A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time

Autor: Nadine Meskens, Christine Di Martinelly
Přispěvatelé: Centre de Recherches et d'Etudes en Gestion Industrielle (CREGI), Facultés Universitaires Catholiques de Mons (FUCAM), Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Département Gestion de Production et des Opérations
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: International Journal of Production Economics
International Journal of Production Economics, Elsevier, 2017, 191, pp.323-334. ⟨10.1016/j.ijpe.2017.05.014⟩
International Journal of Production Economics, 2017, 191, pp.323-334. ⟨10.1016/j.ijpe.2017.05.014⟩
ISSN: 0925-5273
DOI: 10.1016/j.ijpe.2017.05.014⟩
Popis: This paper addresses the detailed assignment of nurses to surgical operations taking into account the skills requirements. We consider the building of weekly nurse schedule roster by assigning the nurses to surgical operations while generating teams which have strong affinities and minimising nurse idle times. Nurses are assigned to shifts based on their availability, legal constraints on their working hours and the elective surgery schedule. Building on the e-constraint method, we propose a new bi-objective approach that can solve the problem faster and more accurately, as well as provide insight into the trade-offs between the two objectives. The approach is also used to gain more insight into the problem and evaluate the impact of nurse settings. In this paper, we considered the impact of using circulating and scrub nurses or using polyvalent (multi-skilled) nurses. In all instances and settings, the affinities between the surgical team members were more sensitive to variations in idle time. Furthermore, the use of polyvalent (multi-skilled) nurses yielded rosters with reduced idle time and better surgical team member affinities.
Databáze: OpenAIRE