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 …
Machine learning methods in solving the boolean satisfiability problem
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT),
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
A deep instance generative framework for milp solvers under limited data availability
In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …
From distribution learning in training to gradient search in testing for combinatorial optimization
Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet …
Research background: The article explores the integration of Artificial Intelligence (AI) in
predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses …
predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses …
Hubrouter: Learning global routing via hub generation and pin-hub connection
Global Routing (GR) is a core yet time-consuming task in VLSI systems. It recently attracted
efforts from the machine learning community, especially generative models, but they suffer …
efforts from the machine learning community, especially generative models, but they suffer …
[HTML][HTML] Automating distribution networks: Backtracking search algorithm for efficient and cost-effective fault management
Electricity outages can result in consequences for customers and cause disruptions that
result in revenue loss, business productivity reduction, appliance damage, and …
result in revenue loss, business productivity reduction, appliance damage, and …
Learning to dive in branch and bound
M Paulus, A Krause - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Primal heuristics are important for solving mixed integer linear programs, because they find
feasible solutions that facilitate branch and bound search. A prominent group of primal …
feasible solutions that facilitate branch and bound search. A prominent group of primal …
GNN&GBDT-guided fast optimizing framework for large-scale integer programming
The latest two-stage optimization framework based on graph neural network (GNN) and
large neighborhood search (LNS) is the most popular framework in solving large-scale …
large neighborhood search (LNS) is the most popular framework in solving large-scale …
Learning to branch with Tree-aware Branching Transformers
J Lin, J Zhu, H Wang, T Zhang - Knowledge-Based Systems, 2022 - Elsevier
Abstract Machine learning techniques have attracted increasing attention in learning Branch-
and-Bound (B&B) variable selection policies, but most of the existing methods lack …
and-Bound (B&B) variable selection policies, but most of the existing methods lack …