Methodology for evaluating the resilience of complex logistics systems under extreme infrastructure stress

Анотація

The purpose of the article is to develop and scientifically substantiate an integrated methodology for assessing the viability of complex logistics systems under extreme loads on the infrastructure, taking into account modern security, economic and technological challenges. The study aims to develop a comprehensive approach to analyzing the functioning of logistics networks that increases the validity of management decisions in crisis conditions.

The methodological basis of the study is a system analysis, scenario approach and simulation modeling, supplemented by methods of statistical, comparative and multi-criteria analysis. Network models were used to determine structural dependencies between elements of logistics systems, scenario forecasting to assess sensitivity to external influences and quantitative indicators for an integrated assessment of functional stability, adaptability and recoverability. The methodology was tested through an analysis of the dynamics of transport indicators and changes in logistics flows under crisis conditions.

Modern theoretical and methodological approaches to assessing the viability of logistics systems are summarized, and key factors influencing extreme infrastructure loads are identified, including overloading of transport nodes, capacity limitations, infrastructure destruction, information failures and changes in the structure of transport flows. An integrated assessment model has been developed that combines structural, functional and dynamic levels of analysis with an expanded system of quantitative indicators, allowing for a comprehensive assessment of the state of logistics systems, timely identification of critical risks and the formulation of adaptive management strategies. The proposed model can be used to improve mechanisms for managing logistics processes, optimizing resource and forming strategies for responding to crisis challenges in an environment of high uncertainty. Further research is required to adapt the model to industry-specific conditions, develop digital assessment tools and improve methods for forecasting the long-term performance of logistics systems.