optiwindnet.MILP.ortools

Module Contents

optiwindnet.MILP.ortools.make_min_length_model(A: networkx.Graph, capacity: int, *, topology: optiwindnet.MILP._core.Topology = Topology.BRANCHED, feeder_route: optiwindnet.MILP._core.FeederRoute = FeederRoute.SEGMENTED, feeder_limit: optiwindnet.MILP._core.FeederLimit = FeederLimit.UNLIMITED, balanced: bool = False, max_feeders: int = 0) → tuple[ortools.sat.python.cp_model.CpModel, optiwindnet.MILP._core.ModelMetadata]

Make discrete optimization model over link set A.

Build OR-tools CP-SAT model for the collector system length minimization.

Parameters:

• A – graph with the available edges to choose from

• capacity – maximum link flow capacity

• topology – one of Topology.{BRANCHED, RADIAL}

• feeder_route – FeederRoute.SEGMENTED -> feeder routes may be detoured around subtrees; FeederRoute.STRAIGHT -> feeder routes must be straight, direct lines

• feeder_limit – one of FeederLimit.{MINIMUM, UNLIMITED, SPECIFIED, MIN_PLUS1, MIN_PLUS2, MIN_PLUS3}

• max_feeders – only used if feeder_limit is FeederLimit.SPECIFIED

optiwindnet.MILP.ortools.warmup_model(model: ortools.sat.python.cp_model.CpModel, metadata: optiwindnet.MILP._core.ModelMetadata, S: networkx.Graph) → ortools.sat.python.cp_model.CpModel

Set initial solution into model.

Changes model in-place.

Parameters:

• model – CP-SAT model to apply the solution to.

• metadata – indices to the model’s variables.

• S – solution topology

Returns:

The same model instance that was provided, now with a solution.

optiwindnet.MILP.ortools.topology_from_mip_sol(*, metadata: optiwindnet.MILP._core.ModelMetadata, solver: SolverORTools | ortools.sat.python.cp_model.CpSolver, **kwargs) → networkx.Graph

Create a topology graph from the OR-tools solution to the MILP model.

Parameters:

• metadata – attributes of the solved model

• solver – solver instance that solved the model

• kwargs – not used (signature compatibility)

Returns:

Graph topology S from the solution.