Pages that link to "Item:Q2029358"

The following pages link to Machine learning for combinatorial optimization: a methodological tour d'horizon (Q2029358):

Displaying 48 items.

• Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints (Q5087714) (← links)
• Online Mixed-Integer Optimization in Milliseconds (Q5106419) (← links)
• From numerical optimization method to learning optimization method (Q5115390) (← links)
• Set-to-Sequence Methods in Machine Learning: A Review (Q5154747) (← links)
• Learning to schedule heuristics for the simultaneous stochastic optimization of mining complexes (Q6047903) (← links)
• Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II (Q6047920) (← links)
• Multi-constructor CMSA for the maximum disjoint dominating sets problem (Q6065657) (← links)
• Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems (Q6069215) (← links)
• A reinforcement learning approach for transaction scheduling in a shuttle‐based storage and retrieval system (Q6082273) (← links)
• ReLU neural networks of polynomial size for exact maximum flow computation (Q6086001) (← links)
• An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents (Q6087487) (← links)
• A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment (Q6090150) (← links)
• Research trends in combinatorial optimization (Q6091419) (← links)
• Towards Lower Bounds on the Depth of ReLU Neural Networks (Q6100606) (← links)
• A data driven Dantzig-Wolfe decomposition framework (Q6102860) (← links)
• The first AI4TSP competition: learning to solve stochastic routing problems (Q6103656) (← links)
• A reinforced hybrid genetic algorithm for the traveling salesman problem (Q6106563) (← links)
• Machine learning augmented approaches for hub location problems (Q6109558) (← links)
• Adaptive solution prediction for combinatorial optimization (Q6112875) (← links)
• Navigational guidance -- a deep learning approach (Q6113464) (← links)
• Designing topological data to forecast bankruptcy using convolutional neural networks (Q6115949) (← links)
• gym-flp: a Python package for training reinforcement learning algorithms on facility layout problems (Q6130685) (← links)
• Machine learning-based online scheduling in distributed computing (Q6135487) (← links)
• Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness (Q6167662) (← links)
• Integrating driver behavior into last-mile delivery routing: combining machine learning and optimization in a hybrid decision support framework (Q6168609) (← links)
• Tailored presolve techniques in branch‐and‐bound method for fast mixed‐integer optimal control applications (Q6180277) (← links)
• Learning to repeatedly solve routing problems (Q6196890) (← links)
• A differentiable approach to the maximum independent set problem using dataless neural networks (Q6488722) (← links)
• CCGnet: a deep learning approach to predict Nash equilibrium of chance-constrained games (Q6492614) (← links)
• Container port truck dispatching optimization using Real2Sim based deep reinforcement learning (Q6554614) (← links)
• Predicting and optimizing marketing performance in dynamic markets (Q6556794) (← links)
• Learning to sample initial solution for solving 0-1 discrete optimization problem by local search (Q6564766) (← links)
• Facility location decisions for drone delivery: a literature review (Q6565382) (← links)
• Solution algorithms for dock scheduling and truck sequencing in cross-docks: a neural branch-and-price and a metaheuristic (Q6568435) (← links)
• Explainable AI for operational research: a defining framework, methods, applications, and a research agenda (Q6572853) (← links)
• A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data (Q6572858) (← links)
• Graphs connected to isotopes of inverse property quasigroups: a few applications (Q6577870) (← links)
• One-shot learning for MIPs with SOS1 constraints (Q6579097) (← links)
• A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem (Q6589065) (← links)
• Automatic MILP Solver configuration by learning problem similarities (Q6589112) (← links)
• Outlier robust feature correspondence by learning based matching process (Q6595032) (← links)
• Differentiable discrete optimization using dataless neural networks (Q6606216) (← links)
• Machine learning constructives and local searches for the travelling salesman problem (Q6606630) (← links)
• Mathematical programming and machine learning for a task allocation game (Q6619766) (← links)
• A neural network based guidance for a BRKGA: an application to the longest common square subsequence problem (Q6635971) (← links)
• Crew recovery optimization with deep learning and column generation for sustainable airline operation management (Q6658347) (← links)
• Monte Carlo tree search for dynamic shortest-path interdiction (Q6659083) (← links)
• Polynomial optimization: tightening RLT-based branch-and-bound schemes with conic constraints (Q6661703) (← links)