Pith. sign in

REVIEW 2 cited by

Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2401.03244 v2 pith:C22B2FXE submitted 2024-01-06 math.OC cs.AI

Zhenan Fan , Bissan Ghaddar , Xinglu Wang , Linzi Xing , Yong Zhang , Zirui Zhou This is my paper · ORCID
classification math.OCcs.AI
keywords researchoperationsartificialintelligencemodelprocessacrossadvancement
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language

    cs.AI 2026-05 reject novelty 7.0 of 10

    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.

  2. Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees

    cs.LG 2025-06 reject novelty 6.0 of 10

    C-MICL embeds conformal prediction sets into mixed-integer constraint learning, claiming a 1-alpha probability that optimized solutions are feasible for the true unknown constraint.

Pith tools