-
Notifications
You must be signed in to change notification settings - Fork 0
Experiments
WANG Yan edited this page Feb 5, 2024
·
2 revisions
The sequences are generated by sampling items out of the pre-defined item set randomly. The disvantage is the optimality of a sequence is unknown so that it is difficult to gauge the packing performance with this benchmark.
Generating training sequences via cutting stock, by which items in a sequence are created by sequentially "cutting" the bin into items of the pre-defined types, so that the sequence may be perfectly packed and restored back to the bin.
Two variations:
- CUT-1: sorting items based on Z coordinates of their FLBs
- CUT-2: sorting items based on their stacking dependency
The performance of the packing algorithm is quantitated with
- space utilization (space uti.)
- the total number of items packed (# items)
- Multiple bins:
- initialize multiple BPP-1 instances matching the total bin number
- choose the bin in which the item introduces the least drop of the critic value given by the corresponding BPP-1 network
- Item re-orientation:
- create two feasibility masks for each item, one for each orientation