Explaining a staff rostering problem using partial solutions.

Catalano, GianCarlo A.P.I.; Brownlee, Alexander E.I.; Cairns, David; McCall, John A.W.; Fyvie, Martin; Ainslie, Russell

Abstract

There are many critical optimisation tasks that metaheuristic approaches have been shown to be able to solve effectively. Despite promising results, users might not trust these algorithms due to their intrinsic lack of interpretability. This paper demonstrates the use of explainability to resolve this issue by producing human-interpretable insights that focus on simplicity, fitness and linkage. Our explainability approach revolves around the concept of Partial Solutions, which assist in breaking up the solutions of optimisation problems into smaller components. We first expand upon our previous research proposing the technique, and then provide a use case on the Staff Rostering task: a large and otherwise uninterpretable optimisation problem with ethical implications due to its direct impact on humans. The explanations consist in rota assignments for interacting groups of workers, along with the reasons why they are interacting. Lastly, some experiments are used to ascertain that the algorithms work as intended and for hyperparameter tuning. The results suggest that our methodology is capable of presenting insightful information for the Staff Rostering problem, by producing both local explanations of solutions and global explanations of the problem definition.

Citation

CATALANO, G.A.P.I., BROWNLEE, A.E.I., CAIRNS, D., MCCALL, J.A.W., FYVIE, M. and AINSLIE, R. 2025. Explaining a staff rostering problem using partial solutions. In Bramer, M. and Stahl, F. (eds) Artificial intelligence XLI: proceedings of the 44th SGAI (Specialist Group on Artificial Intelligence) International conference on artificial intelligence 2024 (AI 2024), 17-19 December 2024, Cambridge, UK. Lecture notes in computer science, 15447. Cham: Springer [online], part II, pages 179-193. Available from: https://doi.org/10.1007/978-3-031-77918-3_13

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 44th SGAI (Specialist Group on Artificial Intelligence) International conference on artificial intelligence 2024 (AI 2024)
• Start Date: Dec 17, 2024
• End Date: Dec 19, 2024
• Acceptance Date: Nov 29, 2024
• Online Publication Date: Nov 29, 2024
• Publication Date: Dec 31, 2025
• Deposit Date: Jan 7, 2025
• Publicly Available Date: Nov 30, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Issue: Part II
• Pages: 179-193
• Series Title: Lecture notes in computer science (LNCS)
• Series Number: 15447
• Series ISSN: 0302-9743 ; 1611-3349
• Book Title: Artificial intelligence XLI: proceedings of the 44th SGAI (Specialist Group on Artificial Intelligence) International conference on artificial intelligence 2024 (AI 2024), 17-19 December 2024, Cambridge, UK
• ISBN: 9783031779176
• DOI: https://doi.org/10.1007/978-3-031-77918-3_13
• Keywords: Explainability; XAI; Job scheduling; Metaheuristics
• Public URL: https://rgu-repository.worktribe.com/output/2593188