arXiv:2604.17708v2 Announce Type: replace Abstract: Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows. Complex OR tasks require adaptive coordination among problem interpretation, mathematical formulation, solver selection, code generation, and iterative debugging. To address this limitation, we propose EvoOR-Agent, a co-evolutionary framework for automated optimization. The framework represents agent workflows as activity-on-edge (AOE)-style networks, making workflow topology, execution dependencies, and altern
The rapid advancement of large language models (LLMs) and the increasing complexity of real-world optimization problems are driving the need for more sophisticated and autonomous AI systems.
This development proposes a framework that could significantly enhance the automation and adaptability of operations research, a critical component for efficiency across various industries.
The shift from hand-crafted to co-evolved and interpretable agent architectures allows AI to more autonomously manage complex problem-solving workflows, reducing human intervention and improving flexibility.
- · AI software developers
- · Logistics and supply chain sectors
- · Manufacturing optimization
- · Operations research practitioners
- · Companies reliant on static, hard-coded optimization systems
- · Entry-level operations analysts performing repetitive workflow tasks
Increased efficiency and adaptability in complex operational planning and resource allocation.
Broad adoption across industries leading to significant productivity gains and reduced operational costs.
The development of fully autonomous enterprise-level optimization agents, redefining white-collar work in operations and strategy.
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Read at arXiv cs.AI