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  • This paper presents a genetic algorithm, implemented on a GPU (Graphics Processing Unit), solving permutation flow shop scheduling problem. This approach is based on the homogeneous computing model in order to maximize the algorithm efficiency. The genetic algorithm is based on work presented by Pospichal et al. In our work, we suggest several improvements (e.g. another mechanism for individuals migration) allowing to apply this algorithm on a wider group of NP-hard combinatorial problems. Our experimental results show significant speedup with respect to the sequential algorithm version.
  • This paper presents a genetic algorithm, implemented on a GPU (Graphics Processing Unit), solving permutation flow shop scheduling problem. This approach is based on the homogeneous computing model in order to maximize the algorithm efficiency. The genetic algorithm is based on work presented by Pospichal et al. In our work, we suggest several improvements (e.g. another mechanism for individuals migration) allowing to apply this algorithm on a wider group of NP-hard combinatorial problems. Our experimental results show significant speedup with respect to the sequential algorithm version. (en)
Title
  • Flow Shop Scheduling on GPU
  • Flow Shop Scheduling on GPU (en)
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  • Flow Shop Scheduling on GPU
  • Flow Shop Scheduling on GPU (en)
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  • RIV/68407700:21230/11:00181877!RIV12-MSM-21230___
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  • P(GPP103/10/P401), Z(MSM6840770038)
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  • 199988
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  • RIV/68407700:21230/11:00181877
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  • GPU; Flow shop; CUDA; Scheduling; Combinatorial optimization (en)
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  • [6586E38512FE]
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  • Zajíček, Tomáš
  • Šůcha, Přemysl
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  • 21230
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