A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems
Neural and Evolutionary Computing
2017-04-19 v1 Distributed, Parallel, and Cluster Computing
Discrete Mathematics
Abstract
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.
Cite
@article{arxiv.1704.05132,
title = {A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems},
author = {Nikolaos Antoniadis and Angelo Sifaleras},
journal= {arXiv preprint arXiv:1704.05132},
year = {2017}
}
Comments
8 pages, 1 figure