Pages that link to "Item:Q2029358"
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The following pages link to Machine learning for combinatorial optimization: a methodological tour d'horizon (Q2029358):
Displaying 50 items.
- Empirical decision model learning (Q511793) (← links)
- Computational complexity of combinatorial optimization problems induced by collective procedures in machine learning (Q643804) (← links)
- Using computational learning strategies as a tool for combinatorial optimization (Q1267770) (← links)
- Learning heuristics for the TSP by policy gradient (Q1626725) (← links)
- A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
- A comment on: ``What makes a VRP solution good? The generation of problem-specific knowledge for heuristics'' (Q2002856) (← links)
- Scenario-based learning for stochastic combinatorial optimisation (Q2011603) (← links)
- Optimization problems for machine learning: a survey (Q2029894) (← links)
- Data-driven algorithm selection and tuning in optimization and signal processing (Q2043447) (← links)
- Learning chordal extensions (Q2046324) (← links)
- Beyond graph neural networks with lifted relational neural networks (Q2071316) (← links)
- Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning (Q2077963) (← links)
- Learning to select operators in meta-heuristics: an integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem (Q2079447) (← links)
- Reinforcement learning of simplex pivot rules: a proof of concept (Q2080843) (← links)
- Reinforcement learning for the knapsack problem (Q2089607) (← links)
- Neural large neighborhood search for routing problems (Q2093389) (← links)
- Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times (Q2102999) (← links)
- Learning variable activity initialisation for lazy clause generation solvers (Q2117201) (← links)
- Injecting domain knowledge in neural networks: a controlled experiment on a constrained problem (Q2117227) (← links)
- Learning to reduce state-expanded networks for multi-activity shift scheduling (Q2117239) (← links)
- SeaPearl: a constraint programming solver guided by reinforcement learning (Q2117242) (← links)
- Learning generalized strong branching for set covering, set packing, and 0-1 knapsack problems (Q2140266) (← links)
- Boosting ant colony optimization via solution prediction and machine learning (Q2147035) (← links)
- Learning the travelling salesperson problem requires rethinking generalization (Q2152276) (← links)
- Generative deep learning for decision making in gas networks (Q2155386) (← links)
- Predicting solutions of large-scale optimization problems via machine learning: a case study in blood supply chain management (Q2177838) (← links)
- Catch me if you scan: data-driven prescriptive modeling for smart store environments (Q2240025) (← links)
- Machine learning and combinatorial optimization. Editorial (Q2241907) (← links)
- Generalization of machine learning for problem reduction: a case study on travelling salesman problems (Q2241908) (← links)
- A novel solution approach with ML-based pseudo-cuts for the flight and maintenance planning problem (Q2241911) (← links)
- Metaheuristics ``In the large'' (Q2242238) (← links)
- Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art (Q2242290) (← links)
- On combining machine learning with decision making (Q2512894) (← links)
- Reinforcement learning for combinatorial optimization: a survey (Q2669503) (← links)
- Deep-learning-based partial pricing in a branch-and-price algorithm for personalized crew rostering (Q2669662) (← links)
- Guidelines for the computational testing of machine learning approaches to vehicle routing problems (Q2670511) (← links)
- An algorithm selection approach for the flexible job shop scheduling problem: choosing constraint programming solvers through machine learning (Q2672113) (← links)
- BDD-based optimization for the quadratic stable set problem (Q2673237) (← links)
- Towards a machine learning-aided metaheuristic framework for a production/distribution system design problem (Q2676294) (← links)
- Constraint-based robust planning and scheduling of airport apron operations through simheuristics (Q2678623) (← links)
- Dynamic graph conv-LSTM model with dynamic positional encoding for the large-scale traveling salesman problem (Q2688704) (← links)
- Merging AI and OR to solve high-dimensional stochastic optimization problems using approximate dynamic programming (Q2899030) (← links)
- Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems (Q4995099) (← links)
- High generalization performance structured self-attention model for knapsack problem (Q5025155) (← links)
- Learning to Approximate Industrial Problems by Operations Research Classic Problems (Q5031031) (← links)
- A note on no-free-lunch theorem (Q5057731) (← links)
- Improving Variable Orderings of Approximate Decision Diagrams Using Reinforcement Learning (Q5058000) (← links)
- A Classifier to Decide on the Linearization of Mixed-Integer Quadratic Problems in CPLEX (Q5060504) (← links)
- Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information (Q5084648) (← links)
- JANOS: An Integrated Predictive and Prescriptive Modeling Framework (Q5085992) (← links)
