Variable neighborhood search for a planning problem with resource constraints in a health simulation center
Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data a...
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| Autor*in: |
Caillard, Simon [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 52(2021), 6 vom: 04. Sept., Seite 6245-6261 |
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volume:52 ; year:2021 ; number:6 ; day:04 ; month:09 ; pages:6245-6261 |
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10.1007/s10489-021-02730-7 |
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| 520 | |a Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. | ||
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10.1007/s10489-021-02730-7 doi (DE-627)OLC2078469629 (DE-He213)s10489-021-02730-7-p DE-627 ger DE-627 rakwb eng 004 VZ Caillard, Simon verfasserin (orcid)0000-0002-9175-171X aut Variable neighborhood search for a planning problem with resource constraints in a health simulation center 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. Healthcare training Timetabling Optimization Scheduling Devendeville, Laure Brisoux aut Lucet, Corinne aut Enthalten in Applied intelligence Springer US, 1991 52(2021), 6 vom: 04. Sept., Seite 6245-6261 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2021 number:6 day:04 month:09 pages:6245-6261 https://doi.org/10.1007/s10489-021-02730-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2021 6 04 09 6245-6261 |
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10.1007/s10489-021-02730-7 doi (DE-627)OLC2078469629 (DE-He213)s10489-021-02730-7-p DE-627 ger DE-627 rakwb eng 004 VZ Caillard, Simon verfasserin (orcid)0000-0002-9175-171X aut Variable neighborhood search for a planning problem with resource constraints in a health simulation center 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. Healthcare training Timetabling Optimization Scheduling Devendeville, Laure Brisoux aut Lucet, Corinne aut Enthalten in Applied intelligence Springer US, 1991 52(2021), 6 vom: 04. Sept., Seite 6245-6261 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2021 number:6 day:04 month:09 pages:6245-6261 https://doi.org/10.1007/s10489-021-02730-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2021 6 04 09 6245-6261 |
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Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center offers numerous training sessions based on simulation learning for health actors, be they professionals or students. The data and constraints of the SimUSanté problem, close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are presented in detail using a 0-1 linear program modelization. A dedicated greedy algorithm SimUG is used to generate a relevant initial solution in the VNS algorithm. SimULS combines different neighborhood functions stemmed from operators saturator, intra, extra and extra +. A diversification function is applied when the search becomes trapped by a local optimum. First, SimULS was compared to the open source KHE solver by relaxing the precedence constraints. Next, SimULS was tested on all the generated SimUSanté instances. Both experiments show that the strength of SimULS is to schedule all the activities, even for the largest instances, without violating any hard constraints. In addition, the solutions given by SimULS are close to the optimum with a gap less than 7.33%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Variable neighborhood search for a planning problem with resource constraints in a health simulation center |
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Devendeville, Laure Brisoux Lucet, Corinne |
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