REVIEW 5 cited by
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
read the original abstract
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.
Forward citations
Cited by 5 Pith papers
-
PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
Training an LLM as a multi-turn agent that runs and repairs solver code raises verified optimization solve rates, with the 4B PEARL model outperforming DeepSeek-V3.2-685B in aggregate.
-
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
-
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve uses LLMs and RL to generate certified inventory policies that outperform classical and deep learning methods on synthetic and real data while providing multi-period performance guarantees.
-
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
-
A note on the convergence guarantees of RLT-based algorithms for polynomial optimization
A note that flags an oversight in RLT convergence proofs for polynomial optimization and recovers correctness via one extra natural assumption.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.