PDF(Pubmed)
Abstract：
The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting \"if a shipment can be exported from one source to another\", despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs.
摘要：
持续的COVID-19大流行给全球供应链(SC)造成了前所未有的困境。基本和救生产品的装运，从制药，农业,和医疗保健，制造业，受到重大影响或延误，使全球SC脆弱。更好地了解装运风险可以大大减少这种紧张情绪。此后,本文提出了一些深度学习(DL)方法，通过预测“货物是否可以从一个来源出口到另一个来源”来减轻装运风险，尽管COVID-19大流行施加了限制。拟议的DL方法有四个主要阶段：数据捕获，去噪或预处理，特征提取,和分类。特征提取阶段取决于DL模型的两个主要变体。第一个变体涉及三个递归神经网络(RNN)结构(即，长短期记忆(LSTM)，双向长短期记忆(BiLSTM)，和门控经常性单位(GRU))，第二个变体是时间卷积网络(TCN)。就分类阶段而言，六个不同的分类器被用来测试整个方法。这些分类器是SoftMax，随机树(RT)，随机森林(RF)，k-最近邻(KNN)，人工神经网络(ANN)，和支持向量机(SVM)。基于在线数据集（作为案例研究）评估了所提出的DL模型的性能。数值结果表明，提出的模型之一(即，TCN)在预测COVID-19限制下运往特定目的地的风险方面约100%准确。毫无疑问，这项工作的后果将有助于决策者主动预测供应链风险,以提高SCs的弹性.
