Geographically supported service center positioning and optimization system for dynamic cargo distribution processes

Abstract

The invention relates to an integrated system that enables the determination of the most suitable locations for cargo service centers, the optimization of responsibility areas, and the efficient planning of distribution routes in the logistics and cargo transportation sector The system operates based on data received from the user, digital map data provided by the geographical information systems module (30), dispatch data (100), and dynamic parameters. The service center positioning module (40) performs density analyses and customer address clustering to determine the most suitable service center locations. The route optimization module (60) optimizes distribution routes by using dynamic data such as traffic density and delivery durations. The simulation module (70) evaluates system performance by testing alternative scenarios, and the visualization tool (110) presents analysis results to the user (10) on a dynamic map. Through this structure, time and cost optimization is achieved, thereby increasing overall logistics efficiency.

Description

[0001] DESCRIPTION

[0002] Geographically Supported Service Center Positioning and Optimization System for Dynamic Cargo Distribution Processes

[0003] Technical Area

[0004] The present invention relates to a system used in the logistics and cargo transportation sector, specifically designed to determine cargo service center locations, responsibility zones, and distribution routes by utilizing geographical information systems (GIS) and optimization algorithms. The invention is developed to increase efficiency in logistics operations, accelerate distribution processes, reduce operational costs, and enhance customer satisfaction.

[0005] The system is designed to eliminate the shortcomings of traditional methods used in cargo transportation and delivery processes. By analyzing numerous parameters — such as traffic density, customer address distributions, historical delivery data, and regional demand intensity — the system automatically calculates the most suitable cargo service center locations and the responsibility zones of these centers. Additionally, it incorporates a dynamic structure to ensure workload balancing between service centers and to achieve maximum delivery efficiency across designated routes.

[0006] The invention provides optimization not only based on geographical distance but also by taking into consideration traffic data, delivery frequency, customer density, and logistics costs. Particularly in large metropolitan areas with variable traffic conditions or regions with high delivery volume, the analyses provided by the system contribute to building faster and more accurate delivery processes.

[0007] This technical field presents an innovative solution aimed at increasing operational efficiency at a time when digitalization and data-driven management are gaining importance in the logistics sector. The invention seeks to enable cargo transportation and delivery processes to be managed in a faster, more accurate, and cost-effective manner.Background of the Invention

[0008] In the logistics and cargo transportation sector, processes such as positioning cargo service centers, defining their responsibility zones, and optimizing distribution routes are generally based on analyses carried out through manual methods or static data. Existing systems primarily consider geographical distance and regional demand density when determining service center locations; however, they do not sufficiently account for dynamic parameters such as traffic congestion, variable customer behavior, delivery frequency, and workload imbalance. This leads to reduced operational efficiency, longer delivery times, and decreased customer satisfaction.

[0009] In the known state of the art, logistics companies typically determine fixed service center locations based on regional demand analyses and attempt to balance workloads between these centers through manual practices. However, this approach becomes insufficient particularly in large metropolitan areas where traffic density fluctuates or in regions where demand changes instantaneously. For example, during peak hours, traffic congestion may cause delivery delays, while during low-demand periods, vehicles may operate unnecessarily, increasing fuel consumption and raising operational costs.

[0010] Additionally, existing systems fail to effectively analyze the geographical distribution of customer addresses, resulting in imbalanced workload distribution between service centers. For instance, one service center may be responsible for a high-density region, whereas another may operate in a low-demand area. Such imbalance renders delivery processes inefficient and negatively impacts the quality of service provided to customers.

[0011] In known technologies, optimization of cargo delivery routes is typically performed through fixed routing structures, which are unable to provide solutions adapted to changing traffic conditions or immediate delivery requirements. For example, traffic congestion occurring along a predefined route may disrupt the entire delivery chain and extend delivery times, leading to customer complaints. Furthermore, existing systems may analyze delivery data retrospectively but lack the ability to extract meaningful insights from such data and use them to optimize future operations.

