A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems
This work addresses inventory optimization in reverse logistics, but it is incremental as it focuses on parallelization of an existing method.
The paper tackled the NP-hard multi-product dynamic lot sizing problem with product returns by applying a hybrid CPU-GPU parallelization scheme to Variable Neighborhood Search, reporting promising computational results.
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.