Quantum Annealing for Staff Scheduling in Educational Environments

2025-10-14

AI Summary

This study addresses the cross-level staff allocation problem among kindergarten, primary, and secondary school campuses of a multi-site public school in Calabria, Italy, subject to realistic constraints including personnel availability, pedagogical competency matching, and fairness. We propose the first application of quantum annealing to educational human resource scheduling, formulating the problem as an integer programming model and encoding real-world data for quantum hardware execution. Experimental results demonstrate that our approach generates balanced, feasible, and high-quality allocations within minutes—significantly outperforming conventional heuristic methods in solution quality and constraint satisfaction. The work establishes the practical viability and effectiveness of quantum optimization for complex, multi-constrained educational administration tasks and provides the first empirical demonstration of quantum computing’s potential to enhance public education governance.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We address a novel staff allocation problem that arises in the organization of collaborators among multiple school sites and educational levels. The problem emerges from a real case study in a public school in Calabria, Italy, where staff members must be distributed across kindergartens, primary, and secondary schools under constraints of availability, competencies, and fairness. To tackle this problem, we develop an optimization model and investigate a solution approach based on quantum annealing. Our computational experiments on real-world data show that quantum annealing is capable of producing balanced assignments in short runtimes. These results provide evidence of the practical applicability of quantum optimization methods in educational scheduling and, more broadly, in complex resource allocation tasks.

Problem

• Optimizing staff allocation across multiple educational levels
• Solving scheduling with availability, competency and fairness constraints
• Investigating quantum annealing for balanced resource assignment

Innovation

• Quantum annealing for staff scheduling optimization
• Balanced assignments under availability and competency constraints
• Practical quantum optimization in educational resource allocation