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A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

Nikolaos Antoniadis, Angelo Sifaleras
Department of Applied Informatics, School of Information Sciences, University of Macedonia, 156 Egnatia Str., Thessaloniki 54636, Greece
arXiv:1704.05132 [cs.NE], (17 Apr 2017)

@article{antoniadis2017hybrid,

   title={A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems},

   author={Antoniadis, Nikolaos and Sifaleras, Angelo},

   year={2017},

   month={apr},

   archivePrefix={"arXiv"},

   primaryClass={cs.NE},

   doi={10.1016/j.endm.2017.03.007}

}

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In this paper, we study various parallelization schemes for the Variable Neighborhood Search (VNS) metaheuristic on a CPU-GPU system via OpenMP and OpenACC. A hybrid parallel VNS method is applied to recent benchmark problem instances for the multi-product dynamic lot sizing problem with product returns and recovery, which appears in reverse logistics and is known to be NP-hard. We report our findings regarding these parallelization approaches and present promising computational results.
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