TY - GEN
T1 - A Local Search Algorithm for the Assignment and Work Balance of a Health Unit
AU - Díaz-Escobar, Néstor
AU - Rodríguez, Pamela
AU - Semblantes, Verónica
AU - Taylor, Robert
AU - Morillo-Torres, Daniel
AU - Gatica, Gustavo
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In any healthcare service, guidelines regarding the number of staff and how to respond to patient demand must be followed. In Chile, to ensure there is 24/7 care, coordinators use a manual allocation model system called “The Fourth Shift” (TFS) to assign staff. The model has a four-day shift pattern which allocates 48 h of work and 48 h of rest. However, scheduling healthcare workers is always a challenge, as there are administrative, legal and individual constraints. A balanced shift assignment, meaning one that considers work hours and specific staff requests, has a significant impact on an overall work environment. To find a fair balance, this paper proposes a two-phase heuristic. The first is a constructive phase and the second is a local search phase. This paper simultaneously incorporates six Key Performance Indicators (KPIs) and chance events aiming at leveling the workload for healthcare workers. The heuristics are validated with one-month shifts for a healthcare service with 12 nurses. The results validate the effectiveness of the proposed approach by disrupting the solution with five cumulative scenarios.
AB - In any healthcare service, guidelines regarding the number of staff and how to respond to patient demand must be followed. In Chile, to ensure there is 24/7 care, coordinators use a manual allocation model system called “The Fourth Shift” (TFS) to assign staff. The model has a four-day shift pattern which allocates 48 h of work and 48 h of rest. However, scheduling healthcare workers is always a challenge, as there are administrative, legal and individual constraints. A balanced shift assignment, meaning one that considers work hours and specific staff requests, has a significant impact on an overall work environment. To find a fair balance, this paper proposes a two-phase heuristic. The first is a constructive phase and the second is a local search phase. This paper simultaneously incorporates six Key Performance Indicators (KPIs) and chance events aiming at leveling the workload for healthcare workers. The heuristics are validated with one-month shifts for a healthcare service with 12 nurses. The results validate the effectiveness of the proposed approach by disrupting the solution with five cumulative scenarios.
KW - Healthcare service
KW - Heuristics
KW - Nurse rostering
KW - Shift assignment
UR - http://www.scopus.com/inward/record.url?scp=85113765313&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80906-5_14
DO - 10.1007/978-3-030-80906-5_14
M3 - Conference contribution
AN - SCOPUS:85113765313
SN - 9783030809058
T3 - Studies in Computational Intelligence
SP - 208
EP - 222
BT - Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future - Proceedings of SOHOMA LATIN AMERICA 2021
A2 - Trentesaux, Damien
A2 - Borangiu, Theodor
A2 - Leitão, Paulo
A2 - Jimenez, Jose-Fernando
A2 - Montoya-Torres, Jairo R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st Latin-American Workshop on Service-Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA LATIN AMERICA 2021
Y2 - 27 January 2021 through 28 January 2021
ER -