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4285. Labour scheduling for healthcare workers using efficient strategies
Invited abstract in session TC-15: Staffing and workforce planning and scheduling, stream OR in Health Services (ORAHS).
Tuesday, 12:30-14:00Room: 18 (building: 116)
Authors (first author is the speaker)
1. | Paula Antón Maraña
|
Applied Economics, University of Burgos | |
2. | Joaquín Pacheco
|
Applied Economy, University of Burgos | |
3. | JULIO CESAR PUCHE
|
UNIVERSITY OF BURGOS | |
4. | Silvia Casado
|
University of Burgos |
Abstract
Labour scheduling takes on special relevance in the healthcare field as most professionals work alternate shifts, which makes it difficult to balance work and personal life. One of the common practices is the allocation of schedules according to workers' preferences for each planning period. This results in models where professionals are assigned shifts in order to maximise their preferences while taking into account labour constraints. Usually these models include such constraints in their formulation, which results in excessively complex models. Thus, they can only be applied in very small instances and require heuristic and metaheuritic techniques to solve real instances such as Variable Neighbourhood Search, Simulated Annealing, Tabu Search, Iterated Search and Genetic Algorithms, or even hybrid approaches, mate-heuristic or hyperheuristic strategies. In this work we propose a strategy applied to a real case of an elderly people's home, which consists of the following steps: a) to generate all possible sets of shifts ("patterns") that can be assigned to a professional that meet the labour constraints; b) to propose a model for assigning professionals to patterns. The labour constraints are satisfied, even if they do not appear explicitly, and the resulting model can be solved exactly on real instances of large size. This efficient strategy maximises the satisfaction of healthcare staff preferences, improving their productivity and the quality of patient care.
Keywords
- Scheduling
- Health Care
Status: accepted
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