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Context-rich data sets for school operations models and methods

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  • Ozel, Aysu
  • Smilowitz, Karen

Abstract

In transportation and logistics problems, such as the traveling salesman problem or the vehicle routing problem, the geographic distribution of nodes can significantly impact both the solutions obtained and the performance of solution approaches. Therefore, it is common for researchers to share test instances for meaningful comparisons. In some contexts, this is more challenging when data are protected and cannot be shared. This is particularly true for transportation and logistics problems found in public school operations. Despite growing literature, proposed models and solution approaches are rarely compared across papers because data protection regulations prohibit sharing data. At the same time, randomly generated data can miss critical patterns existing in reality that may impact equitable access to education. In this paper, we introduce a framework to create context-rich data sets for school operations models and methods based on publicly available data that reflect public school district characteristics in the United States.

Suggested Citation

  • Ozel, Aysu & Smilowitz, Karen, 2026. "Context-rich data sets for school operations models and methods," Socio-Economic Planning Sciences, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:soceps:v:104:y:2026:i:c:s0038012125002551
    DOI: 10.1016/j.seps.2025.102406
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