Quantum assisted workforce scheduling

Problem

As the energy transition accelerates, grid operators such as Alliander face increasing pressure on an already scarce workforce of qualified technicians. Every week, hundreds of maintenance and grid-expansion tasks must be assigned while accounting for qualifications, travel times, time windows, operational dependencies, and business priorities. Finding high-quality schedules within the limited decision window available to planners (corresponding to a computational budget of 1 hour) is a challenging combinatorial optimization problem, making it an attractive candidate for quantum computing.

Solution

Together with Alliander, we developed a hybrid quantum-classical workforce scheduling approach. Instead of mapping the entire scheduling problem onto a quantum annealer, we designed a new hybrid workflow that uses quantum annealing for a route-recombination subproblem. This allows the overall problem to scale far beyond the size of quantum hardware, making it possible to tackle realistic planning instances involving hundreds of tasks and technicians, while respecting core operational constraints and business rules.

The resulting solution consistently generated competitive schedules within only ~20% of the decision window, demonstrating that quantum annealing can already be scaled to real-size operational challenges today when combined with the right classical orchestration.

Benefit

Better workforce scheduling helps grid operators like Alliander make the most of scarce technical expertise, enabling faster grid expansion, more efficient maintenance, and improved service reliability. As the energy transition increases demand for skilled technicians, optimizing how those resources are deployed can help accelerate the delivery of critical energy infrastructure.

At the same time, this project demonstrates a practical application of quantum technology in a real operational setting. By successfully combining quantum annealing into an industry-scale scheduling workflow, the project shows how quantum computing can already support complex planning problems today, moving beyond small laboratory demonstrations toward real-world impact.