A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem
Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types t...
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my.uum.repo.263322019-08-28T03:24:41Z http://repo.uum.edu.my/26332/ A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem Ramli, Razamin Abd Rahman, Rosshairy Rohim, Nurdalila QA75 Electronic computers. Computer science Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that could not be achieved in the manually generated rosters, which are still being practiced. Hence, this study focused on solving those issues arising in nurse rostering problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with the hill climbing is aiming at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model is developed with the goal of satisfying the hard constraints, while minimizing the violation of soft constraints in such a way that fulfill hospital’s rules and nurses’ preferences. The real data used for this highly constrained NRPs was obtained from a large Malaysian hospital. Specifically, three main phases were involved in developing the hybrid model, which are generating an initial roster, updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results show that at a larger value of pheromone, the chance of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that fulfilled all the four important criteria, which are coverage, quality, flexibility, and cost. Subsequently, the hybrid model is also beneficial to the hospital’s management whereby nurses can be scheduled with balanced distribution of shifts, which fulfill their preferences as well. Universiti Utara Malaysia 2019 Article PeerReviewed application/pdf en http://repo.uum.edu.my/26332/1/JICT%2018%203%202019%20305%20326.pdf Ramli, Razamin and Abd Rahman, Rosshairy and Rohim, Nurdalila (2019) A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem. Journal of Information and Communication Technology (JICT), 18 (3). pp. 305-326. ISSN 1675-414X http://www.jict.uum.edu.my/index.php/currentissues#a4 |
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QA75 Electronic computers. Computer science Ramli, Razamin Abd Rahman, Rosshairy Rohim, Nurdalila A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
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Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that could not be achieved in the manually generated rosters, which are still being practiced. Hence, this study focused on solving those issues arising in nurse rostering problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with the hill climbing is aiming at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model is developed with the goal of satisfying the hard constraints, while minimizing the violation of soft constraints in such a way that fulfill hospital’s rules and nurses’ preferences. The real data used for this highly constrained NRPs was obtained from a large Malaysian hospital. Specifically, three main phases were involved in developing the hybrid model, which are generating an initial roster, updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results show that at a larger value of pheromone, the chance of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that fulfilled all the four important criteria, which are coverage, quality, flexibility, and cost. Subsequently, the hybrid model is also beneficial to the hospital’s management whereby nurses can be scheduled with balanced distribution of shifts, which fulfill their preferences as well. |
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Article |
author |
Ramli, Razamin Abd Rahman, Rosshairy Rohim, Nurdalila |
author_facet |
Ramli, Razamin Abd Rahman, Rosshairy Rohim, Nurdalila |
author_sort |
Ramli, Razamin |
title |
A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
title_short |
A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
title_full |
A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
title_fullStr |
A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
title_full_unstemmed |
A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
title_sort |
hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem |
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Universiti Utara Malaysia |
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2019 |
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http://repo.uum.edu.my/26332/1/JICT%2018%203%202019%20305%20326.pdf http://repo.uum.edu.my/26332/ http://www.jict.uum.edu.my/index.php/currentissues#a4 |
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