Hybrid Model of Genetic Algorithms and Tabu Search Memory for Nurse Scheduling Systems

Hybrid Model of Genetic Algorithms and Tabu Search Memory for Nurse Scheduling Systems

Adebayo A. Abayomi-Alli, Frances Omoyemen Uzedu, Sanjay Misra, Olusola O. Abayomi-Alli, Oluwasefunmi T. Arogundade
DOI: 10.4018/IJSSMET.297494
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The main challenge of Nurse Scheduling Problem (NSP)is designing a nurse schedule that satisfies nurses preferences at minimal cost of violating the soft constraints. This makes the 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 the development of an automatic hospital nurse scheduling system (GATS_NSS). The data collected from the nursing services unit of a Federal Medical Centre (FMC) in Nigeria with 151 nursing staffs was preprocessed and adopted for training the GATS_NSS. The system was implemented in Java for Selection, Evaluation and Genetic Operators (Crossover and Mutation) of GA alongside the memory properties of TS. Nurses’ shift and ward allocation was optimized based on defined constraints of the case study hospital and the results obtained showed that GAT_NSS returned an average accuracy of 94%, 99% allocation rate, 0% duplication, 0.5% clash and an average improvement in the computing time of 94% over the manual approach.
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1 Introduction

Scheduling is the course of action of substances (individuals, undertakings, vehicles, addresses, tests, gatherings, and so forth) into an example in space-time so that requirements are fulfilled and certain objectives are accomplished (Rivera & Mesa, 2015). Developing schedules is not an easy task because several factors must be considered, some of which are time, space and other (frequently restricted) resources. The limitations are connections among the resources or between the elements and the patterns that limit the construction of the schedule. Most scheduling tasks are described as NP-hard problem due to the enormous administrative reuirements and optimizations.Shift Scheduling Problem (SSP) is considered as an advanced N-P hard problem (Özder, Özcan&Eren, 2019) and Nurse Scheduling Problem (NSP) fall in this category. ShiftScheduling (NS) has found application in different sectors such as examination scheduling (Abayomi-Alli et al., 2019a); (Abayomi-Alli et al., 2019b); Transportation (Guoet al., 2017), flight scheduling (Chen et al., 2019), machine scheduling (Nedaei, 2018;Wu et al., 2018), social event scheduling (Bikakis, Kalogeraki&Gunopulos, 2019), occupation shop planning (Piroozfard, Wong&Hassan, 2016), e.t.c.

The service sector like the health care industry is presently experiencing massive expansion while still contributing positively to the Gross Domestic Product (GDP) of several countries(Ishola and Olusoji, 2020).The sector is highly service oriented and constitutes an important part of the service sector(Sisodia, 2019)but it involves varaious parties such as physicians, administrators, nurses, lab scientist, etc. to collaborate in order to provide care to patients(Barhounet al., 2019).Nurse’s satisfaction with their schedules or roster helps in motivating them to provide quality care to patientsespecially when nursing services is the most important predictor of the patient’s overall satisfaction with the hospital care (Olowe and Odeyemi, 2019;Gishuet al., 2019).

Causmaecker&Berghe (2011) defined NSP as the appointment of several nurses to several shifts in such a way that hospital rules are not violated. In NSP, the objective is to appoint shifts to the nurses while satisfying the hospital’s rules during the planning period. Hospitals and medical clinics' human resource, represent an extensive piece of the clinic's annual budgets. Hospital policy makers are therefore tasked with the responsibility of maximizing the available nurses and other health workers effectively. The issue is worsened by the inadequate number of nurses in most hospitals and medical centre especially in developing countries where the shortage of healthcare workers is more prevalent(Misedaet al., 2017)and poor working environments in the work place will lead to unmotivated employees (Galli, 2020).

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