Heuristics integrated deep reinforcement learning for online 3d bin packing
S Yang, S Song, S Chu, R Song… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Online 3D Bin Packing Problem (3D-BPP) has a wide range of industrial applications and
there is an emerging research interest in learning optimal bin packing policy and deploying …
there is an emerging research interest in learning optimal bin packing policy and deploying …
Automating bin packing: A layer building matheuristics for cost effective logistics
In this paper, we address the problem of automating the definition of feasible pallets
configurations. This issue is crucial for the competitiveness of logistic companies and is still …
configurations. This issue is crucial for the competitiveness of logistic companies and is still …
Bq-nco: Bisimulation quotienting for efficient neural combinatorial optimization
D Drakulic, S Michel, F Mai, A Sors… - Advances in Neural …, 2024 - proceedings.neurips.cc
Despite the success of neural-based combinatorial optimization methods for end-to-end
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation
The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO)
problem. Since it has many applications in our daily life, eg logistics and resource allocation …
problem. Since it has many applications in our daily life, eg logistics and resource allocation …
Artificial intelligence in smart logistics cyber-physical systems: State-of-the-arts and potential applications
Y Liu, X Tao, X Li, AW Colombo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Logistics creates tremendous economic value through supporting the trading of goods
between firms and customers, thereby improving the welfare of the society. In order to …
between firms and customers, thereby improving the welfare of the society. In order to …
Graph learning assisted multi-objective integer programming
Objective-space decomposition algorithms (ODAs) are widely studied for solving multi-
objective integer programs. However, they often encounter difficulties in handling scalarized …
objective integer programs. However, they often encounter difficulties in handling scalarized …
Traffic prediction-enabled energy-efficient dynamic computing resource allocation in cran based on deep learning
Y Fu, X Wang - IEEE Open Journal of the Communications …, 2022 - ieeexplore.ieee.org
Due to the greatly increased bandwidth of 5G networks compared with that of 4G networks,
the power consumption brought by baseband signal processing of 5G networks is much …
the power consumption brought by baseband signal processing of 5G networks is much …
Attend2Pack: Bin packing through deep reinforcement learning with attention
This paper seeks to tackle the bin packing problem (BPP) through a learning perspective.
Building on self-attention-based encoding and deep reinforcement learning algorithms, we …
Building on self-attention-based encoding and deep reinforcement learning algorithms, we …
Reinforcement learning for combinatorial optimization
D Wang - Encyclopedia of Data Science and Machine Learning, 2023 - igi-global.com
Combinatorial optimization (CO) problems have many important application domains,
including social networks, manufacturing, and transportation. However, as an NP-hard …
including social networks, manufacturing, and transportation. However, as an NP-hard …
The third party logistics provider freight management problem: a framework and deep reinforcement learning approach
A Abbasi-Pooya, MT Lash - Annals of Operations Research, 2024 - Springer
In many large manufacturing companies, freight management is handled by a third-party
logistics (3PL) provider, thus allowing manufacturers and their suppliers to focus on the …
logistics (3PL) provider, thus allowing manufacturers and their suppliers to focus on the …