发表机构
University of Calabria; Consiglio Nazionale delle Ricerche (ICAR-CNR)（卡拉布里亚大学; 意大利国家研究委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对教育与空间约束下的学校网络重组问题，提出结合地理等标准的整数线性规划优化框架，通过合成基准与意大利卡拉布里亚大区真实案例验证，还实现为混合量子优化模型，可作为学校规划的决策支持工具。
AI 中文摘要
学校网络重组是一项战略规划问题，需在平衡人口趋势、地域可达性、教育要求和机构约束的同时，确保公共资源的高效分配。本文提出一种用于学校规模决策的优化框架，基于整合地理、行政和教育标准的新型整数线性规划（Integer Linear Programming）公式。引入合成基准生成器以评估模型在人工实例上的可扩展性和计算性能，同时利用意大利卡拉布里亚大区完整公立学校网络的实际机构、地域和人口数据开展真实案例研究。所提方法可在不同政策场景下有效识别最优合并方案，同时保留教育系统的结构特征。此外，该模型被重新表述为约束二次模型，并在混合量子优化环境中实现，展示其与新兴量子技术的兼容性。结果凸显了所提方法的稳健性及其作为可持续、公平的学校网络规划决策支持工具的潜力。
英文摘要
School network reorganization is a strategic planning problem that requires balancing demographic trends, territorial accessibility, educational requirements, and institutional constraints while ensuring an efficient allocation of public resources. This paper proposes an optimization framework for school dimensioning decisions based on a novel Integer Linear Programming formulation integrating geographical, administrative, and educational criteria. A synthetic benchmark generator is introduced to evaluate the scalability and computational performance of the model on artificial instances, while a real-world case study involving the complete public school network of the Calabria region (Italy) is conducted using actual institutional, territorial, and demographic data. Furthermore, the model is implemented within a hybrid quantum optimization environment. The results show that the proposed formulation can be effectively solved by exact classical optimization and can also be represented and evaluated within a hybrid quantum-classical optimization framework. The computational experiments provide a proof-of-concept assessment of the applicability of hybrid quantum optimization to the considered school aggregation problem, while also highlighting the current computational advantage of classical optimization and the limitations of the tested benchmark instances.