[0012] Another issue observed in the known state of the art is that the capacities of cargo service centers are not scaled appropriately based on parameters such as customer density anddelivery frequency. For example, a service center located in a high-demand area may have insufficient capacity, while another center in a region with lower demand may operate below capacity. This results in inefficient resource utilization and decreases the overall effectiveness of logistics operations.

[0013] In conclusion, existing systems lack integrated solutions that consider dynamic parameters in logistics and cargo transportation processes. The inability to collectively analyze factors such as traffic density, the geographical distribution of customer addresses, delivery frequency, and regional demand intensity leads to increased operational costs, extended delivery times, and reduced customer satisfaction. Accordingly, there is a need for the development of a new system.

[0014] Objectives of the Invention

[0015] The primary objective of the present invention is to develop an innovative system that integrates geographical information systems (GIS) and optimization algorithms in order to eliminate the deficiencies found in existing systems used in the logistics and cargo transportation sector, and to optimize cargo service center locations, responsibility zones, and distribution routes. The invention is designed to enable logistics processes to be managed more rapidly, accurately, and cost-effectively.

[0016] Another objective of the invention is to enable the analysis of dynamic parameters — such as traffic density, customer address distributions, delivery frequency, and historical cargo data — in order to determine cargo service center locations in the most optimal manner. In this way, delivery delays caused by traffic congestion are prevented, manpower and vehicle utilization become more efficient, and customer satisfaction is improved.

[0017] A further objective of the invention is to automatically ensure workload balance among cargo service centers. By analyzing the responsibility zones of each center, the system prevents imbalances in workload distribution and enhances operational efficiency. Thus, delivery delays and insufficient capacity issues arising in high-demand centers are eliminated, while the resources of low-demand centers are utilized more efficiently.

[0018] Another important objective of the invention is to optimize delivery routes, ensuring that vehicles follow the shortest and most efficient paths. Through the integration ofgeographical information systems and optimization algorithms, cargo vehicles can generate dynamic routes by considering traffic density, delivery sequence, and geographical proximities. This reduces fuel consumption, lowers the carbon footprint, and significantly decreases logistics operational costs.

[0019] The invention also aims to generate forecasts for future logistics needs by analyzing historical cargo data. Using big data analytics techniques, the system identifies trends such as delivery frequency and customer density and utilizes these insights to enable more efficient service center positioning and route planning processes.

[0020] In conclusion, the invention provides an innovative system designed to increase efficiency in the logistics and cargo transportation sector, reduce operational costs, shorten delivery times, and enhance customer satisfaction. By contributing to the digitalization of logistics processes and the adoption of data-driven management, the system aims to create substantial value within the industry.

[0021] The invention, in order to achieve the objectives described above, is a system that enables determining the most suitable locations of cargo service centers, optimizing responsibility zones, and planning distribution routes within logistics and cargo transportation processes, and comprises: at least one user device that accesses the system and performs data entry, a database that securely stores geographical data, waybill data, analysis results and dynamic parameters kept within the system, and enables data flow among the other modules, a geographical information systems module that processes street-avenue information, neighborhood boundaries, building address data and population densities on a digital city map based on the analysis-zone parameters provided by the user through the system, and that transfers traffic density and current population data to the database through dynamic data integration, an integration module that transfers waybill data such as cargo addresses, delivery durations and order volumes to the database and enables data flow to other modules for analysis processes, a data analysis module that uses the data provided by the database, the geographical information systems module and the integration module to perform customer segmentation based on parameters such as order frequency and order volume, and analyzes historical cargo data to forecast future cargo demand densities, a route optimization module that optimizes the distribution routes required by the service center positioning module based on traffic density, delivery times and cargo densities,and enables these routes to be planned according to shortest distance, lowest cost and fastest delivery criteria, a service center positioning module that performs density analyses by processing geographical data provided by the geographical information systems module, dispatch data provided by the integration module, customer segmentation and predictive analyses processed by the data analysis module, and the distribution routes determined by the route optimization module, thereby optimizing responsibility zones and determining the most suitable service center locations, a visualization tool that visualizes the results produced in the analysis and optimization processes on a dynamic map using the map data provided by the geographical information systems module, and presents such visualization to the user through the user device and a web interface that enables user access to the system through the user device, allows the entry of analysis parameters, and enables detailed examination of analysis results and visualized data.

