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 …
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
Container stacking optimization based on Deep Reinforcement Learning
X Jin, Z Duan, W Song, Q Li - Engineering Applications of Artificial …, 2023 - Elsevier
Cargo storage is one of the key aspects of the maritime transportation. As the prior site
planning, container stacking has a critical influence on the operation efficiency of the storage …
planning, container stacking has a critical influence on the operation efficiency of the storage …
[HTML][HTML] Instance-specific algorithm configuration via unsupervised deep graph clustering
Abstract Instance-specific Algorithm Configuration (AC) methods are effective in
automatically generating high-quality algorithm parameters for heterogeneous NP-hard …
automatically generating high-quality algorithm parameters for heterogeneous NP-hard …
Big data analytics in production and distribution management
Production and distribution are two key constituents of a supply chain. In view of the growing
availability of data and advances in big data analytics techniques, there have been more …
availability of data and advances in big data analytics techniques, there have been more …
Stochastic economic lot scheduling via self-attention based deep reinforcement learning
W Song, N Mi, Q Li, J Zhuang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic
optimization problem with wide industrial applications. Traditional methods such as hyper …
optimization problem with wide industrial applications. Traditional methods such as hyper …
A cerebellar operant conditioning-inspired constraint satisfaction approach for product design concept generation
Conceptual design is a pivotal stage of new product development. The function-behaviour-
structure framework is adopted in this stage to help designers search design space and …
structure framework is adopted in this stage to help designers search design space and …
Rethinking the capacity of graph neural networks for branching strategy
Graph neural networks (GNNs) have been widely used to predict properties and heuristics of
mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper …
mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper …
A deep reinforcement learning approach for resource-constrained project scheduling
X Zhao, W Song, Q Li, H Shi, Z Kang… - 2022 IEEE Symposium …, 2022 - ieeexplore.ieee.org
The Resource-Constrained Project Schedule Problem (RCPSP) is one of the most studied
Cumulative Scheduling Problems with many real-world applications. Priority rules are widely …
Cumulative Scheduling Problems with many real-world applications. Priority rules are widely …
Learning a generic value-selection heuristic inside a constraint programming solver
T Marty, T François, P Tessier, L Gauthier… - arXiv preprint arXiv …, 2023 - arxiv.org
Constraint programming is known for being an efficient approach for solving combinatorial
problems. Important design choices in a solver are the branching heuristics, which are …
problems. Important design choices in a solver are the branching heuristics, which are …