Abstract
Transportation disruption causes economic loss in supply chains in the gas industry while population growth increases, which calls for a comprehensive review of the operations by management. Hence, the gas company should study and evaluate the situation and make the right decision by taking corrective action to minimize the negative impact of disruption. This paper aims to develop a deep learning-based model to improve the efficiency of the constrained transportation network of an industrial gas company in conjunction with historical data. The framework was demonstrated using an example case of the gas industry in Jeddah, Saudi Arabia. The findings revealed the usefulness of the Wilde Neural Network model in classifying the trip cost with an accuracy of 100% and a short duration of training of 2.84 seconds.
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