INFORMATION TECHNOLOGY FOR PLANNING LOGISTICAL PROCESSES WITH TERRITORIAL ZONING IN THE CONTEXT OF REAL-WORLD URBAN DEVELOPMENT

Abstract

Context. The effectiveness of planning geographically distributed logistics systems, particularly emergency services and humanitarian missions, is highly dependent on the accuracy with which the spatial environment is represented. Traditional approaches to location-allocation problems often rely on simplified geometric metrics and averaged demand data. Ignoring natural barriers, failing to account for actual population density, and neglecting the specifics of the road network in real-world urban environments can lead to significant planning errors. Integrating modern Geographic Information System (GIS) technologies with location-allocation algorithms based on mathematical optimization models allows for a transition from abstract calculations to highly accurate modeling.Objective. The aim of this work is to improve the quality and feasibility of management decisions regarding the organization of geographically distributed logistics systems by integrating modern GIS technologies and mathematical tools for solving optimization problems related to the optimal location-allocation of resources, including models and methods for multiplex zoning of territories and a two-stage distribution model for material flows, taking into account the location of infrastructure facilities.Method. The research is based on methods for solving continuous location and resource allocation problems, specifically incorporating multiplex zoning of territories and a two-stage allocation process. These methods are integrated with technologies for processing large volumes of geospatial data. A key feature of the approach is the use of detailed information about the road network and pedestrian accessibility (through the OSRM routing service), as well as high-precision population data (WorldPop), which are processed using modern Python libraries for vectorized calculations. This allows for the integration of optimization algorithms with realworld constraints of the spatial environment directly during the calculation process.Results. A software and algorithmic framework has been developed and experimentally investigated to determine the relationship between the quality of zoning and the level of detail in the input data. Experimental results have demonstrated that using a realistic road network and population density data leads to a significant transformation of responsibility zones compared to traditional models. The zones adapt to physical barriers (rivers, railways) and concentrate in areas of actual demand. The effectiveness of algorithms for optimal multiplex partitioning and a two-stage resource allocation process has been examined, along with changes in the configuration of responsibility zones and the location of collection centers, when transitioning from Euclidean metrics to metrics based on actual travel paths.Conclusions. The results obtained from the developed software complex confirm the validity of integrating real-world GIS data during the initialization phase of optimization algorithms to achieve appropriate solutions for location-allocation problems in real environment. Multiplex zoning enhances the reliability of providing logistical services by accounting for unforeseen circumstances (e.g., flexibility in consumer choice of service centers, temporary unavailability of the nearest center or routes). The allocation of resources based on the use of models and methods for optimal partitioning of sets with additional linkages allows for a synergistic reduction in logistical costs across two distinct stages. The incorporation of information about the road network and the spatial distribution of demand directly into the computational core improves the accuracy in determining optimal locations and service areas, minimizing the risks of making incorrect decisions