Hybrid Model of Genetic Algorithms and Tabu Search Memory for Nurse Scheduling Systems
نویسندگان
چکیده
The main challenge of Nurse Scheduling Problem (NSP)is designing a nurse schedule that satisfies nurses preferences at minimal cost violating the soft constraints. This makes NSP an NP-hard problem with no perfect solution yet. In this study, two meta-heuristics procedures: Genetic Algorithm (GA) and Tabu Search (TS) memory was applied for development automatic hospital scheduling system (GATS_NSS). data collected from nursing services unit Federal Medical Centre (FMC) in Nigeria 151 staffs preprocessed adopted training GATS_NSS. implemented Java Selection, Evaluation Operators (Crossover Mutation) GA alongside properties TS. Nurses’ shift ward allocation optimized based on defined constraints case study results obtained showed GAT_NSS returned average accuracy 94%, 99% rate, 0% duplication, 0.5% clash improvement computing time 94% over manual approach.
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ژورنال
عنوان ژورنال: International Journal of Service Science, Management, Engineering, and Technology
سال: 2022
ISSN: ['1947-9603', '1947-959X']
DOI: https://doi.org/10.4018/ijssmet.297494