Cross-industry
Making production optimization models fast and stable enough to trust
Formulation and solver engineering that cut run times and let production models grow without becoming numerically fragile.
30–70%
Reduction in solve times
2–5×
Growth in model size with numerical stability maintained
Client
Enterprise optimization teams across industries
Basis
Partner experience, prior role
The insight
“When a model cannot solve inside the decision window, the business quietly stops using it. Faster hardware rarely fixes that. Better formulations usually do.”
Exhibit
A stronger formulation reaches a usable answer inside the decision window
Optimality gap over solve time, same model and data
Schematic, not client data. The gap measures how far the best plan found could be from the true optimum. Tightening the formulation typically moves the curve far more than solver settings or hardware do.
Models that worked, but too slowly
Enterprise teams had optimization models in production for planning, scheduling, and allocation. The models worked, but they were slow, and each new business requirement made them larger and slower.
Slow or unstable models lose the business’s trust
When a model cannot solve inside the decision window, the business quietly stops using its answers, and when a model becomes numerically unstable, small input changes produce large and unexplained swings in the plan. Both failures erode trust faster than any modeling error, and buying more hardware rarely fixes either one.
Fixing the formulation before tuning the solver
We reviewed model architecture, formulations, and solver behavior together. The largest gains usually came from the formulation itself: tighter constraints, better variable definitions, and removing constraints the business did not need, followed by targeted solver tuning and numerical diagnostics. Each change was measured against the team’s own production instances.
30 to 70 percent faster, with room to grow
Solve times fell by 30 to 70 percent, and models grew two to five times larger while remaining numerically stable, giving teams room to add the business detail they had previously deferred.
What made the difference
01
Tighten the formulation
Stronger constraints and better variable definitions closed the gap faster than any solver setting could.
02
Remove what is not needed
Constraints the business did not actually require were taken out, shrinking the model without changing its answers.
03
Measure on real instances
Every change was benchmarked on the team's own production instances, with numerical diagnostics alongside run time.