[0022] Description of the Figures

[0023] Figure- 1 is a schematic representation of the geographically supported service center positioning and optimization system for dynamic cargo distribution processes of the invention.

[0024] Description of the Part References

[0025] 10. User

[0026] 20. User Device

[0027] 30. Geographical Information Systems Module

[0028] 40. Service Center Positioning Module

[0029] 50. Data Analysis Module

[0030] 60. Route Optimization Module

[0031] 70. Simulation Module

[0032] 80. Integration Module

[0033] 90. Database

[0034] 100. Waybill Data

[0035] 110. Visualization Tool

[0036] 120. Web InterfaceDetailed Description of the Invention

[0037] The invention relates to an integrated system that enables determining the most suitable locations of cargo service centers, optimizing responsibility zones, and planning distribution routes in the most efficient manner within the logistics and cargo transportation sector. The system operates through a complex data flow that includes processing user-provided data, performing geographical information systems analyses, density evaluations, and route planning. The system comprises interconnected components such as the user (10), user device (20), geographical information systems module (30), service center positioning module (40), data analysis module (50), route optimization module (60), simulation module (70), integration module (80), database (90), waybill data (100), visualization tool (110) and web interface (120). These components collectively form a data-driven decision mechanism to achieve time and cost optimization in cargo distribution processes.

[0038] The system is initiated by the user (10) through the user device (20). The user accesses the system via the web interface (120) and defines the analysis region and parameters. The data entered by the user is processed by the geographical information systems module (30) to begin system operation. The geographical information systems module (30) serves as the primary data source of the system and consists of a digital city map and a dynamic data integration layer. The digital city map includes street-avenue information, building address data, neighborhood boundaries, and population densities. The dynamic data integration layer enables real-time incorporation of variable data such as traffic density, current population information, and economic parameters. These data enable the geographical accuracy of service center responsibility zones and support the optimization of distribution processes. The same data set also forms the initial stage input required by the service center positioning module (40), which serves as the core of the system.

[0039] The service center positioning module (40) performs analyses using geographical data provided by the geographical information systems module (30), waybill data (100) transferred into the system through the integration module (80), and customer density information stored in the database (90). The service center positioning module (40) performs density analyses, groups customer address concentrations, and optimizes responsibility zones based on workload balance. It also determines the most suitableservice center locations through algorithmic processes and evaluates the performance of existing service center locations. To conduct these analyses, the service center positioning module (40) uses several algorithms, including voronoi diagrams, clustering algorithms (such as k-means and dbscan), and genetic algorithms for optimization. Voronoi diagrams enable optimal spatial partitioning of regions, clustering algorithms group customer address densities to optimize workload balance, and genetic algorithms determine the most efficient service center locations by minimizing total distribution distance. The service center positioning module (40) also collaborates with the data analysis module (50) to forecast future cargo density regions.

[0040] The route optimization module (60) operates as a sub-module within the service center positioning module (40) and optimizes distribution routes based on the determined service center locations. The route optimization module (60) plans cargo routes according to shortest distance, lowest cost, and fastest delivery criteria by considering dynamic parameters such as traffic density and delivery durations. By dynamically updating distribution routes based on real-time traffic data, the route optimization module (60) enhances system efficiency and minimizes time loss.

[0041] The data analysis module (50) performs advanced analyses on data provided by the service center positioning module (40). It conducts customer segmentation using parameters such as order frequency and order volume. Through segmentation, customer groups are identified and service center locations are optimized according to customer demands. The data analysis module (50) also performs predictive analyses using historical cargo data to forecast future cargo demand densities. These analyses support capacity planning for service centers and improve the accuracy of route planning through data provided to the route optimization module (60).

