EURO Online Seminar Series 2026

Events

Marion Technical Writer February 17, 2026

Hexaly is pleased to participate in the EURO Online Seminar Series 2026 webinar. The full program and sign-up to receive access to upcoming talks are available here. The event is free and open to everyone. Don’t miss the session on Friday, April 20, 2026, 16:30–17:30 CET, 10:30-11:30 EST, 21:00-22:00 IST. 

Fred Gardi, founder & CEO of Hexaly, will explain what Hexaly is in comparison with traditional Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) solvers, and the difference it makes when solving discrete optimization problems like Routing, Scheduling, Packing, Clustering, Assignment, Matching, Location, as encountered in Supply Chain and Workforce Optimization. Below is the abstract of Fred’s talk at the EURO Online Seminar Series:

Beyond MILP: A Hybrid Approach to Large-Scale Real-World Optimization with Hexaly

Mixed-Integer Linear Programming (MILP) solvers have long dominated Operations Research (OR), yet they frequently struggle with large-scale, highly discrete, non-convex, or mixed-variable problems due to limitations in modeling expressiveness and scalability.

Hexaly embodies a post-MILP paradigm through a hybrid global solver that combines heuristic and exact methods, integrating concepts from Mixed-Integer Programming (MIP), Non-Linear Programming (NLP), Constraint Programming (CP), and Black-Box Optimization (BBO). By eliminating the need for linearization and departing from traditional tree-search paradigms, Hexaly achieves superior speed and scalability on real-world instances, routinely delivering high-quality solutions in minutes where conventional MILP solvers fail to produce feasible results.

This talk introduces the core principles of this hybrid post-MILP approach and discusses its implications for bridging OR with modern data-driven decision-making, particularly in enabling stochastic, robust, and bilevel optimization modeling, as well as coupling simulation and machine learning with mathematical optimization.

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