Operations research and optimization in Python — recognising an OR problem, choosing between OR-Tools CP-SAT, MILP solvers via PuLP/Pyomo, routing and network models, and heuristics; modelling patterns for scheduling, assignment, routing, packing, and rostering; objectives and soft constraints; infeasibility diagnosis; time limits and determinism; scaling; and combining a solver with an LLM safely.
