With regard to the Location-Routing Problem （LRP） of domestic waste transfer stations and incineration stations， by considering the economic objective and the obnoxious effect of waste facilities， a piecewise function of obnoxious effect related to wind direction and distance was designed， a Two-Echelon Multi-Objective LRP （2E-MOLRP） model was formulated， and a non-dominated algorithm combining Whale Optimization Algorithm （WOA） and Simulated Annealing （SA） algorithm was proposed， namely WOA-SA. Firstly， the random method and Clarke and Wright （CW） saving algorithm were used to optimize the initial population. Secondly， a nonlinear dynamic inertia weight coefficient was adopted to adjust the convergence speed of the WOA-SA. Thirdly， the global optimization ability was enhanced by designing the parallel structure of WOA-SA. Finally， the Pareto solution set was obtained by using the non-dominated sorting method. The analysis was carried out on 35 benchmark cases such as Prins and Barreto as well as a simulated case of Tianjin. The results show that the WOA-SA can find the Best Known Solution （BKS） of 20 benchmark cases， and has the mean values of the difference between the solution results and the BKSs of 0.37% and 0.08% on Prins and Barreto cases， which proves the good convergence and stability of the WOA-SA. The proposed model and algorithm were applied to the instance， and provided three schemes with different obnoxious effect values and economic costs for decision makers with different decision preferences. Therefore， the cost of waste recycling and the obnoxious effect of facilities on environment were reduced.

Due to high carbon emissions in the logistics and distribution process, from a low carbon perspective, a Low Carbon Vehicle Routing Problem with Pickup and Delivery (LCVRPPD) considering fuzzy demand was formulated, and a 2-OPT based differential algorithm was proposed to solve the problem. In the algorithm, the natural number encoding method was adopted and three different fitness functions were given. Then, the 2-OPT algorithm was introduced to replace the original mutation mechanism of differential algorithm, and the binomial crossover operators and greedy selection operator were combined, so as to accelerate the convergence of the improved algorithm. In the case study, Taguchi method was used to determine reasonable values of parameters in the improved algorithm, and the SPSS (Statistical Product and Service Solutions) analysis revealed that the solution of the model with the minimum total cost as the objective function is the best compared to those of the other two different objective models of transportation cost minimization and carbon minimization respectively. For examples with different customer scales, compared with the basic differential algorithm, the improved algorithm has the total cost reduced by 1.8% to 3.0% and the carbon emission decreased by 0.7% to 3.5%; compared with genetic algorithm, the improved algorithm has the total cost reduced by 1.9% to 16.47% and the carbon emission decreased by 1.2% to 4.3%; compared with particle swarm optimization algorithm, the optimization effect is more obvious, the improved algorithm has the total cost reduced by 4.0% to 22.5% and the carbon emission decreased by 1.56% to 7.88%, which verify the effectiveness and advancement of the proposed algorithm. In summary, the proposed model and algorithm can provide a reference for the low carbon routing problem of pickup and delivery vehicles.