期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：10
页码：325-334
出版社：SERSC
摘要：Accurate forecast of logistics demand can provide scientific guidance for logistics planning and decisionmaking.With the complexity and uncertaintycharacteristicsin logistics demand, the forecasting of logistics demand shows comprehensive and complex. The forecasting precision of the traditionalforecasting methods often are not satisfying. It is necessary to look for novel forecasting methods to enhance the forecasting precision of logistics demand. Integrating the unbiased GM (1,1) model (UGM (1,1)) into the adaptive inertia weight particle swarm optimization (AIWPSO) algorithm, this paper developed a novel model for forecasting logistics demand, called AIWPSO-UGM (1,1) model, in which the UGM (1,1) model was used to forecastlogistics demand and the AIWPSO algorithm was adopted to optimizethe grey parameters needed in UGM (1,1) model. Two examples were selected to prove the out-of-sample performance of the AIWPSO-UGM (1,1) model in forecasting logistics demand. The results imply that the proposed AIWPSO-UGM (1,1) model performs better in logistics demand forecasting compared to the GM (1,1) model optimized by AIWPSO algorithm (AIWPSO-GM (1,1)), UGM (1,1), and GM (1,1) models.
