Dangerous Goods Vehicle Routing Optimization Model Considering Time-Varying Conditions

To achieve safety control and minimize transportation costs in the transportation of dangerous goods vehicles, a multi-objective path optimization model is established. The transportation risk is evaluated by the population exposure number of passing road sections. A time-dependent function based on travel speed is employed to capture the dynamic characteristics of the urban road network. An optimization model with the goals of minimizing total transportation cost and transportation risk is constructed. A multi-objective evolutionary algorithm is designed based on NSGA-II combined with the simulated annealing algorithm. The model and algorithm are verified by simulating the dangerous material transportation network with 19 demand nodes. The results show that the algorithm can be used to obtain Pareto solutions, which will provide decision support for the scheduling of dangerous material transportation vehicles.