Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase. The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style. Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.
The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.
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