EWO seminar, CAPD, Carnegie Mellon University

Events

Marion Technical Writer April 15, 2026

Fred Gardi, Founder and CEO, is invited to speak about Hexaly at the Enterprise-Wide Optimization seminar, organized by Prof. Ignacio E. Grossmann and Prof. Chrysanthos E. Gounaris of the Center for Advanced Process Decision-making (CAPD) at Carnegie Mellon University. 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 Thursday, May 7, 2026, 10:00 am ET. Below is the abstract of Fred’s talk at the EWO seminar:

Beyond MILP: Hexaly, a Hybrid Optimization Solver

Mixed‑Integer Linear Programming (MILP) has been the dominant optimization framework in Operations Research for decades. While it has proven extremely powerful, it is also well known that MILP formulations can become unwieldy when faced with large‑scale, highly combinatorial, non‑convex, or structurally rich problems, particularly in domains such as routing, scheduling, and packing.

Hexaly is an industrial optimization solver built around a hybrid, post‑MILP approach. Rather than relying on linearization and classical branch‑and‑bound‑centered workflows, it combines heuristic and exact techniques and draws inspiration from multiple paradigms, including Mixed‑Integer Programming, Constraint Programming, Nonlinear Programming, and Black‑Box Optimization. A central focus is on modeling expressiveness and openness: enabling users to describe problems closer to their natural combinatorial structure, and allowing different algorithmic components to interact rather than compete.

In this talk, I will present the overall philosophy behind this approach, with an emphasis on discrete optimization problems where Hexaly currently delivers its strongest results, such as large‑scale routing, scheduling, and packing. I will explain how hybridization plays a role not only in the backend algorithms, but also—crucially—in the modeling layer. Finally, I will provide an honest snapshot of where we stand today from an algorithmic perspective, supported by selected performance benchmarks.

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