ABSTRACT
• A resilience assessment framework for the GLSN across two dimensions, including static resilience and dynamic resilience. • Identification of overlapping community structure and key nodes with the clique percolation method. • A network disintegration method to consider the impact of traffic flow on system resilience assessment. • An innovative, knock-on effect simulation model with tailored, local weighted flow redistribution rules for dynamic resilience assessment. As a critical infrastructure system of modern society, the global liner shipping network (GLSN) has become increasingly complex and thus vulnerable to disruptions. This study proposes a resilience assessment framework for the GLSN across two dimensions, including static resilience and dynamic resilience. First, by leveraging high-frequency vessel movement data, the GLSN is constructed. Then, with the clique percolation method (CPM), overlapping community structures and key nodes can be identified. The static resilience assessment is initially conducted using simulation techniques, with nodes attacked through pre-designed scenarios. Then, a network disintegration method is employed to consider the impact of traffic flow on system resilience assessment, which separates the weighted GLSN into different layers for evaluation. The results show that both overlapping community structure and traffic flow significantly impact the resilience evaluation of the GLSN. Finally, to assess the dynamic resilience of the GLSN, we propose an innovative, knock-on effect simulation model with tailored, locally weighted flow redistribution rules. It provides a method for predicting the impacts of potential global disruptions (e.g., the COVID-19 pandemic) and critical maritime infrastructure failures (e.g., the Suez Canal obstruction) on the shipping network, which are of great concern not only to academia but also to industry. [ FROM AUTHOR]
ABSTRACT
As a critical infrastructure system of modern society, the global liner shipping network (GLSN) has become increasingly complex and thus vulnerable to disruptions. This study proposes a resilience assessment framework for the GLSN across two dimensions, including static resilience and dynamic resilience. First, by leveraging high-frequency vessel movement data, the GLSN is constructed. Then, with the clique percolation method (CPM), overlapping community structures and key nodes can be identified. The static resilience assessment is initially conducted using simulation techniques, with nodes attacked through pre-designed scenarios. Then, a network disintegration method is employed to consider the impact of traffic flow on system resilience assessment, which separates the weighted GLSN into different layers for evaluation. The results show that both overlapping community structure and traffic flow significantly impact the resilience evaluation of the GLSN. Finally, to assess the dynamic resilience of the GLSN, we propose an innovative, knock-on effect simulation model with tailored, locally weighted flow redistribution rules. It provides a method for predicting the impacts of potential global disruptions (e.g., the COVID-19 pandemic) and critical maritime infrastructure failures (e.g., the Suez Canal obstruction) on the shipping network, which are of great concern not only to academia but also to industry.
ABSTRACT
Recently, an increasing number of companies have encountered random production disruptions due to the COVID-19 pandemic. In this study, we investigate a two-stage supply chain in which a retailer can order products from a low-price (“cheap”) unreliable supplier (who may be subject to an uncertain production disruption and partially deliver the order) and an “expensive” reliable supplier at Stage 1 and a more “expensive” backup supplier at Stage 2. If the disruption happens, only the products that were produced before the disruption time can be obtained from the unreliable supplier. It is found that in the case with imperfect demand information updating, the unreliable supplier is always used while the reliable supplier can be abandoned. The time-dependent supply property of the unreliable supplier reduces the retailer's willingness of adopting the dual sourcing strategy at Stage 1, compared with the scenario with all-or-nothing supply. Different from the case with imperfect demand information updating, either the reliable or unreliable supplier can be abandoned in the case with perfect demand information updating. We derive the optimal ordering decisions and the conditions where single sourcing or dual sourcing is adopted at Stage 1. We conduct numerical experiments motivated by the sourcing problem of 3M Company in the US during the COVID-19 and observe that the unreliable supplier is more preferable when the demand uncertainty before or after the emergency order is higher. Interestingly, the retailer tends to order more from the unreliable supplier when the production disruption probability is larger in some cases.
ABSTRACT
This paper studies the vulnerability of the worldwide air transportation network (WATN) during a global catastrophe such as COVID-19. Considering the WATN as a weighted network, many airport connections could be completely or partially disrupted during such extreme events. However, it is found that existing weighted metrics cannot reflect the impact of connection capacity reduction on network connectivity. Therein, this work proposes a novel network efficiency metric termed as layered weighted network efficiency (LWNE) metric to measure the connectivity of the air transportation networks (ATNs) and study their vulnerability in response to different levels of disruptions, including airport level, country level, and global level. The most critical airport connections and their impact on network connectivity are identified. It is found that the critical connections are mostly between so-called bridge airports but not core airports in the WATN. By examining the impact of partial link disruptions, it is found that some connections mainly serve local travel demand and are very robust to partial disruptions, while the others connecting global hubs are sensitive to partial disruptions. Further, the WATN is robust to the individual disconnection of most countries; however, it is vulnerable to the simultaneous disconnection of countries that serve international transfers. Interestingly, the WATN is insensitive to the disconnection between any two countries, even those with sizeable domestic ATNs. Concerning global disconnections, as long as all the international connections hold 10% of their original flights, the WATN can still expect 40% of its pre-disruption performance. This paper deepens the understanding of ATNs under extreme events and provides a method for studying transportation networks' vulnerability facing global disruptions.
ABSTRACT
This paper studies the spatiotemporal variation of the worldwide air transportation network (WATN) induced by the COVID-19 pandemic in 2020. The variations are captured from four perspectives: passenger throughput, network connectivity, airport centrality, and international connections. Further, this work also considers both global and local connectivity-based metrics for the network analysis. Supported by real-world data, we show that the performance of the WATN has experienced a dynamic pattern of decline and recovery in 2020. Interestingly, the network metrics undergo tremendous variations in a very short period after the World Health Organization declared COVID-19 as a pandemic, with the number of flights and connections dropping by more than 40% within only the first four weeks. Intuitively, the passenger throughput's changing rate is highly correlated to confirmed cases' growth rate during the early period of the COVID-19 outbreak. However, the air transport response to the pandemic condition is very diverse among different countries. The major airports in the WATN fluctuate gradually in different pandemic stages, which is further influenced by the domestic pandemic situation that restricts airport operations. Also, the restoration speed of local connectivity is faster than that of global connectivity because the recovery of international aviation is geographically dependent on different policies of travel restriction, conditional openings, and the number of COVID-19 cases. The analysis deepens our understanding to formulate bilateral policies for pandemic-induced ATN design and management.
ABSTRACT
The COVID-19 pandemic has seriously impacted the air transport network (ATN) globally. Policies to restrict international passenger arrivals adopted by many countries are effective responses to control the spread of the virus. This paper studies the impact of two entry restriction policies implemented by some countries against international travelers during COVID-19, i.e., direct flight suspension and complete entry suspension, on the international connectivity (IC) of ATNs. Firstly, the concept of international air transport network (IATN) is defined, and a novel weighted IC index for ATNs is proposed considering flight frequency. Furthermore, to systematically analyze the difference between two policies, the hierarchical structure of the IATN is investigated, followed by studying the change of the IC index assuming different countries impose the two policies. Taking China as an example, this paper evaluates the influence of two policies based on real policy implementation of some countries against travelers from China. Besides, the critical countries affecting the IC are identified, and the network robustness is assessed. Implications for assessing and ranking the impact of different countries under different policies are provided and discussed. Lastly, two extensions are presented to discuss the impact of partial suspension and response actions such as air travel bubble. This work is one of the first to study the impact of country-to-country disconnection on air transport connectivity and deepens our understanding of the performance of ATNs during emergencies.