3D Bin Packing
4 papers with code • 0 benchmarks • 0 datasets
As a classic NP-hard problem, the bin packing problem (1D-BPP) seeks for an assignment of a collection of items with various weights to bins. The optimal assignment houses all the items with the fewest bins such that the total weight of items in a bin is below the bin’s capacity. In its 3D version (3D-BPP), an item has a 3D “weight” corresponding to its length, width and height.
Benchmarks
These leaderboards are used to track progress in 3D Bin Packing
Most implemented papers
Learning Practically Feasible Policies for Online 3D Bin Packing
alexfrom0815/Online-3D-BPP-DRL
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In this problem, the items are delivered to the agent without informing the full sequence information.
Three-Dimensional Bin Packing and Mixed-Case Palletization
This is particularly true for its practical variant, the mixed-case palletization problem, where item support is needed.
Online 3D Bin Packing with Constrained Deep Reinforcement Learning
alexfrom0815/Online-3D-BPP-DRL
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We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP).
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
alexfrom0815/Online-3D-BPP-PCT
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ICLR 2022
PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL).
