Constraint solving A.I. algorithms in OptaPlanner
|Big Data, Machine Learning, AI & Analytics|
Some problems are (NP-)hard: employee shift rostering, vehicle routing problems, cloud optimization, skill based task assigning or even just building a fair tennis club schedule. Simple algorithms can’t solve these problems anywhere near optimally. But if we used advanced algorithms, we get better solutions that increase profit, reduce our ecological footprint and improve customer satisfaction.
In this session, I will introduce constraint optimization, demo a few use cases, use weighted hard and soft constraints to formalize business goals, walk through example code in Java of our open source constraint solver OptaPlanner, cover real-time continuous planning and teach algorithms such as Exhaustive Search (Brute Force), Construction Heuristics (First Fit) and Local Search (Tabu Search, Simulated Annealing).
|Geoffrey De Smet|
Geoffrey De Smet is the founder and lead of OptaPlanner (www.optaplanner.org), the leading open source A.I. constraint solver in Java.