Study of Food Cold Chain Logistics Demand Forecast Based on Multiple Regression and AW-BP Forecasting Method on System Order Parameters

2016 ◽  
Vol 13 (7) ◽  
pp. 4019-4024 ◽  
Author(s):  
Bi Ya
Keyword(s):  
Multiple Regression ◽  
Order Parameters ◽  
Cold Chain ◽  
Demand Forecast ◽  
Forecasting Method ◽  
Logistics Demand ◽  
System Order

scholarly journals Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

2021 ◽  
Vol 9 (1) ◽  
pp. 73-89
Author(s):  
Guoyou Yue
Keyword(s):  
Moving Average ◽  
Beibu Gulf ◽  
Demand Forecast ◽  
Forecasting Models ◽  
Forecasting Method ◽  
Port Logistics ◽  
Logistics Demand ◽  
Cargo Throughput ◽  
Port Areas ◽  
Weighted Moving Average

Objective - The objective of this paper is to establish the forecasting models of port cargo throughput and container throughput in Guangxi Beibu Gulf Port in the next 5 years, and to put forward the countermeasures of port logistics development in Guangxi Beibu Gulf Port according to the forecast results. Methodology/Technique – The data of cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2009-2020 are collected through the data of Guangxi Statistical Yearbook and Guangxi Statistical Bulletin. Based on 2019 and 2020, the forecasting models of cargo throughput and container throughput in Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou are establishe using a weighted moving average forecasting method. The cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2020/2021-2025 are predicted. Findings – The forecast results show that by 2025, the cargo throughput of Guangxi Beibu Gulf Port is expected to exceed 400 million tons, and the container throughput is expected to exceed 10 million TEU. According to the fitting diagram of forecast results and actual data, it can be seen that the accuracy of the forecast results is very high. Novelty – It is innovative to select 2 base years in 2019 and 2020 to establish forecasting model. Based on the comparative analysis of the forecast results, this paper puts forward various measures to promote the development of port logistics of Guangxi Beibu Gulf port, such as strengthening the construction of port self-condition, strengthening the co-ordinated development of port and economic hinterland, speeding up the construction of port collection and distribution system, training and introducing all kinds of high-quality port logistics talents. Type of Paper: Empirical. JEL Classification: C53, R41. Keywords: Logistics Demand Forecast; Cargo Throughput Forecast; Container Throughput Forecast; Weighted Moving Average Forecasting Method; Guangxi Beibu Gulf Port Reference to this paper should be made as follows: Yue, N. (2021). Forecasting the Logistics Demand of Guangxi Beibu Gulf Port, GATR Global J. Bus. Soc. Sci. Review, 9(1): 73 – 89. https://doi.org/10.35609/gjbssr.2021.9.1(9)

scholarly journals Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

2022 ◽  
Vol 16 ◽  
pp. 257-263
Author(s):  
Ying Fu ◽  
Xiangpeng Zeng ◽  
Yihua Li ◽  
Yiming Wen ◽  
Xiaowei Wen
Keyword(s):  
Cognitive Neuroscience ◽  
Government Policy ◽  
Bp Neural Network ◽  
Hunan Province ◽  
Cold Chain ◽  
Demand Forecast ◽  
Logistics System ◽  
New Perspective ◽  
Logistics Demand ◽  
Bp Neural Network Model

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

scholarly journals Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

2022 ◽  
Vol 16 ◽  
pp. 571-577
Author(s):  
Ying Fu ◽  
Xiangpeng Zeng ◽  
Yihua Li ◽  
Yiming Wen ◽  
Xiaowei Wen
Keyword(s):  
Cognitive Neuroscience ◽  
Government Policy ◽  
Bp Neural Network ◽  
Hunan Province ◽  
Cold Chain ◽  
Demand Forecast ◽  
Logistics System ◽  
New Perspective ◽  
Logistics Demand ◽  
Bp Neural Network Model

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Demand Prediction of Cold Chain Logistics Under B2C E-Commerce Model

2018 ◽  
Vol 22 (7) ◽  
pp. 1082-1087 ◽  
Author(s):  
Shen-Xiang Wang ◽  
◽  
Cheng-Yan Wei
Keyword(s):  
Linear Regression ◽  
Goodness Of Fit ◽  
Prediction Equation ◽  
Multivariate Linear Regression ◽  
Cold Chain ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Correlation Degree ◽  
Grey Correlation Analysis ◽  
Logistics Demand

In order to meet the increasing demand, the demand of cold chain logistics under the background of B2C e-commerce mode is predicted, to provide theoretical guidance for the development of cold chain logistics. A multivariate linear regression demand prediction model based on grey relational analysis is proposed. The present situation of cold chain logistics demand is as the basis for the analysis. Using appropriate quantitative analysis method, the factors affecting the demand of cold chain logistics are screened, and the selection principles of logistics demand evaluation index for cold chain products are determined, including product supply, logistics demand scale, and cold chain efficiency and so on. The grey correlation analysis is used to standardize the data sequence and calculate the correlation degree between the factors. The factor of large correlation degree is chosen as the key factor, and the multivariate linear regression prediction equation is constructed. According to the progressive regression idea, the model is amended to improve the goodness of fit of the model. The grey multivariate regression model is applied to predict and analyze the cold chain logistics demand of a fruit product in a certain city. The result shows that the model can predict the demand of cold chain logistics accurately.

scholarly journals ANNUAL AVERAGE DAILY TRAFFIC FORECASTING USING DIFFERENT TECHNIQUES

Transport ◽  
2006 ◽  
Vol 21 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Tomas Šliupas
Keyword(s):  
Growth Factor ◽  
Linear Regression ◽  
Multiple Regression ◽  
Traffic Data ◽  
Annual Average ◽  
Traffic Forecasting ◽  
Annual Average Daily Traffic ◽  
Forecasting Method ◽  
The Future

This paper describes annual average daily traffic (AADT) forecasting for the Lithuanian highways using a forecasting method used by Idaho Department for Transportation, growth factor, linear regression and multiple regression. AADT forecasts obtained using these methods are compared with the forecasts evaluated by traffic experts and given in references. The results show that the best Lithuanian traffic data are obtained using Idaho forecasting method. It is assumed that the curve of AADT change should be exponential in the future.

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