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Production and Operations Management ; 32(2):524-546, 2023.
Article in English | Scopus | ID: covidwho-2246480


The recent outbreak of Coronavirus disease 2019 (COVID-19) has posed serious threats and challenges to global supply chain management (GSCM). To survive the crisis, it is critical to rethink the proper setting of global supply chains and reform many related operational strategies. We hence attempt to reform the GSCM from both supply and demand sides considering different pandemic stages (i.e., pre, during, and post-pandemic stages). In this research paper, we combine a careful literature review with real-world case studies to examine the impacts and specific challenges brought by the pandemic to global supply chains. We first classify the related literature from the demand and supply sides. Based on the insights obtained, we search publicly available information and report real practices of GSCM under COVID-19 in nine top global enterprises. To achieve responsiveness, resilience, and restoration (3Rs), we then propose the "GREAT-3Rs” framework, which shows the critical issues and measures for reforming GSCM under the three pandemic stages. In particular, the "GREAT” part of the framework includes five critical domains, namely, "government proactive policies and measures,” "redesigning global supply chains,” "economic and financing strategies under risk,” "adjustment of operations,” and "technology adoption,” to help global enterprises to survive the pandemic;"3Rs” are the outputs that can be achieved after using the "GREAT” strategies under the three pandemic stages. Finally, we establish a future research agenda from five aspects. © 2022 Production and Operations Management Society.

Ieee Access ; 10:134785-134798, 2022.
Article in English | Web of Science | ID: covidwho-2191673


Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.

J Air Transp Manag ; 95: 102086, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1253123


The outbreak of the COVID-19 pandemic has drastically disrupted the air cargo industry. This disruption has taken many directions, one of which is the demand imbalance which occurs due to the sudden change in the cargo capacity, as well as demand. Therefore, the random change leads to excessive demand in some routes (hot-selling routes), while some other routes suffer from a big shortage of demand (underutilized routes). Routes are substitutable when there are several adjacent airports in the Origin & Destination (O&D) market. In this market, demand imbalance between substitutable routes occurs because of the above reasons. To tackle the demand imbalance problem, a novel model is introduced to estimate the quantity combinations which maintains the balance between underutilized and hot-selling routes. This model is a variant of the classic Cournot model which captures different quantity scenarios in the form of the best response for each route compared to the other. We then cultivate the model by integrating the Puppet Cournot game with the quantity discount policy. The quantity discount policy is an incentive which motivates the freight forwarders to increase their orders in the underutilized routes. After conducting numerical experiments, the results reveal that the profit can increase up to 25% by using the quantity discount. However, the quantity discount model is only applicable when the profit increase in the hot-selling route is greater than the profit decrease in the underutilized route.