Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address...

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Main Author: Lim, Huai Tein
Format: Thesis
Language:eng
eng
Published: 2015
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QA Mathematics
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https://etd.uum.edu.my/5794/2/s91515_01.pdf
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institution Universiti Utara Malaysia
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language eng
eng
advisor Ramli, Razamin
topic QA Mathematics
QA Mathematics
spellingShingle QA Mathematics
QA Mathematics
Lim, Huai Tein
Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
description Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Lim, Huai Tein
author_facet Lim, Huai Tein
author_sort Lim, Huai Tein
title Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
title_short Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
title_full Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
title_fullStr Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
title_full_unstemmed Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
title_sort enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2015
url https://etd.uum.edu.my/5794/1/depositpermission_s91515.pdf
https://etd.uum.edu.my/5794/2/s91515_01.pdf
_version_ 1747827983339487232
spelling my-uum-etd.57942021-03-18T08:31:41Z Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem 2015 Lim, Huai Tein Ramli, Razamin Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA Mathematics QA75 Electronic computers. Computer science Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions. 2015 Thesis https://etd.uum.edu.my/5794/ https://etd.uum.edu.my/5794/1/depositpermission_s91515.pdf text eng staffonly https://etd.uum.edu.my/5794/2/s91515_01.pdf text eng 2017-07-24 public Ph.D. doctoral Universiti Utara Malaysia Abdallah, K. S., & Jang, J. (2014). An exact solution for vehicle routing problems with semi-hard resource constraints. 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