# alexfrom0815/Online-3D-BPP-PCT

Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.

Repository: https://github.com/alexfrom0815/Online-3D-BPP-PCT
Canonical: https://ross.abutalabs.com/products/online-3d-bpp-pct
Language: Python
License: MIT
License Family: permissive
Topics: 3d-packing, packing-algorithm, reinforcement-learning, online-packing, bin-packing
Last push: 2026-01-21T12:35:32+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 63, release rhythm 35, longevity 100
- inputs: {"age_days": 1673, "days_push": 224, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1045, forks 58 (observed 2026-08-28T04:03:21.579397+00:00)

## What it is
A Python research implementation of the ICLR 2022 paper 'Learning Efficient Online 3D Bin Packing on Packing Configuration Trees', using deep reinforcement learning over a hierarchical packing configuration tree. It supports arbitrary bin/item sizes, continuous solution spaces, packing stability approximation, and custom practical constraints.

## Use cases
- solve online 3D bin packing with deep reinforcement learning
- pack boxes into a container with continuous item sizes
- approximate packing stability for 3D bin packing
- benchmark DRL packing against heuristic baselines
- handle custom constraints in 3D packing
- reproduce ICLR 2022 bin packing paper results

## When to choose
- you need an online 3D bin packing solver with learned policies
- your items have continuous or arbitrary dimensions
- you need stability-aware or constraint-aware packing
- you want a research baseline for DRL-based packing

## When to avoid
- you need offline (all-items-known) packing optimization
- you need irregular-shape or buffered packing (see the authors' IR-BPP repo)
- you want a production-ready commercial packing service
- you need a simple heuristic without ML dependencies

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, reinforcement-learning, simulation
- domain: artificial-intelligence, reinforcement-learning, logistics
- platform: python, cross-platform
- tags: 3d-bin-packing, deep-reinforcement-learning, packing-configuration-tree, online-packing, iclr-2022, combinatorial-optimization, research-code, optimization, algorithms, linux

## Member repositories
- alexfrom0815/Online-3D-BPP-PCT (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.579397+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:02.983193+00:00, confidence not recorded.
  - readme: https://github.com/alexfrom0815/Online-3D-BPP-PCT (fetched 2026-08-28T04:03:21.579397+00:00, sha b4d3b484ba41)
- Data as of 2026-08-30T08:39:29.467469+00:00.