On-line three-dimensional packing problems: A review of off-line and on-line solution approaches
Abstract Three-Dimensional Packing Problems (3D-PPs) can be applied to effectively
reduce logistics costs in various areas, such as airline cargo management and warehouse …
reduce logistics costs in various areas, such as airline cargo management and warehouse …
A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning
L Wang, X Hu, Y Wang, S Xu, S Ma, K Yang, Z Liu… - Computer Networks, 2021 - Elsevier
Job-shop scheduling problem (JSP) is used to determine the processing order of the jobs
and is a typical scheduling problem in smart manufacturing. Considering the dynamics and …
and is a typical scheduling problem in smart manufacturing. Considering the dynamics and …
Packerbot: Variable-sized product packing with heuristic deep reinforcement learning
Z Yang, S Yang, S Song, W Zhang… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
Product packing is a typical application in ware-house automation that aims to pick objects
from unstructured piles and place them into bins with optimized placing policy. However, it …
from unstructured piles and place them into bins with optimized placing policy. However, it …
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects
Dense packing in pick-and-place systems is an important feature in many warehouse and
logistics applications. Prior work in this space has largely focused on planning algorithms in …
logistics applications. Prior work in this space has largely focused on planning algorithms in …
BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems
SA Murdivien, J Um - Sensors, 2023 - mdpi.com
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected
disruptions, such as product changes or rapid order, in supply chain changes while …
disruptions, such as product changes or rapid order, in supply chain changes while …
Task scheduling for control system based on deep reinforcement learning
Y Liu, Y Ni, C Dong, J Chen, F Liu - Neurocomputing, 2024 - Elsevier
We investigate the control system's computational task scheduling problem within limited
time and with limited CPU cores in the cloud server. We employ a neural network model to …
time and with limited CPU cores in the cloud server. We employ a neural network model to …
Smart nesting: estimating geometrical compatibility in the nesting problem using graph neural networks
Reducing material waste and computation time are primary objectives in cutting and packing
problems (C &P). A solution to the C &P problem consists of many steps, including the …
problems (C &P). A solution to the C &P problem consists of many steps, including the …
GOPT: Generalizable Online 3D Bin Packing via Transformer-Based Deep Reinforcement Learning
H Xiong, C Guo, J Peng, K Ding… - IEEE Robotics and …, 2024 - ieeexplore.ieee.org
Robotic object packing has broad practical applications in the logistics and automation
industry, often formulated by researchers as the online 3D Bin Packing Problem (3D-BPP) …
industry, often formulated by researchers as the online 3D Bin Packing Problem (3D-BPP) …
A multi-heuristic algorithm for multi-container 3-d bin packing problem optimization using real world constraints
With the growing demand for sustainable and optimal packaging solutions, this study
proposes a novel two-stage algorithm for the multi-container three-dimensional bin packing …
proposes a novel two-stage algorithm for the multi-container three-dimensional bin packing …