An evolutionary algorithm based on constraint set partitioning for nurse rostering problems
Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function...
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Gespeichert in:
| Autor*in: |
Huang, Han [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 25(2014), 3-4 vom: 03. Jan., Seite 703-715 |
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| Übergeordnetes Werk: |
volume:25 ; year:2014 ; number:3-4 ; day:03 ; month:01 ; pages:703-715 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-013-1536-2 |
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OLC2025594429 |
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| 520 | |a Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. | ||
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| 700 | 1 | |a Hao, Zhifeng |4 aut | |
| 700 | 1 | |a Lim, Andrew |4 aut | |
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10.1007/s00521-013-1536-2 doi (DE-627)OLC2025594429 (DE-He213)s00521-013-1536-2-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Han verfasserin aut An evolutionary algorithm based on constraint set partitioning for nurse rostering problems 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. Evolutionary algorithm Nurse rostering problem Constraint set partitioning Integer programming Lin, Weijia aut Lin, Zhiyong aut Hao, Zhifeng aut Lim, Andrew aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 03. Jan., Seite 703-715 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:03 month:01 pages:703-715 https://doi.org/10.1007/s00521-013-1536-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 03 01 703-715 |
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10.1007/s00521-013-1536-2 doi (DE-627)OLC2025594429 (DE-He213)s00521-013-1536-2-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Han verfasserin aut An evolutionary algorithm based on constraint set partitioning for nurse rostering problems 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. Evolutionary algorithm Nurse rostering problem Constraint set partitioning Integer programming Lin, Weijia aut Lin, Zhiyong aut Hao, Zhifeng aut Lim, Andrew aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 03. Jan., Seite 703-715 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:03 month:01 pages:703-715 https://doi.org/10.1007/s00521-013-1536-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 03 01 703-715 |
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10.1007/s00521-013-1536-2 doi (DE-627)OLC2025594429 (DE-He213)s00521-013-1536-2-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Han verfasserin aut An evolutionary algorithm based on constraint set partitioning for nurse rostering problems 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. Evolutionary algorithm Nurse rostering problem Constraint set partitioning Integer programming Lin, Weijia aut Lin, Zhiyong aut Hao, Zhifeng aut Lim, Andrew aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 03. Jan., Seite 703-715 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:03 month:01 pages:703-715 https://doi.org/10.1007/s00521-013-1536-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 03 01 703-715 |
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10.1007/s00521-013-1536-2 doi (DE-627)OLC2025594429 (DE-He213)s00521-013-1536-2-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Han verfasserin aut An evolutionary algorithm based on constraint set partitioning for nurse rostering problems 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. Evolutionary algorithm Nurse rostering problem Constraint set partitioning Integer programming Lin, Weijia aut Lin, Zhiyong aut Hao, Zhifeng aut Lim, Andrew aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 03. Jan., Seite 703-715 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:03 month:01 pages:703-715 https://doi.org/10.1007/s00521-013-1536-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 03 01 703-715 |
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10.1007/s00521-013-1536-2 doi (DE-627)OLC2025594429 (DE-He213)s00521-013-1536-2-p DE-627 ger DE-627 rakwb eng 004 VZ Huang, Han verfasserin aut An evolutionary algorithm based on constraint set partitioning for nurse rostering problems 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. Evolutionary algorithm Nurse rostering problem Constraint set partitioning Integer programming Lin, Weijia aut Lin, Zhiyong aut Hao, Zhifeng aut Lim, Andrew aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 03. Jan., Seite 703-715 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:03 month:01 pages:703-715 https://doi.org/10.1007/s00521-013-1536-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 03 01 703-715 |
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an evolutionary algorithm based on constraint set partitioning for nurse rostering problems |
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An evolutionary algorithm based on constraint set partitioning for nurse rostering problems |
| abstract |
Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. © Springer-Verlag London 2014 |
| abstractGer |
Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. © Springer-Verlag London 2014 |
| abstract_unstemmed |
Abstract The nurse rostering problem (NRP) is a representative of NP-hard combinatorial optimization problems. The hardness of NRP is mainly due to its multiple complex constraints. Several approaches, which are based on an evolutionary algorithm (EA) framework and integrated with a penalty-function technique, were proposed in the literature to handle the constraints found in NRP. However, these approaches are not very efficient in dealing with large-scale NPR instances and thus need to be improved upon. In this paper, we investigate a large-scale NRP in a real-world setting, i.e., Chinese NRP (CNRP), which requires us to arrange many nurses (up to 30) across a 1-month scheduling period. The CNRP poses various constraints that lead to a large solution space with multiple isolated areas of infeasible solutions. We propose a single-individual EA for the CNRP. The novelty of the proposed approach is threefold: (1) using a constraint separation to partition the constraints into hard and soft constraints; (2) using a revised integer programming to generate a high-quality initial individual (solution), which then leads the subsequent EA search to a promising feasible solution space; and (3) using an efficient mutation operator to quickly search for a better solution in the restricted feasible solution space. The experimental results based on extensive simulations indicate that our proposed approach significantly outperforms several existing representative algorithms, in terms of solution quality within the same calculation times of the objective function. © Springer-Verlag London 2014 |
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