Neural Network Model for the Risk Prediction in Cold Chain Logistics

Xu, Weiyang; Zhang, Zhenji; Gong, Daqing; Guan, Xiaolan

doi:10.14257/ijmue.2014.9.8.10

Cited by 24 publications

(15 citation statements)

References 20 publications

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“…The "mean squared error" loss function with "ADAM" optimization algorithm are used in compiling the model. RMSE [34], [26], [22], [8] is used to evaluate the performance of the model to forecast. It calculates the variance between actual value and the predicted value and used to evaluate different models for a certain data and not amid datasets.…”

Section: Training and Forecastingmentioning

confidence: 99%

An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

Rebortera¹,

Fajardo²

2019

IJACSA

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This technical quest aspired to build deep multifaceted system proficient in forecasting banana harvest yields essential for extensive planning for a sustainable production in the agriculture sector. Recently, deep-learning (DL) approach has been used as a new alternative model in forecasting. In this paper, the enhanced DL approach incorporates multiple long short term memory (LSTM) layers employed with multiple neurons in each layer, fully trained and built a state for forecasting. The enhanced model used the banana harvest yield data from agrarian reform beneficiary (ARB) cooperative of Dapco in Davao del Norte, Philippines. The model parameters such as epoch, batch size and neurons underwent tuning to identify its optimal values to be used in the experiments. Additionally, the root-mean-squared error (RMSE) is used to evaluate the performance of the model. Using the same set of training and testing data, experiment exhibits that the enhanced model achieved the optimal result of 34.805 in terms of RMSE. This means that the enhanced model outperforms the single and multiple LSTM layer with 43.5 percent and 44.95 percent reduction in error rates, respectively. Since there is no proof that LSTM recurrent neutral network has been used with the same agricultural problem domain, therefore, there is no standard available with regards to the level of error reduction in the forecast. Moreover, investigating the performance of the model using diverse datasets specifically with multiple input features (multivariate) is suggested for exploration. Furthermore, extending and embedding this approach to a web-based along with a handy application is the future plan for the benefit of the medium scale banana growers of the region for efficient and effective decision making and advance planning.

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Section: Training and Forecastingmentioning

confidence: 99%

An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

Rebortera¹,

Fajardo²

2019

IJACSA

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“…Aiming at the uncertainty and complexity of urban logistics system, Mingjing Zhao [23] et al adopted the method of combining interpretive structure model and Bayesian network model to analyze logistics risk factors and find out the sensitive nodes of logistics system. Weiyang Xu [24] et al…”

Section: Introductionmentioning

confidence: 99%

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Zhao

Wei

2020

PLoS ONE

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For the complicated operation process, many risk factors, and long cycle of urban logistics, it is difficult to manage the security of urban logistics and it enhances the risk. Therefore, to study a set of effective management mode for the safe operation of urban logistics and improve the risk prediction mechanism, is the primary research item of urban logistics security management. This paper summarizes the risk factors to public security in the process of urban logistics, including pick up, warehouse storage, transport, and the end distribution. Generalized regression neural network (GRNN) is combined with particle swarm optimization (PSO) to predict accidents, and the Apriori algorithm is used to analyze the combination of high-frequency risk factors. The results show that the method of combining GRNN with PSO is effective in accident prediction and has a powerful generalization ability. It can prevent the occurrence of unnecessary urban logistics public accidents, improve the ability of relevant departments to deal with emergency incidents, and minimize the impact of urban logistics accidents on social and public security.

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“…A cold chain is a one of supply chain to control temperatures. An unbroken cold chain includes a series of activities such as processing, storage, transportation, logistics, retail, until consumption, which maintain a given environment temperature [1,2]. Cold chain is complex, time-critical, and dynamic.…”

Section: Introductionmentioning

confidence: 99%

“…However, the specific temperature tolerances may depend on the products being shipped [1]. Cold chain is very important for the supply chain management of the perishable food and pharmaceutical industries since it able to reduce the risk and cost [1,2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of Smart Cold Chain Application Framework Based on Hadoop and Spark

Ko¹,

Kwak²,

Choi³

et al. 2015

IJSEIA

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A smart cold chain management is an information system which monitors and maintains the proper temperature of the products during whole cold chain, and analyzes data to report abnormal environmental status and risks to producers and consumers. In this paper, we propose an application framework for smart cold chain management system based on Hadoop, Spark and IoT (Internet of Things) techniques. The proposed application framework for smart cold chain provides PaaS (Platform as a Service) and IaaS (Infra as a Service) so as that smart cold chain management systems can be developed and operated with low cost and in short time. Also, the proposed application framework for smart cold chain allows the heterogeneous IOT devices such as RFID tags, WSN sensor nodes, BLE (Bluetooth Low Energy) sensor nodes and so on. We design PaaS and IaaS based on Hadoop and Spark to store the large amount of data stream on salable storage and process stream data in real time to detect events and assess risks in cold chain. Through generalizing the functions of existing cold chain management systems, common components in smart cold chain management systems are drawn.

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Neural Network Model for the Risk Prediction in Cold Chain Logistics

Cited by 24 publications

References 20 publications

An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Design of Smart Cold Chain Application Framework Based on Hadoop and Spark

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