An improved grey neural network model for predicting transportation disruptions. (1st March 2016)

• Record Type: Journal Article
• Authors: Liu, Chunxia; Shu, Tong; Chen, Shou; Wang, Shouyang; Lai, Kin Keung; Gan, Lu
• Is Part Of: Expert systems with applications. Volume 45(2016)
• Page Start: 331
• Page End: 340
• Publication Date: 2016-03-01
• Subjects: Transportation disruptions -- GM(1, 1) model -- Neural network -- Prediction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.09.052
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Market demand is highly unpredictable after transportation disruption. It designs an improved prediction model of grey neural networks. It determines the number of neurons in the input layer of BP neural networks. It tests the feasibility of the prediction model through case studies. It helps optimize inventory and production after transportation disruption. Abstract: Transportation disruption is the direct result of various accidents in supply chains, which have multiple negative impacts on supply chains and member enterprises. After transportation disruption, market demand becomes highly unpredictable and thus it is necessary for enterprises to better predict market demand and optimize purchase, inventory and production. As such, this article endeavors to design an improved model of grey neural networks to help enterprises better predict market demand after transportation disruption and then the empirical study tests its feasibility. This improved model of grey neural networks exceeds the conventional grey model GM(1, 1) with respect to the fact that the raw data tend to show exponential growth and data variation is required to be moderate, demonstrating the good attribute of nonlinear approximation in terms of neural networks, setting up standards for selecting the number of neurons in the input layer of BP neural networks, increasing the fitting degree and prediction accuracy and enhancing the stability and reliability of prediction. It can be applied to sequential data prediction in transportation disruption or mutation, contributing to the prediction of transportation disruption. The forecasting results can provide scientific evidence for demand prediction, inventory management and production of supply chain enterprises.