The use of straddle carrier in container terminals can reduce the operation links and reduce the types and quantity of terminal mechanical equipment， at the same time， the setting of buffer capacity is very important. Firstly， in order to reduce the overall completion time of the terminal， improve the operation efficiency of the terminal， as well as solve the spatial-temporal coordination problem caused by joint loading and unloading operation of using straddle carrier as horizontal transportation equipment with quay crane， the dual-cycle operation strategy was introduced and joint operation sequence optimization problem of quay crane and straddle carrier was studied. Secondly， a mixed integer programming model was established to minimize the total completion time. In the model， the practical constraints of dual-cycle operation of quay crane and straddle carrier， as well as the constraints of the buffer capacity of quay crane and safety time were considered. Thirdly， aiming at the limitations of traditional Tabu Search （TS） algorithm， an greedy algorithm based reactive TS algorithm was designed by introducing greedy algorithm， multi-neighborhood search method and reactive algorithm， and numerical experiments were conducted. Experimental results verify the effectiveness of the proposed model and algorithm. Finally， through the experimental analysis of the number of buffer capacity and straddle carrier， the ratio of quay crane and straddle carrier， the optimal number of straddle carriers and buffer capacity， as well as the ratio of quay crane and straddle carrier were obtained. The results show that compared with traditional terminal equipment configuration， the dual-cycle operation strategy can reduce the number of straddle carriers and improve the utilization rate of quay crane and straddle carrier.

Aiming at the optimization problem of multimodal transportation path optimization under the uncertainty of time window and demand， trapezoidal fuzzy numbers were used to express fuzzy demand and fuzzy time window， and a multi-objective fuzzy chance-constrained model was established considering carbon emission costs， transportation costs， and customer satisfaction. The fixed crossover and mutation probabilities will directly affect the convergence of the algorithm. For this problem， the adaptability was combined with the Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ）， and the effectiveness of the proposed model and algorithm was verified by comparing them with DOCPLEX and NSGA-Ⅱ. Finally， the influence of changes in carbon tax and fuzzy demand preference value on optimization results were explored. The research results show that the proposal of carbon tax can effectively promote “road-to-rail transportation and road-to-water transportation” significantly， thereby reducing carbon emissions. But too high carbon tax does not necessarily reduce carbon emissions， and also causes excessive costs to enterprises. And the increase of fuzzy demand preference value will lead to the increase of total cost， which means that transportation economy and reliability cannot be obtained at the same time. Therefore， setting reasonable carbon tax and fuzzy demand preference value is an effective way to improve the environmental benefit and transportation benefit of multimodal transportation.

Aiming at the power problem of Automated Guided Vehicle （AGV） in the process of performing tasks in Automated Container Terminal （ACT）， an integrated scheduling considering AGV charging strategy based on improved Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ） was proposed. Firstly， considering the power consumption of AGV under different operating statuses in the integrated scheduling mode of quay crane， yard crane and AGV， a multi-objective mixed programming model with the goal of minimizing the completion time and total power consumption was established. Secondly， to improve the performance of the traditional NSGA-Ⅱ， an adaptive NSGA-Ⅱ was designed and compared with CPLEX solver and Multi-Objective Partical Swarm Optimization （MOPSO） algorithm on performance. Finally， different charging strategies and equipment number ratios of AGV were designed for experimental research. The experimental results of algorithm comparison show that the solution results of the adaptive NSGA-Ⅱ are improved by 2. 80% and 2. 63% respectively on the two objectives proposed compared with NSGA-Ⅱ. The experimental results of applying the adaptive NSGA-Ⅱ to study the ratio of charging strategies and equipment number ratios show that increasing AGV charging number can reduce AGV charging time， and adjusting the ratio of the equipment number to 3：3：9 and 3：7：3 lead to the highest time utilization of yard crane and AGV respectively. It can be seen that the AGV charging strategy and equipment number ratio can influence the terminal integrated scheduling with multiple equipment.