Igor Ganapolsky

You're not misreading it - the implied ranges matter too, not just explicit coefficients and RHS. Gurobi's recommended ranges apply to variable bounds as well, and for a bilinear or general nonline...

Thanks Bruce — 30x vs HiGHS on the same JSON/slack model is the result that matters. Keep both solvers as a check; use Gurobi for the live season run.Practical note: encode the 4 variations as slac...

The bottleneck is very likely not Gurobi but what you send back through the Pool. 1) You return result['model'], a live Gurobi model object. Gurobi Model/Env objects are not picklable, and even whe...

A few things from your log that usually explain slow bound movement here. 1) Nearly all your constraints are equalities defining auxiliary products/powers (u1_sq = u1^2, c1_1_sq = c1_1^2, d1_c1_1_s...

-scaling checklist that has helped on energy-system MILPs with ranges like yours (GW vs m² vs tonnes): 1. Rescale PV area/irradiance into the same order as other power terms (e.g. express capacity ...

Adding a practical recovery pattern on top of Gwyneth's ServerTimeout tip: 1. Treat DISCONNECTED as a hard session loss — do not reuse the old Env/Model. Build a fresh gp.Env() with current Instant...

Adding to Ryuta's answer: grbgetkey only exists if you installed the full Gurobi Optimizer distribution and its bin directory is on your PATH. If you installed with "pip install gurobipy", there is...

Great use case - clerkship block scheduling is a classic assignment/timetabling MIP. Two things that helped on similar rostering models: (1) Symmetry: if blocks or identical sites are interchangeab...