Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators
The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution)....
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34632/ http://umpir.ump.edu.my/id/eprint/34632/1/Window-based%20multi-objective%20optimization%20for%20dynamic%20patient%20scheduling.pdf |
| _version_ | 1848824558672412672 |
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| author | Mahmed, Ali Nader Mohd Nizam Mohmad, Kahar |
| author_facet | Mahmed, Ali Nader Mohd Nizam Mohmad, Kahar |
| author_sort | Mahmed, Ali Nader |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality. |
| first_indexed | 2025-11-15T03:14:56Z |
| format | Article |
| id | ump-34632 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:14:56Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-346322022-09-23T07:11:01Z http://umpir.ump.edu.my/id/eprint/34632/ Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators Mahmed, Ali Nader Mohd Nizam Mohmad, Kahar QA75 Electronic computers. Computer science QA76 Computer software TA Engineering (General). Civil engineering (General) The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality. MDPI 2022-05 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/34632/1/Window-based%20multi-objective%20optimization%20for%20dynamic%20patient%20scheduling.pdf Mahmed, Ali Nader and Mohd Nizam Mohmad, Kahar (2022) Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators. Computers, 11 (5). pp. 1-16. ISSN 2073-431X. (Published) https://doi.org/10.3390/computers11050063 https://doi.org/10.3390/computers11050063 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software TA Engineering (General). Civil engineering (General) Mahmed, Ali Nader Mohd Nizam Mohmad, Kahar Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title | Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title_full | Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title_fullStr | Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title_full_unstemmed | Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title_short | Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| title_sort | window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators |
| topic | QA75 Electronic computers. Computer science QA76 Computer software TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/34632/ http://umpir.ump.edu.my/id/eprint/34632/ http://umpir.ump.edu.my/id/eprint/34632/ http://umpir.ump.edu.my/id/eprint/34632/1/Window-based%20multi-objective%20optimization%20for%20dynamic%20patient%20scheduling.pdf |