A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems
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}
}
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.
April 20, 2017 by hgpu
