Optimization method of mine waste transportation path in green and low carbon context based on improved particle swarm algorithm

In order to solve the optimal path decision-making problem between the application origin and destination of mine waste transportation vehicles, relying on the highway network database and the bridge access standard database, the optimal path decision-making problem of the application vehicles is transformed into the optimal path matching problem under the axle weight optimization. A multi-objective optimization model is established according to the spatial accessibility, bridge accessibility, economy and timeliness indexes, and a particle swarm algorithm based on nonlinear inertia weights and dynamic learning factors is used to improve the optimal solution, which is verified and analyzed with the application data of a mine waste vehicle as an example. The results show that the parameter-optimized mine waste transport vehicle can be The results show that the parameter-optimized bulky transport vehicle can match the passage path whose original passage conditions do not meet the requirements, and the new path makes full use of the highway capacity and saves time cost, which verifies the feasibility of the optimization method. The results show that the new path can fully utilize the road capacity and save time cost, which verifies the feasibility of the optimization method.