Quantum Annealing Solves Staff Scheduling across Multiple School Levels, Demonstrating Practical Resource Allocation in Educational Environments

Staff scheduling presents a significant challenge for educational institutions, requiring careful consideration of availability, expertise, and fairness when assigning personnel across multiple sites and levels. Alessia Ciacco, Francesca Guerriero, and Eneko Osaba, from the University of Calabria and TECNALIA, Basque Research and Technology Alliance, address this complex problem with a novel approach to staff allocation, stemming from a real-world case study in Calabria, Italy. The team develops an optimisation model and investigates its solution using quantum annealing, demonstrating its ability to generate balanced assignments quickly. This research highlights the practical potential of advanced optimisation methods for improving resource allocation not only within education, but also across a wide range of complex scheduling tasks.

Scientists created a mathematical model representing this staffing challenge, considering constraints such as staff availability, skills, contractual limits, gender balance, and the need for service continuity. They tested both traditional optimization methods and a quantum-inspired approach to find the best solutions. The results demonstrate that quantum-inspired algorithms hold promise for solving complex scheduling problems, particularly as quantum hardware improves.

For smaller and medium-sized problems, both approaches performed well, achieving optimal or near-optimal solutions quickly. However, for larger, more complex instances, the quantum-inspired method maintained solution quality and speed advantages. To tackle this challenge, the team pioneered an optimization model and investigated a solution approach leveraging quantum annealing, a promising technique for solving complex combinatorial problems. Scientists meticulously modeled the real-world scenario, accounting for the unique organizational, spatial, and regulatory demands of the Italian school system. They then translated this problem into a format suitable for a D-Wave quantum annealer, a specialized computer designed to find optimal solutions. Experiments using real-world data demonstrated that quantum annealing is capable of producing balanced assignments in short runtimes, offering a significant advantage over traditional optimization methods as problem complexity increases. This achievement highlights the potential of quantum computing to address complex resource allocation tasks in highly regulated environments like public education.

Quantum Annealing Optimizes School Staff Allocation

Scientists developed a novel optimization model to address the complex problem of staff allocation within a public school system in Calabria, Italy. The research focuses on efficiently distributing personnel across kindergartens, primary schools, and secondary schools while adhering to constraints related to staff availability, required competencies, and fairness considerations. To tackle this challenge, the team investigated a solution approach based on quantum annealing, a technique particularly well-suited for complex combinatorial optimization problems. Experiments demonstrate that quantum annealing is capable of producing balanced staff assignments in short runtimes, offering a practical solution for educational scheduling. The team utilized D-Wave’s Constrained Quadratic Model (CQM) hybrid solver to formulate and test the problem on quantum hardware, enabling the exploration of large solution spaces. This approach leverages quantum principles to overcome computational barriers that often trap classical algorithms, providing evidence for the practical applicability of quantum optimization methods in educational settings.

Calabria School Staffing Optimisation Achieved

This research presents a novel optimization model and solution approach for complex staff allocation, demonstrated through a case study of a school system in Calabria, Italy. The team successfully developed a method to assign personnel across multiple school levels, kindergarten, primary, and secondary, while adhering to constraints related to availability, competencies, and fairness. Computational experiments using real-world data demonstrate the model’s ability to generate balanced assignments within a reasonable timeframe, suggesting its practical value for educational scheduling and broader resource allocation challenges. The model accounts for factors such as working hour limits, mandatory breaks, gender requirements, and individual staff preferences, all while striving to minimize multi-site assignments.