2019 Vol.2 Dec. No.6

• Title: Multi-region logistics distribution demand forecasting method based on big data analysis
• Name: Donker Cormack
• Company: International Pacific College
• Abstract:

  The conventional logistics distribution demand forecasting method has the disadvantage of low analysis accuracy when forecasting multi-region logistics distribution demand. Therefore, a multi-region logistics distribution demand forecasting method based on big data analysis is proposed. Introduce big data technology, build a multi-regional logistics distribution demand forecasting framework, and build a multi-regional logistics distribution model; rely on the determination of different needs for logistics distribution between regions, and embed the demand forecasting model to realize multi-regional logistics distribution demand forecasting and analysis. The experimental data show that the proposed big data prediction method is 57.23% more accurate than the conventional method, which is suitable for the prediction of logistics distribution demand in different regions and multiple regions.

• Keyword: Big Data Analysis; Multi-Regional Distribution; Logistics Network; Demand Forecasting;0 Introduction
• DOI: 10.12250/jpciams2019060630
• Citation form: Donker Cormack.Multi-region logistics distribution demand forecasting method based on big data analysis[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 151-156.

[1] Cam M L， Zmeureanu R， Daoud A. Cascade-based short-term forecasting method of the electric demand of HVAC system[J]. Energy， 2017， 119:1098-1107.

[2] Chen M， Yuan J， Liu D， et al. An adaption scheduling based on dynamic weighted random forests for load demand forecasting[J]. Journal of Supercomputing， 2017(1):1-19.

[3] Ren S， Chan H L， Ram P. A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty[J]. Annals of Operations Research， 2017， 257(1-2):1-21.

[4] Gao D， Wang N， He Z， et al. The Bullwhip Effect in an Online Retail Supply Chain: A Perspective of Price-Sensitive Demand Based on the Price Discount in E-Commerce[J]. IEEE Transactions on Engineering Management， 2017， PP(99):1-15.

[5] Alsaleh M， Abdul-Rahim A S， Mohd-Shahwahid H O. An empirical and forecasting analysis of the bioenergy market in the EU28 region: Evidence from a panel data simultaneous equation model[J]. Renewable & Sustainable Energy Reviews， 2017， 80(12):1123-1137.

[6] Shaikh F， Ji Q， Shaikh P H， et al. Forecasting China’s natural gas demand based on optimised nonlinear grey models[J]. Energy， 2017， 156:182-196.

[7] Cao H， Li T， Li S， et al. An integrated emergency response model for toxic gas release accidents based on cellular automata[J]. Annals of Operations Research， 2017， 255(1-2):617-638.

[8] Panapakidis I P， Dagoumas A S. Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model[J]. Energy， 2017， 118:231-245.

[9] Zhang P， Xiong Y， Xiong Z， et al. Information sharing and service channel design in the presence of forecasting demand[J]. Journal of the Operational Research Society， 2018(2):1-15.

[10] Paleti R， Vovsha P， Vyas G， et al. Activity sequencing， location， and formation of individual non-mandatory tours: application to the activity-based models for Columbus， Cincinnati， and Cleveland， OH[J]. Transportation， 2017， 44(3):615-640.