Quantum Annealing For Staff Scheduling In Educational Environments

Alessia Ciacco, University of Calabria

Francesca Guerriero, University of Calabria

Date

10/2025

AI Overview

Why it matters

School consolidations have increased operational complexity, making staff scheduling demanding. No scheduling model has accounted for the specific regulatory and spatial needs of non-teaching school collaborators.

Study at a glance

Question: Can quantum annealing effectively solve complex staff scheduling problems in multi-site public schools?
Setting and data: Istituto Comprensivo di Cerisano, Calabria, Italy; 20 school collaborators across 9 campuses (kindergarten, primary, secondary), plus synthetic larger instances up to 40 collaborators.
Method: Mathematical optimization model evaluated via quantum annealing solver against an exact solver (Model or benchmark evaluation).

Key findings

On the real-world school instance, the hybrid quantum annealing solver reached the optimal schedule found by the exact solver across all five runs in about 15.16 seconds.
On synthetic instances, the solver matched the optimal solution in 100% of runs for 25 and 30 collaborators, but reached it in only 20% of runs for 35 collaborators.
The quantum annealing solver failed to produce a feasible schedule within the 10-second limit when scaled to 40 collaborators due to high combinatorial complexity.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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.