Seeing the bigger picture: domain transformation approach to the nurse scheduling problem / Geetha Baskaran, Andrzej Bargiela, Rong Qu // Australian Journal of Basic and Applied Sciences [Dokument elektroniczny]. ... więcej

Seeing the bigger picture: domain transformation approach to the nurse scheduling problem / Geetha Baskaran, Andrzej Bargiela, Rong Qu // Australian Journal of Basic and Applied Sciences [Dokument elektroniczny]. – 2014, 8 (24), Spec. Iss., s. 308-320 : tab. – Tryb dostępu: http://www.ajbasweb.com/old/ajbas/2014/IPN%20JAKARTA/308-320.pdf. – Bibliogr. s. 319-320, Abstr. – Mat. konf.: ICCSIT 2014 : International Conference on Computer Science and Information Technology, Jakarta, Indonesia, 12-13.12.2014. – ISSN 1991-8178

Background: Nurse scheduling is a complex combinatorial optimization problem. Objective: This paper presents a novel approach to solving the nurse scheduling problem by simplifying it through information ... więcej

Background: Nurse scheduling is a complex combinatorial optimization problem. Objective: This paper presents a novel approach to solving the nurse scheduling problem by simplifying it through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Results: In general, the problem consists of assigning one of S different shifts to each nurse (who has one of C possible contracts) over the scheduling period of W weeks. The study selected to depict the scheduling problem is staffed by 12 full time nurses working a contracted 36 hours per week, one part-time nurse working 32 hours per week and 3 other part-time nurses working 20 hours per week, each being assigned to one of the five possible shift types over a period of five weeks. The solution space for such a specific problem involves 16*535, which are approx. 4.6*1025 possible schedules. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences that lead to a re-formulation of the original problem in a much reduced solution space of 8*1017schedules defined through weekly patterns. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. The grouping of the shift types into a prototype shift and the grouping of shift sequences into weekly patterns used in scheduling are collectively referred to as information granulation. The proposed method has been evaluated and has shown a capable of finding high quality schedules. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers deterministic reproducibility of the results. We note that however it does not guarantee the global optimum results.