A Mathematical Model for Optimizing the Loading of Trucks for Automated Transportation Management Systems Based on Machine Vision

Increasing truck loads, expanding transportation processes, and expanding intermodal transportation are exacerbating the impact on road surfaces. Organizations involved in cargo transportation are faced with the need to find effective solutions to optimize processes and select transport, taking into account legal restrictions. The use of automated decision-making systems based on aggregated big-data from telematic control systems is the best practice. The authors consider the problems of implementing systems for estimating mass, load and degree of body loading. Based on data on cost and potential harm to the road, a multicriteria optimization problem is solved to select the optimal transport model. The modified method of searching for Pareto-optimal solutions is used as a mathematical method. The results of experimental studies of the influence of transportation process parameters on the load capacity utilization rate are presented. The resulting mathematical optimization model is ready for integration into automated cargo transportation management systems and has a calculation error of up to 12.8%.