Local search

Solving with local_search

Do a hill-climbing, 2-opt exchange local search with the default potential points strategy (see theory). A brief explanation of the local search procedure here.

>>> from hyperpack import HyperPack
>>> problem_data = {
>>>     "containers": containers,
>>>     "items": items,
>>>     "settings": settings
>>> }
>>> problem = HyperPack(**problem_data)
>>> problem.local_search()

After solving has finished, the best solution can be found in problem.solution instance attribute.

Potential points strategy

If you want to override the potential points strategy used, simply set the proper value to the potential_points_strategy attribute.

The attribute is managed and protected by deletion and assignment validation:

It must be a tuple (immutability).
It must contain only proper potential points (see HyperPack.DEFAULT_POTENTIAL_POINTS_STRATEGY for reference).
It cannot be deleted.
Must not contain duplicate points.

In every invalid case, a PotentialPointsError will be raised with approriate message and logging.