Can anyone share how this is used in practice? Somehow I imagine implementing numerical optimization often fails due to the usual problems with data-driven approaches (trust, bad data, etc.) and ultimately someone important just decides how things are going to be done based on stomach feel.
FMCG company here. We use these in practice of:
1. Salesman & delivery travel plan
2. Machine, Human and material resource scheduling for production
3. Inventory level for warehouse distribution center. This one isn't fully automatic because demand forecasting is hard
have a read through the case studies:
gurobi case studies: https://www.gurobi.com/case_studies/
some cplex case studies: https://www.ibm.com/products/ilog-cplex-optimization-studio/...
hexaly (formerly localsolver) case studies: https://www.hexaly.com/customers
We use solvers throughout the stack at work: solvers to schedule home batteries and EVs in peoples homes optimally, solvers to schedule hundreds of thousands of those homes optimally as portfolios, solvers to trade that portfolio optimally.
The EU electricity spot price is set each day in a single giant solver run, look up Euphemia for some write ups of how that works.
Most any field where there is a clear goal to optimise and real money on the line will be riddled with solvers