Reinforcement learning-based genetic algorithm for solving low carbon multimodal transportation path planning problem

To address the low-carbon multimodal transportation path planning problem, this paper firstly constructs a model of multimodal transportation path planning considering carbon trading, which minimizes total transportation cost as the objective function. A reinforcement learning-based genetic algorithm is designed to solve the model, and the Q-learning algorithm is implemented to adjust the crossover probability and mutation probability in the genetic algorithm to optimize the ability to avoid getting trapped in the local optima of the algorithm. Finally, arithmetic experiment verifies that the reinforcement learning-based genetic algorithm has a superior performance and the method can effectively reduce the transportation cost of multimodal transportation, improve transportation efficiency and reduce carbon emission.