## 1 Introduction  
One of the basic requirements signaled by the TLS (transport, logistic, expedition) market is developing an IT solution enabling effective, quasi-optimal, and quick planning of deliveries to given recipients (destination points). Distribution of goods among customers can be classified as the vehicle routing problem (VRP), one of the best-known combinatorial, NP-hard problems [1, 2]. It is worth noting, in the case of fleet management, the issue can also be classified as a part of WorkForce Management (WFM) [3, 4]. The VRP issues need to find an optimal set of routes for a fleet of vehicles to deliver goods to a given group of customers. However, optimization tasks are often performed manually, and their effectiveness is related to the competencies and experience of logisticians and planners. The proposed routes are usually beneficial in the case of static scenarios, where the list of destinations is constant, but they may differ from optimal solutions in case of the particularly frequent changes in the distribution of destination points. Therefore, improving management efficiency by implementing automated WFM problem optimization systems is still a key market challenge. There are many different variants of the VRP problem and methods for solving them (a basic overview can be found in [5, 6, 7]). In [8], related papers from 2009-2015 were analyzed, in which 327 computational models were presented. Most often (in approx. 90% of cases), the researchers analyzed the Capacited VRP variant (with limited load capacity) and then (approx. 38% of models) Time Windowed VRP (with time restrictions). As the VRP is an NP-hard problem, heuristics and metaheuristics as Genetic Algorithm (GA) [9], Simulated Annealing (SA) [10; 11], Tabu Search (TS) [12; 13], Ant Colony Optimization (ACO) [14], and Particle Swarm Optimization (PSO) [15] are often used in practical solutions due to the large size of real-life problem. Furthermore, many authors often propose multi-step or hybrid methods of obtaining satisfactory results [16; 17]. Current transport management systems are also supported by geographical information System (GIS) solutions. It is defined as a computer-based system for collecting, editing, integrating, visualizing, and analyzing spatially-referenced data [18; 19; 20]. Its potential has been expanded with the popularization of Global Navigation Satellite Systems (GNSS), including Europe's Galileo, the USA's NavSTAR Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS), and China's BeiDou Navigation Satellite System. Primarily, the GPS has been utilized in travel surveys since the late 1990s. Monitoring of transport fleets is one of the most popular applications that have been used for over 20 years [21]. This solution can significantly minimize transport costs by reducing unnecessary and redundant routes. In [22; 23] the importance of GPS in the logistics industry was discussed. More advanced fleet management systems using GPS were presented in [24; 25; 26]. In work [24], GNSS systems were enriched with communication modules based on LoRaWAN. Aspects of operation in sending and receiving messages from and to prototypes developed for the vehicles were discussed, complying with the established location, tracking, exchange data, and security requirements. These systems are a valuable source of information used in WFM/VRP issues. Along with the development of GIS/GNSS systems, numerous additional services support the vehicle routing process. Many open-source geographic information systems (GIS) routing tools such as Qgis Road Graph plugin (QRG), Open Street Routing Machine (OSRM), Google Maps Engine (GME), GraphHopper (GH), and OsmAn can also be considered as potential candidates to help solve optimization problems. Integration with mentioned systems is possible thanks to the so-called Mapping API [27; 28]. These solutions allow one to develop of new concepts of transport fleet management.