Hypersearch

Solving with hyper_search

This method implements a potentially exhaustive 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 orient_items method.

sorting_by parameter is used for 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 problem.solution 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 here

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:

1. Maximum utilization found.

2. Max time in seconds has been surpassed.

3. 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.