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Speaker: Andrea Taverna, PhD, CAP
Senior Operations Researcher for Pricing Optimization @ Priceloop

Titolo: Operations Research: The Scalable AI for Complex Decision Problems

Abstract: Thanks to Machine Learning (ML), a large amount of data has been put to good use in the recent years, detecting patterns, extracting insights and providing valuable predictions to decision makers.

However, having actionable knowledge is only part of the picture. Be it a robot or a human, once the data is in, a course of action, possibly the best, has to be found, while satisfying several requirements.

For example, deciding which stocks to buy and when, how to schedule deliveries, which widget to produce and in which machinery to invest, are all decision problems.

Operations Research (OR) tries to reliably answer those questions by explicitly modelling decisions and finding the optimal ones for the chosen goals.

Given the tremendous impact decision optimization can yield for a business, OR is one of the best way to exploit and valorize ML models!

In this talk I will present Mathematical Programming, a versatile decision modelling method, and its application to an example Power Plant scheduling problem, solved via open source solvers and Python libraries.

While smaller problems can be solved in reasonable time with a generic solver on a single machine, the same technology is routinely scaled in real-world applications to larger and more complex problems via generic “decomposition methods”.

I will then present an example of decomposition for the Power Plant problem.

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