Meta just open-sourced Rebalancer, the assignment solver that has run resource allocation across Meta for 9+ years. It solves ~40M assignment problems per day across 30+ problem formulations, from shard placement to global traffic routing.

The core innovation is separating the problem spec from the solver. You describe objects, bins, constraints and goals once. Rebalancer compiles that into an expression graph, then solves it with either a parallel local search or a MIP solver (Gurobi, FICO Xpress or HiGHS).

The trade-off is explicit. MIP gives optimal answers but the model grows as O(objects × bins), so Meta's largest problems are too big for any MIP solver. Local search works directly on the graph with an O(objects + bins) neighborhood and evaluates millions of moves per second. Meta uses local search for almost all large problems.

Production numbers: P99 solve time of 12s on 265k objects and 3.2k bins. Problems above 1M objects and 5k bins average 171s, across 3.4k+ runs.

Full analysis: https://www.marktechpost.com/2026/10/06/meta-ai-open-sources-rebalancer-a-c-assignment-solver-that-runs-about-40-million-placement-problems-a-day/

Repo: https://github.com/facebook/rebalancer

Paper: https://www.usenix.org/conference/osdi24/presentation/kumar

Technical details: https://engineering.fb.com/2026/09/21/open-source/rebalancer-generic-high-performance-library-assignment-problems/