML solve different types of problems:

• Operations Research (Optimization): Finds the best decision given objectives and constraints.
  Example: Assigning flights and crews to minimize costs while respecting labor laws.
• Machine Learning: Finds patterns from data to make predictions or classifications.
  Example: Predicting tomorrow's flight demand based on past booking data.

"Optimization decides! Machine Learning predicts!"

When to Use Each

• Use OR/Optimization when the problem is about allocating scarce resources, planning, or scheduling under constraints.
• Use ML/AI when the problem is about learning from data, detecting patterns, or forecasting uncertain outcomes.

Neither is a silver bullet. Using the wrong tool for the job often leads to disappointment.

Where They Complement Each Other

The real magic happens when they work together:

• Data-driven optimization: ML predicts demand, OR uses those forecasts to plan production and distribution.
• Smart heuristics: ML can guide optimization algorithms by suggesting promising regions of the solution space.
• Robust decision-making: OR ensures decisions meet constraints, while ML provides probability distributions for uncertain inputs.

Example: In supply chain management, ML can forecast demand more accurately than traditional models. But it's OR that translates those forecasts into optimal inventory levels, production plans, and transport plans.

Why This Matters Now

As systems get more complex and data-rich, the line between prediction and decision becomes more important. Companies that only forecast (ML) but don't optimize, risk leaving money and efficiency on the table. Companies that only optimize without accurate forecasts risk solving the wrong problem.

Closing Thought

Operations Research and AI are not competitors, but partners.

• OR brings rigor, structure, and decision-making under constraints.
• AI/ML brings adaptability, pattern recognition, and predictive power.

Together, they enable smarter, more resilient systems that can not only understand the world, but also act effectively within it.