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  • This paper deals with the Nurse Rerostering Problem (NRRP) performed by a parallel algorithm on a Graphics Processing Unit (GPU). This problem is focused on rescheduling of human resources in healthcare, when a roster is disrupted by unexpected circumstances. Our aim is to resolve NRRP in a parallel way to shorten the needed computational time in comparison to already known algorithms. The design of the parallel algorithm is a non-trivial task and brings many crucial issues that are described in this paper, e.g. a thread mapping issue, the utilization of the memory and the minimization of the communication overhead between the PC and the GPU. These issues must be taken into account in order to achieve the expected speedup. Our algorithm is evaluated on the benchmark datasets and compared to the optimal results given by ILP. The part of the heterogeneous parallel algorithm running on the GPU was up to 6 times faster in comparison to its sequential version. In total, our parallel algorithm provides a speedup of 1.9 (2.5) times for the NRRP instances with 19 (32) nurses in comparison to the sequential algorithm.
  • This paper deals with the Nurse Rerostering Problem (NRRP) performed by a parallel algorithm on a Graphics Processing Unit (GPU). This problem is focused on rescheduling of human resources in healthcare, when a roster is disrupted by unexpected circumstances. Our aim is to resolve NRRP in a parallel way to shorten the needed computational time in comparison to already known algorithms. The design of the parallel algorithm is a non-trivial task and brings many crucial issues that are described in this paper, e.g. a thread mapping issue, the utilization of the memory and the minimization of the communication overhead between the PC and the GPU. These issues must be taken into account in order to achieve the expected speedup. Our algorithm is evaluated on the benchmark datasets and compared to the optimal results given by ILP. The part of the heterogeneous parallel algorithm running on the GPU was up to 6 times faster in comparison to its sequential version. In total, our parallel algorithm provides a speedup of 1.9 (2.5) times for the NRRP instances with 19 (32) nurses in comparison to the sequential algorithm. (en)
Title
  • An Acceleration of the Algorithm for the Nurse Rerostering Problem on a Graphics Processing Unit
  • An Acceleration of the Algorithm for the Nurse Rerostering Problem on a Graphics Processing Unit (en)
skos:prefLabel
  • An Acceleration of the Algorithm for the Nurse Rerostering Problem on a Graphics Processing Unit
  • An Acceleration of the Algorithm for the Nurse Rerostering Problem on a Graphics Processing Unit (en)
skos:notation
  • RIV/68407700:21230/13:00208402!RIV14-MSM-21230___
http://linked.open...avai/riv/aktivita
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  • P(7H12008), P(GAP103/12/1994)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 60262
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  • RIV/68407700:21230/13:00208402
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  • nurse rerostering problem; parallel algorithm; GPU; heuristic; personnel/human resources scheduling (en)
http://linked.open.../riv/klicoveSlovo
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  • [C2F06E0CFFB4]
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  • Lisabon
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  • PMB 253-5525 West Boulevard, Vancouver, BC V6M 3
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  • Lecture Notes in Management Science - 5th International Conference on Applied Operational Research, Proceedings
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  • Bäumelt, Zdeněk
  • Dvořák, Jan
  • Hanzálek, Zdeněk
  • Šůcha, Přemysl
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 2008-0050
number of pages
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  • Tadbir Operational Research Group Ltd.
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  • 21230
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