Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement.

Collins, Joseph; Zăvoianu, Alexandru-Ciprian; McCall, John A.W.

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.

Citation

COLLINS, J., ZĂVOIANU, A.-C. and 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. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-47994-6_39

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI 2023)
• Start Date: Dec 12, 2023
• End Date: Dec 14, 2023
• Acceptance Date: Sep 6, 2023
• Online Publication Date: Nov 8, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Sep 15, 2023
• Publicly Available Date: Nov 9, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 451-464
• Series Title: Lecture notes in computer science (LNCS)
• Series Number: 14381
• Series ISSN: 0302-9743; 1611-3349
• Book Title: Artificial intelligence XL: proceedings of the 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI 2023)
• ISBN: 9783031479939; 9783031479946
• DOI: https://doi.org/10.1007/978-3-031-47994-6_39
• Keywords: Simulated annealing; Evolution strategies; Staff rostering; Staff training; Combinatorial optimisation; Uncertainty
• Public URL: https://rgu-repository.worktribe.com/output/2079274