Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constructing good rosters. In the present work we propose a metaheuristic-based strategy for designing optimal cyclical rosters that can accommodate uneven demand patterns. A key part of our approach relies on integrating an efficient optimal trainee placement module within the metaheuristic-driven search. Results obtained on a real-life problem proposed by the Port of Aberdeen indicate that by incorporating a demand-informed random rota initialisation procedure, our strategy can generally achieve high-quality end-of-run solutions when using relatively simple base solvers like simulated annealing (SA) and evolution strategies (ES). While ES converge faster, SA outperforms quality-wise, with both approaches being able to improve the man-made baseline
