Ghaithan Ahmed M, Alarfaj Ibrahim, Mohammed Awsan, Qasim Osaid
Construction Engineering & Management Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261 Saudi Arabia.
Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, P.O. Box 5067, Dhahran, 31261 Saudi Arabia.
Neural Comput Appl. 2022;34(13):11213-11231. doi: 10.1007/s00521-022-07037-3. Epub 2022 Mar 14.
Since COVID-19 was declared as a pandemic by World Health Organization in March 2020, 169,682,828 cases have been reported worldwide, with 151,416,570 recovered, and 3,526,647 deaths by May 28, 2021. Oxygen gas cylinders demand is booming globally due to its need for COVID-19's for intensive care. Thus, it is critical for hospitals to know exactly the time of receiving oxygen gas cylinders since this will help in minimizing the fatality rate. In this regards, this paper proposes a Multilayer Perceptron Neural Network-based model to predict the delivery time of oxygen gas cylinders for a real-life logistics data from a company that delivers oxygen gas cylinders to all cities around Saudi Arabia. Besides, Multilayer Perceptron Neural Network is benchmarked to supported vector machine and multiple linear regression. Although all the considered models have the ability to provide accurate prediction results, the findings indicate that the proposed supported vector machine and Multilayer Perceptron Neural Network model provide better prediction results. The analysis was achieved through a methodology to identify factors with the highest impact and build a neural network model. The model was further optimized to identify the best order and select the best subset of input variables. The analysis showed that the neural network model can be used effectively to estimate the delivery time of oxygen gas cylinders. The model illustrated high accuracy of prediction by comparing the predicted values to the actual values.
自2020年3月世界卫生组织宣布新冠疫情为大流行以来，截至2021年5月28日，全球报告了169,682,828例病例，其中151,416,570例已康复，3,526,647例死亡。由于新冠肺炎重症监护需要氧气，全球氧气瓶需求激增。因此，医院准确掌握接收氧气瓶的时间至关重要，因为这有助于降低死亡率。在这方面，本文提出了一种基于多层感知器神经网络的模型，用于预测一家向沙特阿拉伯各地城市运送氧气瓶的公司实际物流数据中氧气瓶的交付时间。此外，将多层感知器神经网络与支持向量机和多元线性回归进行了基准测试。尽管所有考虑的模型都有能力提供准确的预测结果，但研究结果表明，所提出的支持向量机和多层感知器神经网络模型提供了更好的预测结果。通过一种方法进行分析，以识别影响最大的因素并建立神经网络模型。该模型进一步优化，以确定最佳顺序并选择输入变量的最佳子集。分析表明，神经网络模型可有效用于估计氧气瓶的交付时间。通过将预测值与实际值进行比较，该模型显示出较高的预测准确性。
