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 …
A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals
J Zhu, W Zhang, L Yu, X Guo - Omega, 2024 - Elsevier
To improve the operating efficiency of container terminals, we investigate a closed-loop
scheduling method in an autonomous inter-terminal system that employs unmanned …
scheduling method in an autonomous inter-terminal system that employs unmanned …
[HTML][HTML] Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and …
F Han, R Ding, Y Deng, X Zha, G Fu - Agronomy, 2024 - mdpi.com
In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity
maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on …
maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on …
[HTML][HTML] A machine-learning-based column generation heuristic for electric bus scheduling
Bus scheduling in public transit consists in determining a set of bus schedules to cover a set
of timetabled trips at minimum cost. This planning process has evolved recently with the …
of timetabled trips at minimum cost. This planning process has evolved recently with the …
Improved Peel-and-Bound: Methods for generating dual bounds with multivalued decision diagrams
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete
optimization. However, the field of decision diagrams is relatively new, and is still …
optimization. However, the field of decision diagrams is relatively new, and is still …
Decision diagram-based branch-and-bound with caching for dominance and suboptimality detection
The branch-and-bound algorithm based on decision diagrams is a framework for solving
discrete optimization problems with a dynamic programming formulation. It works by …
discrete optimization problems with a dynamic programming formulation. It works by …
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 …
A decision support method for credit risk based on the dynamic Bayesian network
Purpose Credit risk evaluation is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. Most of the current credit risk methods rely …
to support decision-making on granting loans. Most of the current credit risk methods rely …
Boosting Decision Diagram-Based Branch-And-Bound by Pre-Solving with Aggregate Dynamic Programming
Discrete optimization problems expressible as dynamic programs can be solved by branch-
and-bound with decision diagrams. This approach dynamically compiles bounded-width …
and-bound with decision diagrams. This approach dynamically compiles bounded-width …
Racing Control Variable Genetic Programming for Symbolic Regression
Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing
equations from experimental data. Popular approaches based on genetic programming …
equations from experimental data. Popular approaches based on genetic programming …