Earticle

초록

영어

Accurate forecast of logistics demand can provide scientific guidance for logistics planning and decision making. With the complexity and uncertainty characteristics in logistics demand, the forecasting of logistics demand shows comprehensive and complex. The forecasting precision of the traditional forecasting 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 forecast logistics demand and the AIWPSO algorithm was adopted to optimize the 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.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Unbiased GM (1, 1) Model
  2.2. AIWPSO Algorithm
  2.3. The Optimal Unbiased GM (1, 1) by PSO
 3. Examples Analysis
  3.1. Data Description
  3.2. Examples Process and Results Analysis
 4. Conclusions
 References

키워드

저자

• Li-Yan Geng [ School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang ]
• Zhan-Fu Zhang [ Department of Electrical and Engineering, Sifang College, Shijiazhuang Tiedao University, Shijiazhuang ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605