INFOLOGICAL MODEL OF FACTORS, INDICATORS, AND ROUTE OPTIMALITY CRITERIA IN GRAPH DATABASES

Abstract

Modern transport and logistics systems operate in a highly dynamic and complex environment, characterized by dense traffic flows, high volumes of heterogeneous information, and the necessity to make multi-criteria decisions under uncertainty. These challenges are further intensified in the era of digital transformation, where the rapid evolution of intelligent logistics systems, globalized supply chains, economic volatility, and external factors such as military conflicts or pandemics place additional demands on the adaptability and responsiveness of transport infrastructures.

In this context, there is a growing need for innovative methods and technologies that enable the storage, processing, and analysis of large-scale transport data in real time. One such solution is the integration of graph-based data models, which are particularly suitable for representing and analyzing complex transport networks due to their natural structure and ability to support flexible querying.

The paper proposes an infological model that formalizes the key factors, indicators, and optimality criteria relevant to transportation routing. This model serves as a semantic layer that connects high-level decision-making logic with the underlying data architecture. When implemented using graph databases, the model provides an efficient and scalable framework for adaptive route optimization.

By incorporating multi-criteria analysis into the decision-making process, the developed optimization approach allows for the identification of transportation routes that strike a balance between cost efficiency, delivery time, network congestion, safety considerations, and other contextual parameters. Moreover, the adaptive nature of the system enables continuous reconfiguration of route parameters in response to real-time changes in network conditions, infrastructure availability, and external disruptions.

The proposed approach enhances the resilience, efficiency, and intelligence of modern transport systems, offering a foundation for the development of decision support tools in the field of logistics and supply chain management.