mlsanityData to 7 Oct 2026

Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process

arXiv 2401.03244 ↗First posted 6 Jan 2024math.OC cs.AIcited by 11

Not on the map. Why?

Abstract (arXiv)

The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform OR, this paper aims to inspire further research and innovation in the development of AI-enhanced OR methods and tools. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 86 of its 166 references that are in the map. First posted on arXiv 6 Jan 2024.

Builds on

86 of 166 references.

References · 86

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  7. 2018How Powerful are Graph Neural Networks?cited by 4,355
  8. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  9. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  10. 2015Trust Region Policy Optimizationcited by 2,503
  11. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  12. 2023Code Llama: Open Foundation Models for Codecited by 2,105
38 more
  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  2. 2023LIMA: Less Is More for Alignmentcited by 1,080
  3. 2016An overview of gradient descent optimization algorithmscited by 1,021
  4. 2016Learning to learn by gradient descent by gradient descentcited by 616
  5. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  6. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 588
  7. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 557
  8. 2018Behavioral Cloning from Observationcited by 482
  9. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  10. 2017Smart "Predict, then Optimize"cited by 332
  11. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 313
  12. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  13. 2016A Network-based End-to-End Trainable Task-oriented Dialogue Systemcited by 252
  14. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  15. 2019Differentiation of Blackbox Combinatorial Solverscited by 224
  16. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  17. 2016Learning to Optimizecited by 195
  18. 2020Solving Mixed Integer Programs Using Neural Networkscited by 178
  19. 2019SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solvercited by 173
  20. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 156
  21. 2021End-to-End Constrained Optimization Learning: A Surveycited by 154
  22. 2014From Predictive to Prescriptive Analyticscited by 142
  23. 2023NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptionscited by 133
  24. 2020One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Controlcited by 114
  25. 2017Learned Optimizers that Scale and Generalizecited by 109
  26. 2019Reinforcement Learning for Integer Programming: Learning to Cutcited by 108
  27. 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
  28. 2018Understanding and correcting pathologies in the training of learned optimizerscited by 97
  29. 2020Hybrid Models for Learning to Branchcited by 95
  30. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 88
  31. 2019Smart Predict-and-Optimize for Hard Combinatorial Optimization Problemscited by 88
  32. 2023Everything is Connected: Graph Neural Networkscited by 85
  33. 2018Learning to Branchcited by 78
  34. 2020Parameterizing Branch-and-Bound Search Trees to Learn Branching Policiescited by 62
  35. 2022Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learningcited by 58
  36. 2017Learning Gradient Descent: Better Generalization and Longer Horizonscited by 50
  37. 2021Learning to Select Cuts for Efficient Mixed-Integer Programmingcited by 48
  38. 2016Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPUcited by 46

50 most cited of 86 All 86 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 6; 2025: 5.

11 papers.

  1. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  2. 2024LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problemscited by 18
  3. 2024Evaluating LLM Reasoning in the Operations Research Domain with ORQAcited by 12
  4. 2024From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifestocited by 11
  5. 2025Large Language Models in Operations Research: Methods, Applications, and Challengescited by 4
  6. 2024All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformerscited by 1
  7. 2025Expressive Power of Implicit Models: Rich Equilibria and Test-Time Scalingcited by 1
  8. 2024The Evolution and Future Perspectives of Artificial Intelligence Generated Contentcited by 0
  9. 2025Optimization Problem Solving Can Transition to Evolutionary Agentic Workflowscited by 0
  10. 2025Conformal Mixed-Integer Constraint Learning with Feasibility Guaranteescited by 0
  11. 2025Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selectioncited by 0