为了尽可能减小施工对机场运营的影响，机场道面养护施工常采用关闭部分时段或部分道面的不停航施工方式。因此机场道面养护和修复有必要综合考虑质量、工期、安全等施工管理关键目标，开展科学有效的施工组织管理研究。提出一种基于多目标优化的机场道面不停航养护和修复活动优化模型。以机场道面养护施工管理中的工期目标为切入点，以施工工序为研究对象，分别建立成本、质量与施工时间之间的函数关系，实现了各目标的科学量化，并进一步采用施工窗口期约束构建了优化模型。模型结合了关闭夜间时段，保证了机场可在不停航状态下高效施工，并实现了工期、成本最小化，质量水平最大化的三目标优化。对比了多目标粒子群算法（multiple objective particle swarm optimization, MOPSO）、第Ⅱ代非支配排序遗传算法（nondominated sorting genetic algorithm-Ⅱ，NSGA-Ⅱ）和第Ⅲ代非支配排序遗传算法（NSGA-Ⅲ）在三维目标下的求解性能，结果发现，三种算法在解决凹面优化问题DTLZ2时均表现出了良好的性能，NSGA-Ⅲ解决三维优化问题时性能显著优于另外两者。经实例验证，本模型在满足施工约束的基础上实现了施工管理各目标的进一步优化，为不同需求的机场道面养护和修复活动管理提供决策依据。

In order to minimize the influence of construction for airport operation, the construction method of closing part of time or some pavement without stopping is usually used by the airport pavement maintenance office. Therefore, it is necessary to consider the key objectives of construction management, such as quality, duration and safety, and carry out a research on construction organization and management in the maintenance and repair of airport pavement. A multi-objective optimization model was proposed for the non-stop construction organization of airport pavements, focusing on the construction period within airport pavement maintenance management. The construction process was emphasized and functional relationships among cost, quality, and construction time were established to quantify each objective scientifically. Moreover, an optimization model was developed, incorporating the construction window period as a constraint and leveraging the closure of nighttime hours to facilitate uninterrupted construction without disrupting navigation. The approach ensures efficient non-stop construction of airport pavements. In the model, a triple objective optimization strategy was employed, aiming to minimize duration and cost while maximizing quality level. To solve the model, the performance of multiple objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm-Ⅱ（NSGA-II), and NSGA-Ⅲ algorithms was compared under the 3D objectives. The results demonstrate that all three algorithms show commendable performance in solving the concave optimization problem DTLZ2, with NSGA-Ⅲ notably outperforming the others in tackling the three-dimensional optimization problem. The capacity of the model to further optimize construction management objectives, while fully adhering to construction constraints, was verified, which provided a decision-making framework for construction organization and management in airport pavement maintenance, catering to diverse requirements.