Papers with Code 아카이브
3D Bin Packing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
Online 3D Bin Packing Reinforcement Learning Solution with Buffer
Learning Practically Feasible Policies for Online 3D Bin Packing
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
Online 3D Bin Packing with Constrained Deep Reinforcement Learning
Three-Dimensional Bin Packing and Mixed-Case Palletization
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.