Parallel Computing

The authors address the computationally demanding uncapacitated multi-item economic lot-sizing problem with remanufacturing by developing parallel versions of the General Variable Neighborhood Search (GVNS) algorithm. Through an experimental comparison of serial, OpenMP, OpenACC, and hybrid CPU-GPU implementations, the study shows that parallelizing the algorithm significantly improves the quality of the solutions found within a given time limit. Specifically, the hybrid OpenMP-OpenACC scheme slightly outperformed the other methods, demonstrating the practical advantages of leveraging both CPU threads and GPU hardware for heuristic performance tuning.

The author explores the theoretical background of metaheuristics, specifically Variable Neighborhood Search (VNS), alongside parallel programming architectures before implementing a parallelized Variable Neighborhood Descent (VND) algorithm for a multi-product dynamic lot-sizing problem. Computational experiments reveal that both OpenMP and OpenACC significantly reduce execution time, achieving a speedup of approximately 3.3 times over the serial version. However, because the applied parallelization strategy focused strictly on accelerating computations rather than expanding the search space, the overall quality of the final solutions remained unchanged.