in Terminal Manoeuvring Area (TMA) at hub airports is the main problem in Chinese air transportation system. At most of the hub airports, the capacity is near saturated or even overloaded. Civil Aviation Administration of China (CAAC) reported that Air Traffic Management (ATM) is the main cause of delays. Despite the already overloaded ATM system, the Chinese airplanes fleet is continuing to expand. China will become the largest traffic flow in the world before the end of 2035. There is an urgent need to develop a new more efficient method for sequencing and merging arrival flows in TMA, so that airports can maximise the benefits from the emerging Communication, Navigation and Surveillance (CNS) techniques, and consequently increasing capacity. Automation can be highly efficient in ATM, however, due to safety considerations, full automation in ATM is still a challenge. Facing extremely dense operations in complex TMA, we can consider reducing traffic complexity by solving all potential conflicts in advance with a feasible trajectory control for controllers, or automating a large proportion of routine operations, such as sequencing, merging and spacing. As parallel runways are a common structure of Chinese hub airports, in this thesis, we propose a novel system to integrated sequencing and merging aircraft to parallel runways. Our methodology integrates a Area Navigation (RNAV)-based 3D Multi-Level and Multi-Point Merge System (MLMPMS), a hybrid heuristic optimization algorithm and a simulation module to find good, systematic, operationally-acceptable solutions. First, a Receding Horizon Control (RHC) technique is applied to divide 24-hour traffic optimization problem into several sub-problems. Then, in each sub-problem, a tailored Simulated Annealing (SA) algorithm and a trajectory generation module worn together to find a near-optimal solution. Our primary objective is to rapidly generate conflict-free and economical trajectories with easy, flexible and feasible control methods. Based on an initial solution, we continuously explore possible good solutions with less delay and shorter landing interval on runway. Taking Beijing Capital International Airport (BCIA) as a case to study, numerical results show that our optimization system performs well. First, it has very stable de-conflict performance to handle continuously dense traffic flows. Compared with Hill Climbing (HC), the tailored SA algorithm can always guarantee a conflict-free solution not only for the mixed iii or segregated parallel approach (arrivals only) pattern, but also for the independent parallel operation (integrated departures and arrivals) pattern. Second, with its unique Multi-Level Point Merge (ML-PM) route network, it can provide a good trajectory control solution to efficiently and economically handle different kinds of arrival flows. It can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, compared with baseline. For normal traffic, with near-equal traffic demand for two runways, with landing balancing function ON, the average flying time on different Standard Terminal ARrival (STAR) routes can be reduced up to 8 minutes compared with the baseline. It also realizes an easier re-sequencing of aircraft with more relaxed position shifting as well, compared with conventional sequencing method. Theoretically, the Maximum Position Shifting (MPS) can be up to 6 positions, overcoming the hard constraint of 3 position shifts (MPS <= 3). For asymmetric traffic, with big difference on traffic demand for two runways, with runway balancing function ON, it is more likely to find a conflict-free solution compared with the runway balancing function OFF, and again reduces the average flying time. Third, it is efficient for the segregated parallel approach patterns. Compared with hard constrained position shifting, which is often used in current Arrival Manager (AMAN) system and controller’s manual-control First Come First Served (FCFS) method, it can reduce the average delay, average additional transit time in super dense arrival situations. The average time flown level per flight is less than 12% of total transit time in TMA. Fourth, in independent parallel patterns, it can provide a range of useful information concerning the associated objective value, the average flying time, crossing trajectories in hot spots between arrivals and departures, the efficiency of using different designed sequencing legs in ML-PM route network. Thus, it helps the Air Navigation Service Provider (ANSP) to find the best configuration of ML-PM route network to efficiently satisfy the traffic demand. Last but not least, the computation time of our system is reasonable. It generally needs around 290s-350s for 2 hours of heavy traffic demand with the mixed parallel approach. In conclusion, theoretically, our system realizes good trajectory planning of dense flows at busy airports. It can guarantee a conflict-free solution, increase runway throughput, and minimize delay. At the same time, it can simplify merging, re-sequencing, and improve the economical descent profile with advanced ML-PM route design. Although the methodology defined here is illustrated using the BCIA airport, it could be easily applied to airports worldwide.