For the low-carbon multimodal transportation planning problem with fuzzy demands and carbon emission limit, this paper proposed a multi-objective multimodal transportation mathematical model to minimize the path cost and carbon emission. Firstly, it used the chance constrained programming to deal with the uncertain demand represented by trapezoidal fuzzy number according to the characteristics of the model. Secondly, it designed the path relinking algorithm, multiple crossover operators, and mutation operators to replace the search process of original Harris hawks optimizer algorithm. It successfully applied the algorithm to the discrete optimization problems on the premise of preserving the original characteristics of this algorithm. Finally, it studied the multimodal transportation from Nanning city to Harbin city as a case, which gave multiple reasonable route scheme. HHHO was compared with other algorithms. The results show that HHHO, NSGA-Ⅱ, GA, SA and PSO all obtain a set of near-optimal solutions with five solutions in an ideal time, and the solution set of HHHO is closer to the optimal solution set. The running time of HHHO and the other four algorithms are 86.50 s, 118.26 s, 101.67 s, 81.22 s and 68.40 s, respectively. HHHO is faster than GA and NSGA-Ⅱ in running time. Therefore, these results verify the feasibility of the model and the effectiveness of hybrid Harris hawks optimizer algorithm.

[2023-05-29] Accepted Paper /doc/2023.03.0061/v1

[2023-10-05] Printed Article /doc/2023.03.0061/v2