Explaining a staff rostering problem by mining trajectory variance structures.

Fyvie, Martin; McCall, John A.W.; Christie, Lee A.; Zăvoianu, Alexandru-Ciprian; Brownlee, Alexander E.I.; Ainslie, Russell

Abstract

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behaviour and the quality of solutions. In this paper, we apply our method of search trajectory mining through decomposition to the solutions created by a Genetic Algorithm—a non-deterministic, population-based metaheuristic. We complement this method with the use of One-Way ANOVA statistical testing to help identify explanatory features found in the search trajectories—subsets of the set of optimization variables having both high and low influence on the search behaviour of the GA and solution quality. This allows us to highlight these to an end-user to allow for greater flexibility in solution selection. We demonstrate the techniques on a real-world staff rostering problem and show how, together, they identify the personnel who are critical to the optimality of the rosters being created.

Citation

FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., ZĂVOIANU, A.-C., BROWNLEE, A.E.I. and AINSLIE, R. 2023. Explaining a staff rostering problem by mining trajectory variance structures. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 275-290. Available from: https://doi.org/10.1007/978-3-031-47994-6_27

• 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: Aug 29, 2023
• Online Publication Date: Nov 8, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Feb 1, 2024
• Publicly Available Date: Nov 9, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 275-290
• Series Title: Lecture notes in computer science
• 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
• DOI: https://doi.org/10.1007/978-3-031-47994-6_27
• Keywords: Evolutionary algorithms; Artificial intelligence; Search trajectory mining
• Public URL: https://rgu-repository.worktribe.com/output/2139386