In this post, I'll explore a use case for the GSPy bridge: dynamically solving a complex river allocation problem using Python's PuLP Linear Programming (LP) library at every timestep of a GoldSim simulation.
The challenge of simulating a river network with sequential diversions, return flows, and competing demands governed by priorities is non-trivial. By moving the allocation decision to an external optimizer, we can achieve optimal water delivery in a way that is challenging to replicate with explicit rules-based logic.
