Almost by definition, optimization is a source of a tremendous power for automatically improving processes, decisions, products and services. But its potential is still largely un-exploited in most real-world contexts. One of the main reasons blocking its widespread adoption is that standard optimization assumes the existence of a function f(x) to be minimized, while in most real-world business contexts this function does not exist or is extremely difficult and costly to build by hand. Machine learning (ML) comes to the rescue: the function (the model) can be built by machine learning starting from abundant data. By Learning and Intelligent Optimization (LION) we mean this combination of learning from data and optimization which can be applied to complex, dynamic, stochastic contexts.This combination dramatically increases the automation level and puts more power directly in the hands of decision makers without re-sorting to intermediate layers of data scientists (LION has a huge potential for a self-service usage). Reaching this goal is a huge challenge and it will require research at the boundary between two areas, machine learning and optimization, which have been traditionally separated.