[0042] The simulation module (70) enables testing the analyses and planning results under different scenarios. Variables such as service center locations, number of vehicles, and cargo distribution zones are used to generate alternative scenarios and analyze system performance. The simulation module (70) validates the route plans generated by the route optimization module (60) and ensures system adaptability to various conditions. As a result, the system identifies the most efficient solution and increases the reliability of cargo distribution processes.The integration module (80) ensures the flow of data within the system and transfers waybill data (100) such as cargo addresses, delivery durations, and order volumes into the system. These data are securely stored in the database (90) and prepared for use in analysis processes. The database (90) functions as the central data repository of the system and stores digital map data used by the geographical information systems module (30), waybill data used by the service center positioning module (40), and analysis results.

[0043] The final outputs from all analysis and optimization processes are presented to the user through the visualization tool (110). Using the map data provided by the geographical information systems module (30), the visualization tool (110) displays responsibility zones, cargo density analyses, and distribution routes dynamically on a digital map. Through the web interface (120), the user (10) can examine these data in detail and access the analysis results. By clicking on desired regions on the map, the user can review outcomes and manage decision-making processes.

[0044] In the invention, all components operate in an integrated manner, with the service center positioning module (40) serving as the central element. This module balances responsibility zones, optimizes workloads, and determines the most suitable service center locations. The route optimization module (60) accelerates distribution processes by creating the most efficient route plans. The data analysis module (50) and simulation module (70) ensure adaptation to future demands, while the visualization tool (110) presents analysis results to the user in an understandable way. Through this structure, cargo distribution processes are optimized, time loss and costs are minimized, and operational efficiency is increased.

[0045] The operational flow of the system constructed through the components described above is as follows:

[0046] The system is initiated by the user (10) through the user device (20) via the web interface (120), where the user logs into the system using authorized credentials. Menus, analysis tools, and map screens appropriate to the user’s authorization level are displayed.

[0047] The user (10) selects the region to be analyzed, enters the required parameters via the web interface (120), and initiates the data flow within the system.Through the geographical information systems module (30), geographical data belonging to the selected region are processed. Street-avenue information, neighborhood boundaries, building addresses, and population densities are transferred from the digital city map, while dynamic parameters such as traffic density and current population data are incorporated through dynamic data integration. All data are stored in the database (90) for use in analyses.

[0048] Through the integration module (80), waybill data (100) are transferred into the system. These data include cargo addresses, delivery durations, and order volumes and are stored securely in the database (90) for use in analysis processes.

[0049] Using geographical data provided by the geographical information systems module (30) and waybill data (100) transferred through the integration module (80), analysis processes are initiated via the service center positioning module (40). This module performs density analyses, groups customer address concentrations, determines responsibility zones based on workload balance, and identifies the most suitable service center locations through algorithmic processes. Existing service center performance is also evaluated and optimized.

[0050] Through the data analysis module (50), customer segmentation is performed using data provided by the service center positioning module (40). Customer groups are created based on order frequency and order volume; predictive analyses are performed using historical cargo data, and future cargo demand densities are forecast. These analyses support the system’s forward-planning capability.

[0051] Through the route optimization module (60), distribution routes are optimized using dynamic data such as traffic density, delivery durations, and cargo densities based on locations determined by the service center positioning module (40). This module plans routes according to shortest distance, lowest cost, and fastest delivery criteria and continually updates them based on real-time traffic data.

[0052] Through the simulation module (70), the analyses and route plans produced by the route optimization module (60) are tested under different scenarios. Parameters such as number of vehicles, cargo distribution regions, and delivery durations are considered toevaluate system performance and determine the most efficient solution, thereby increasing the reliability and adaptability of the system.

[0053] Through the visualization tool (110), analysis and optimization results are visually presented on a digital map using map data provided by the geographical information systems module (30). The user (10) can examine responsibility zones, cargo density analyses, and distribution routes in detail via the web interface (120). By interacting with the map, the user can review results in desired areas and manage decision-making processes.

[0054] As a result of this integrated operation, the most suitable cargo service center locations are determined, responsibility zones are optimized, and distribution route efficiency is enhanced. The interconnection of all modules reduces operational costs, shortens cargo delivery times, and ensures the accuracy of logistics planning.

