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.math_opt.python.mathopt.Model, optiwindnet.MILP._core.ModelMetadata][source]

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, EXACTLY, SPECIFIED, MIN_PLUS1, MIN_PLUS2, MIN_PLUS3}

  • balanced – enforce subtree loads differing at most by one unit (only possible if feeder_limit pins the feeder count to a single value)

  • max_feeders – upper bound if feeder_limit is FeederLimit.SPECIFIED, exact count if it is FeederLimit.EXACTLY, unused otherwise

optiwindnet.MILP.ortools.warmup_model(model: ortools.math_opt.python.mathopt.Model, metadata: optiwindnet.MILP._core.ModelMetadata, S: networkx.Graph) → ortools.math_opt.python.mathopt.Model[source]

Set initial solution into model.

Changes model and metadata 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.

Raises:

OWNWarmupFailed – if some link in S is not available in model.