1st ICAI 2020

International Conference on Automotive Industry 2020

Mladá Boleslav, Czech Republic

where: disruption impact disruption time

(2) (3)

P Req – required performance level, P(t) – performance at the time t , t 2 – beginning of disruption, t 5 – end of disruption. Resilience metrics is represented as a collective term described by three dimensional vector as follows: (4) where: (5)

(6)

(7)

Based on the work of Bukowski (2019) we propose a general framework for evaluation of transport networks resilience. It consists of 8 steps, and its simplified algorithm is shown in Figure 2. The individual steps of the algorithm will be discussed below. Step 1. A so-called ‘System Engineering Process’ approach can be helpful for problem defining (SEF 2001). The systems engineering process is a top-down comprehensive, iterative and recursive problem solving process, applied sequentially through all stages of development, that is used to: • Transform needs and requirements into a set of system product and process descriptions, • Ensure the compatibility, interoperability and integration of all functional and physical interfaces, • Generate information for decision makers to identify and characterize system’s vulnerabilities and technical risks. Step 2. The entire process is presented as a chain of operations and flows that are subjected to disturbances. The raw data stored in Data Acquisition block is sent to the Data Processing block and subjected to the preparation process. The processed data flows to Data Verification block where they are verified for their veracity and then sent to Data Forming block for generation of Data Mining and Fusion of Data. Raw information flows to Information Evaluating block, where they are evaluated for their utility value and selected on this basis. The selected information is used in Building Patterns block to search for relationships between individual pieces of information and build logical patterns, based on which a new knowledge is generated in New Knowledge Creation block. The last stage of the chain is to enrich the existing knowledge base with new knowledge and to create initial knowledge for further acquisition of new data (Bukowski 2019).

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