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  • Numerous (meta-)heuristics for solving personnel scheduling problems have been introduced in various papers over the last few years. In most cases, These methods consist of two usual steps: 1) generate new solutions and 2) determine their quality (cost) given by an objective function which is very computationally expensive. Our paper proposes a faster evaluation of the objective function based on the solution structure (pattern). The inspiration was found in creating a roster by a human, who is able to recognize an obviously bad roster using their own experience instead of complex computing. For this purpose, a neural network is used as a tool of pattern recognition to distinguish between good and bad solutions. The given approach is applied to the standard benchmark instances for the nurse rostering problem. We demonstrate that the proposed classifier can reduce the runtime of the scheduling algorithm in comparison with standard cost-oriented evaluation of the objective function with equivalent solution quality.
  • Numerous (meta-)heuristics for solving personnel scheduling problems have been introduced in various papers over the last few years. In most cases, These methods consist of two usual steps: 1) generate new solutions and 2) determine their quality (cost) given by an objective function which is very computationally expensive. Our paper proposes a faster evaluation of the objective function based on the solution structure (pattern). The inspiration was found in creating a roster by a human, who is able to recognize an obviously bad roster using their own experience instead of complex computing. For this purpose, a neural network is used as a tool of pattern recognition to distinguish between good and bad solutions. The given approach is applied to the standard benchmark instances for the nurse rostering problem. We demonstrate that the proposed classifier can reduce the runtime of the scheduling algorithm in comparison with standard cost-oriented evaluation of the objective function with equivalent solution quality. (en)
Title
  • A Low Time-Consuming Rosters Evaluation in Personnel Scheduling Problems Based on Pattern Learning
  • A Low Time-Consuming Rosters Evaluation in Personnel Scheduling Problems Based on Pattern Learning (en)
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  • A Low Time-Consuming Rosters Evaluation in Personnel Scheduling Problems Based on Pattern Learning
  • A Low Time-Consuming Rosters Evaluation in Personnel Scheduling Problems Based on Pattern Learning (en)
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  • RIV/68407700:21230/13:00208836!RIV14-MSM-21230___
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  • P(7H12008), P(TE01020197), S
http://linked.open...vai/riv/dodaniDat
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  • 58697
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/13:00208836
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  • tabu search; personnel scheduling; nurse rostering problem; neural network; pattern learning (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [47E44CB316EB]
http://linked.open...v/mistoKonaniAkce
  • Hamburg
http://linked.open...i/riv/mistoVydani
  • Erkelenz
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 14th EU/ME workshop
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  • Hanzálek, Zdeněk
  • Šůcha, Přemysl
  • Václavík, Roman
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • EURO - The Association of European Operational Research Societies
https://schema.org/isbn
  • 978-3-86818-049-7
http://localhost/t...ganizacniJednotka
  • 21230
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