Abstract
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.
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Notes
- 1.
As we are operating on cyclical rotas, \(x_0=x_N\) and \(x_{N+1}=x_1\).
- 2.
Apart from the best penalties for ES 200gen, \(\lambda =100\).
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Acknowledgment
The authors would like to acknowledge the support of staff members at the Port of Aberdeen that have kindly contributed to this research by providing the historical pilot roster pattern, demand data and feedback that informed the problem formalisation.
This work was supported by the Port of Aberdeen and InnovateUK through a Knowledge Transfer Partnership project (KTP reference number 12046).
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Collins, J., Zăvoianu, AC., McCall, J.A.W. (2023). Comparison of Simulated Annealing and Evolution Strategies for Optimising Cyclical Rosters with Uneven Demand and Flexible Trainee Placement. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_39
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