现代机场的停机位指派问题涉及多方利益的协调，包括旅客满意度、机场资源的合理指派以及碳排放量的控制。为解决这一复杂问题，考虑航班类型、飞机机型和停机位可用性等约束条件的基础上，建立一个多目标非线性整数规划模型。其优化目标包括：最小化旅客的步行距离、最大化机型匹配度以及最小化碳排放量。提出一种改进型自适应遗传算法求解指派问题模型。在初始化种群阶段，采用随机、贪婪+扰动多种策略生成多样性更好的初始种群；在算法迭代过程中，自适应调整交叉和变异的概率，并应用先交叉后变异和先变异后交叉两种进化策略，以提高求解效率和全局搜索能力。为了验证算法的有效性，对国内某机场的运营数据进行仿真实验。将改进型自适应遗传算法与传统遗传算法和粒子群算法进行对比，结果显示改进型算法在停机位利用效率、旅客满意度以及碳排放量控制方面均表现出显著优势。此外，通过对改进策略的有效性实验分析，进一步验证所提出算法改进策略的有效性和算法性能的稳定性。所提出的模型和算法为机场停机位管理提供了有力的决策支持，促进了旅客出行满意度的改善、资源的有效利用以及环境的可持续发展。

The issue of gate assignment at modern airports involves coordinating multiple interests, including passenger satisfaction, efficient allocation of airport resources, and control of carbon emissions. A multi-objective nonlinear integer programming model was developed to solve this complex problem, which considering constraints such as flight type, aircraft model, and gate availability. The optimization objectives of model include minimizing passengers' walking distance, maximizing aircraft-gate matching, and minimizing carbon emissions. An improved adaptive genetic algorithm was proposed to solve the gate assignment problem. In the population initialization phase, a combination of random and greedy-perturbation strategies was employed to generate a more diverse initial population. The probabilities of crossover and mutation were adaptively adjusted during the algorithm's iterations. Both crossover-first and mutation-first evolutionary strategies were applied to enhance solution efficiency and global search ability. To validate the effectiveness of the algorithm, some simulation experiments were conducted using operational data from the domestic airport. The improved adaptive genetic algorithm was compared with traditional genetic algorithms and particle swarm optimization algorithms. Results show that the improved algorithm significantly outperforms the others in terms of gate utilization efficiency, passenger satisfaction, and carbon emission control. Furthermore, the effectiveness of the proposed improvement strategies was confirmed through experimental analysis, demonstrating the stability and performance of the algorithm. The proposed model and algorithm provide robust decision support for gate management, contributing to enhanced passenger satisfaction, efficient resource utilization, and sustainable environmental development.