Combined forecasting of regional logistics demand optimized by a Genetic Algorithm

Author

He Feng-biao ; Chang Jun

Author_Institution

Dept. of Econ. & Manage., Huaiyin Normal Univ., Huai´an, China

fYear

2013

fDate

15-17 Nov. 2013

Firstpage

454

Lastpage

458

Abstract

Accurate prediction of regional logistics demand was the premise for scientific decision-making. On the basis of analyzing Trend Extrapolation, Grey System Method and Regression Method, two weight value determination methods that based on Method of Arithmetic Means and Validity Method were compared. Then, the idea of using a Genetic Algorithm to optimize weight values was put forward. The predicting outcomes of turnover volume of freight transport in region A turned out that the Genetic Algorithm can minimize the Sum of Squared Errors.

Keywords

decision making; extrapolation; forecasting theory; genetic algorithms; grey systems; logistics; regression analysis; transportation; arithmetic means; freight transport; genetic algorithm; grey system method; regional logistics demand; regression method; scientific decision making; sum of squared error minimization; trend extrapolation; turnover volume; validity method; weight value determination method; Extrapolation; Forecasting; Genetic algorithms; Logistics; MATLAB; Market research; Prediction methods; Genetic Algorithm; Grey System Method; Regression Method; combined forecasting; regional logistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Grey Systems and Intelligent Services, 2013 IEEE International Conference on

Conference_Location

Macao

ISSN

2166-9430

Print_ISBN

978-1-4673-5247-5

Type

conf

DOI

10.1109/GSIS.2013.6714826

Filename

6714826