arXiv:2606.27611v1 Announce Type: new Abstract: Operations Research (OR) provides a rigorous framework for high-stakes decision-making, but effective OR modeling requires substantial domain knowledge, mathematical abstraction, and solver expertise. Recent LLM-based systems automate parts of this pipeline, yet remain limited by low accuracy on complex problems, opaque outputs, and narrow solver support. We propose COOPA (COoperative OPerations Agent), a modular LLM-agent architecture for interpretable and scalable OR decision support. It combines three components: iterative confidence-based mod
The increasing complexity of operations research problems and the rapid advancements in LLM capabilities are converging, creating a need for more sophisticated AI-driven solutions.
This development represents a significant step towards automating and enhancing high-stakes decision-making in complex operational environments, previously requiring extensive human expertise.
The ability of AI agents to tackle complex Operations Research problems with improved accuracy and interpretability will transform how organizations optimize logistics, supply chains, and resource allocation.
- · Logistics & Supply Chain Management
- · Consulting Firms (implementing AI OR solutions)
- · AI/LLM Developers
- · Manufacturing & Operations
- · Manual Operations Research Analysts (for routine tasks)
- · Inefficient Legacy Systems
- · Companies with Static OR Models
Companies gain more efficient and adaptive decision-making processes for complex operational challenges.
This leads to significant cost savings, optimized resource utilization, and increased resilience in dynamic environments.
The widespread adoption of such agents could reshape entire industries by enabling unprecedented levels of operational efficiency and strategic agility.
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Read at arXiv cs.LG