The optimization of cargo delivery processes with dynamic route updates in smart logistics

The object of research is the processes of transport logistics management under the influence of non-stationary factors of different nature on the functioning of street-road networks (SRN) in cities. The task of dynamic routing at large and variable loading of SRN sections is solved by managing the processes of cargo delivery in real time within the framework of the implementation of the Smart Logistics concept. Simulation studies of cargo delivery routing with dynamic real-time route updating using a modified ant colony algorithm and data on the dynamics of traffic flow (TF) were conducted using an SRN in the city of Kyiv as an example. Here, experimental data were obtained using motion sensors of intelligent transport systems. During the optimization, current data were used acquired online within the framework of the Internet of Things technology, as well as historical data obtained over past periods of time and averaged using Big Data (BD) technology. Route optimization at each stage of real-time updates was achieved using a modified ant colony algorithm. This method has a sufficiently high optimization performance and makes it possible, unlike many other intelligent methods, to directly take into account the non-stationary dynamics of TF within SRN. It is shown that the use of properly averaged BD historical data allows for more efficient planning of transport routes. The simulation studies indicate the possibility of using the proposed approach by transport companies and authorities to solve the problems of managing logistics flows in an automated mode under conditions of complex, unpredictable traffic