DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension: static benchmarks encourage benchmark overfitting, while uncalibrated generators obscure algorithmic capability with stochastic noise. To resolve this, we introduce \textbf{DynaSchedBench}, a diagnostic framework for DFJSP that rigorously controls the instance-generation process. Instead of relying on parameter sampling, our approach utilizes Sequential Event-Space Calibrator (SESC) that compute
The increasing complexity and practical deployment of LLMs for high-stakes optimization problems, like scheduling, are exposing the limitations of current evaluation methodologies, necessitating more robust benchmarking solutions.
Improved, calibrated benchmarks are crucial for accurately assessing the capabilities of AI-based scheduling agents, preventing over-optimistic deployment, and guiding future research toward generalizable solutions.
The introduction of DynaSchedBench provides a more reliable framework for evaluating AI scheduling agents, moving beyond static benchmarks and uncalibrated generators that can mask true algorithmic performance.
- · AI-powered logistics companies
- · Robotics and automation sectors
- · Researchers in neural combinatorial optimization
- · Developers of poorly generalized LLM scheduling agents
- · Organizations relying on uncalibrated scheduling benchmarks
More accurate performance comparisons of LLM-based scheduling agents will become possible.
This will accelerate the development and adoption of robust, generalizable AI scheduling solutions across industries.
Improved AI scheduling could lead to significant efficiency gains and cost reductions in manufacturing, supply chains, and resource allocation, potentially impacting global economic productivity.
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Read at arXiv cs.AI