This method implements a potentially :ref:`exhaustive<hyper_search>` search for maximum bin utilization.
>>> from hyperpack import HyperPack
>>> problem_data = {
>>> "containers": containers,
>>> "items": items,
>>> "settings": settings
>>> }
>>> problem = HyperPack(**problem_data)
>>> problem.hypersearch(
>>> orientation="wide",
>>> sorting_by=("area", True)
>>> )Note
orientation parameter is used for :ref:`orient_items<orient_items>` method.
sorting_by parameter is used for :ref:`sort_items<sort_items>` method.
Presorting and orientation can impact the solution's speed and quality. Pass None to
these parameters to skip these operations.
After solving has finished, the solution can be found in :ref:`problem.solution<solution_structure>` instance attribute.
What this method generally does is it deploys a local search for every potential points strategy sequence available. That means all the possible permutations of the potential points.
For more on potential points see the theory or :ref:`here<Potential points concept>`
In case multiprocessing is used, the pool of strategies is divided to chuncks, and distributed to the generated
subprocesses. Every subprocess executes a local search for every strategy in the chunck, and is coordinated
with the rest of the processes through a mutually accessed multiprocessing.Array object.
If a maximum utilization is found (i.e. all the bins have 100% utilization), the processes are informed through the Array,
and the execution stops.
Upon exiting the winning process delivers the "goods", that is the process with the best found solution.
- Generally the stopping criteria of the hypersearch are:
- Maximum utilization found.
- Max time in seconds has been surpassed.
- An error occured in runtime.
Note
When using hypersearch, throttle=False argument passing can be used to make totally thorough
search on big instances of the problem (>71) items, pussing the local search to check all neighbors
of every node. Beware that this process is really time consuming.