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 …
Learning generalizable models for vehicle routing problems via knowledge distillation
Recent neural methods for vehicle routing problems always train and test the deep models
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …
Simulation-guided beam search for neural combinatorial optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …
discover powerful heuristics for solving complex real-world problems. While neural …
Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
Neural multi-objective combinatorial optimization with diversity enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO)
problems solely rely on decomposition, which often leads to repetitive solutions for the …
problems solely rely on decomposition, which often leads to repetitive solutions for the …
A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling
Y Du, J Li - International Journal of Production Economics, 2024 - Elsevier
The environmental-friendly production demands higher manufacturing efficiency and lower
energy cost; therefore, time-of-use electricity price constraint and distributed production have …
energy cost; therefore, time-of-use electricity price constraint and distributed production have …
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
Machine learning to solve vehicle routing problems: A survey
A Bogyrbayeva, M Meraliyev… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
[HTML][HTML] Discovering Lin-Kernighan-Helsgaun heuristic for routing optimization using self-supervised reinforcement learning
Q Wang, C Zhang, C Tang - Journal of King Saud University-Computer and …, 2023 - Elsevier
Vehicle routing optimization is a crucial responsibility of transportation service providers,
which can significantly reduce operating expenses and improve client satisfaction. Learning …
which can significantly reduce operating expenses and improve client satisfaction. Learning …