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Modeling An Intelligent Staff Schedule Using Genetic Algorithm (GA) For Healthcare Environment

Nuzulha Khilwani, Ibrahim and Burairah, Hussin and Abdul Samad , Hasan Basari and Suriayati, Chuprat and Rabiah, Ahmad and Siti Azirah, Asmai (2011) Modeling An Intelligent Staff Schedule Using Genetic Algorithm (GA) For Healthcare Environment. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

Nurse Scheduling Problem (NSP) become one of the challenging domain in healthcare, this is pa11icularly because of the presence of a range of different requirements from organization and staff for continuous days and shifts. Furthermore, the public healthcare institutions work twenty-four hours per day unlike many other organizations. Workers also have different hour of work compared to the other organization with shift hours. From the previous researches and observations, we can say that most nurse scheduling problems are extremely difficult and complex. NSP is a problem of assigning work slots for nurses with reasonable and acceptable tasks distribution towards job satisfaction in an efficient way of automation. NSP is a complex problem due to its many constraints to be solved with a big number of possible combinations. In this study, the problem of nurse scheduling translated into two categories of constraints which are hard and soft constraints, then technically designed into specific representation of Genetic Algorithm (GA) before generating the schedule after steps of the evolution process with few genetic operators. The optimized schedule using Genetic Algorithm which is belongs to the larger class of Evolutionary Computing (EC), is the final solution achieved where we tested the model in MATLAB environment.

Item Type: Monograph (Project Report)
Uncontrolled Keywords: Genetic algorithms
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Library > Projek Jangka Panjang / Pendek > FTMK
Depositing User: Siti Syahirah Ab Rahim
Date Deposited: 28 Oct 2014 16:59
Last Modified: 28 May 2015 04:32
URI: http://eprints.utem.edu.my/id/eprint/13543

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