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📄 Research Article

School network reorganization under educational and spatial constraints using classical and quantum optimization

● high · 2026-08-09ai-education, k-12, equity, simulation, systematic-review, policy, data-driven, optimization

Synthesis: This paper develops an optimization framework for school network reorganization that integrates geographic, administrative, and educational criteria into an Integer Linear Programming formulation. Applied to the complete public school network of Calabria, Italy, and extended to a hybrid quantum optimization setting, the approach identifies optimal school aggregation plans under different policy scenarios while preserving Equity and accessibility. The framework serves as a decision-support tool for sustainable educational planning in the era of AI-enhanced operations research.

Optimization Framework

The framework models school dimensioning as a constrained optimization problem balancing:

  • Demographic trends: Projected enrollment changes across regions
  • Territorial accessibility: Travel distances and geographic constraints
  • Educational requirements: Class size limits, curriculum coverage, teacher allocation
  • Institutional constraints: Administrative boundaries and policy directives

    • A synthetic benchmark generator enables scalability testing, while the real-world Calabria case study validates practical applicability using actual institutional, territorial, and demographic data.

    Key Findings

  • Optimal aggregation plans: Framework identifies consolidation strategies that maintain educational quality while improving resource efficiency
  • Policy scenario analysis: Model adapts to different policy scenarios, allowing decision-makers to explore trade-offs between cost, accessibility, and educational outcomes
  • Quantum compatibility: Reformulation as a constrained quadratic model demonstrates readiness for emerging quantum-computing technologies
  • Sustainable planning: Robust methodology supports equitable and sustainable school network planning

    • Educational Planning Implications

    As school districts worldwide face declining enrollments and budget pressures, AI-powered optimization offers data-driven alternatives to politically-driven consolidation decisions. The framework's multi-criteria approach ensures that educational quality and equity considerations are not sacrificed to purely financial optimization.

    Connected Concepts

  • Equity
  • AI Education
  • K 12

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    • Citation

    Ciacco, A., Di Puglia Pugliese, L., & Guerriero, F. (2026). School network reorganization under educational and spatial constraints using classical and quantum optimization. arXiv:2608.05427v1.