Claims

CLAIMS1. A system configured to determine the most suitable locations of cargo service centers, to optimize responsibility zones, and to plan distribution routes within logistics and cargo transportation processes, characterized in that it comprises:• at least one user device (20) that provides access to the system and enables data entry,• a database (90) that securely stores geographical data held within the system, waybill data (100), analysis results, and dynamic parameters, and ensures data flow among other modules,• a geographical information systems module (30) that, based on the analysis region parameters provided by the user (10) through the system, processes street-avenue information, neighborhood boundaries, building address data, and population densities over a digital city map, and that transfers traffic density and current population data into the database (90) through dynamic data integration,• an integration module (80) that transfers waybill data (100) such as cargo addresses, delivery durations, and order volumes into the database (90) and ensures data flow to other modules for analysis processes,• data analysis module (50) that performs customer segmentation using parameters such as order frequency and order volume based on the data provided by the database (90), the geographical information systems module (30), and the integration module (80), and that analyzes historical cargo data to forecast future cargo demand densities,• a route optimization module (60) that optimizes distribution routes required by the service center positioning module (40) based on traffic density, delivery durations, and cargo densities, and that ensures route planning according to shortest distance, lowest cost, and fastest delivery criteria,• a service center positioning module (40) that processes geographical data provided by the geographical information systems module (30), waybill data (100) provided by the integration module (80), customer segmentation and predictive analyses processed by the data analysis module (50), and distribution routes determined by the route optimization module (60), and thatperforms density analyses, optimizes responsibility zones, and determines the most suitable service center locations,• a visualization tool (110) that visualizes the results produced during analysis and optimization processes on a dynamic map using map data provided by the geographical information systems module (30), and that presents said results to the user through the user device (20),• a web interface (120) that enables system access through the user device (20), allows entry of analysis parameters, and enables detailed examination of analysis results and visualized data,wherein the system comprises the foregoing components.

2. The system according to claim 1, characterized in that it further comprises a simulation module (70) configured to test the performance of the distribution routes determined by the route optimization module (60) and to validate them under different scenarios.

3. The system according to claim 1 , characterized in that the service center positioning module (40) is configured to divide the responsibility zones in a spatially optimal manner by using voronoi diagrams.

4. The system according to claim 1 , characterized in that the service center positioning module (40) is configured to group customer address densities by using clustering algorithms (k-means, dbscan).

5. The system according to claim 1 , characterized in that the route optimization module (60) is configured to optimize the distribution routes by using genetic algorithms in order to consider traffic density and delivery durations.

6. The system according to claim 1 , characterized in that the data analysis module (50) applies machine learning models in predictive analyses to use historical cargo data.

7. A method for determining the most suitable locations of cargo service centers, optimizing responsibility zones, and planning distribution routes in logistics and cargo transportation processes, characterized by the steps of:• defining, by the user, the analysis region and parameters through the web interface (120) via the user device (20),• processing digital map data by the geographical information systems module (30) and integrating dynamic data such as traffic density and population data into the database (90),• transferring dispatch data (100) into the system by the integration module (80) and storing said data in the database (90),• performing density analyses and determining the most suitable service center locations by the service center positioning module (40) using geographical data, dispatch data (100), customer segmentation, and traffic density analyses,• performing customer segmentation and predicting future cargo demands by analyzing historical data via the data analysis module (50),• optimizing distribution routes based on traffic density and delivery durations via the route optimization module (60),• presenting the analysis and optimization results visually to the user on a digital map via the visualization tool (110).

8. The method according to claim 7, characterized in that it comprises a step of generating different scenarios and analyzing said scenarios via the simulation module (70) for the purpose of testing the performance of the determined service center locations and distribution routes.

9. The method according to claim 7, characterized in that it comprises a step of processing the digital map data provided by the geographical information systems module (30) to form a multi-layered map structure according to the analysis region defined by the user.

10. The method according to claim 7, characterized in that in the predictive analyses performed by the data analysis module (50), the customer segmentation results are dynamically updated and, when new data is integrated into the system, the analysis processes are automatically repeated.

11. The method according to claim 7, characterized in that the traffic density and delivery duration data used in route optimization by the route optimization module (60) are dynamically updated based on time intervals and integrated into the optimization process.

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