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Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines. [ABSTRACT FROM AUTHOR]

As the airline industry is ever expanding, companies are increasing their fleet sizes to obtain greater market shares. Moreover, as the airlines seek more growth, the complexity and size of the airline crew scheduling problem, which is one of the major planning problems in the industry, is also increasing. For this reason, companies dedicate resources to solve this problem, and lease software at great expense from external sources. Rigorous mathematical models and algorithms are used in solving these problems. This paper presents a survey of airline crew scheduling problems, and their proposed solutions from the literature. As a conclusion, prospective studies will be proposed and discussed with the aim of developing better solutions for airline crew scheduling problems in the future. [ABSTRACT FROM AUTHOR]

In this paper, we consider a coordinated multi-aircraft 4D (3D space plus time) trajectories planning problem which is illustrated by planning 4D trajectories for aircraft traversing an Air Traffic Control (ATC) sector. The planned 4D trajectories need to specify each aircraft’s position at any time, ensuring conflict-free and reducing fuel and delay costs, with possible aircraft maneuvers such as speed adjustment and flight level change. Different from most existing literature, the impact of buffer safety distance is also under consideration, and conflict-free is guaranteed at any given time (not only at discrete time instances). The problem is formulated as a pure-strategy game with aircraft as players and all possible 4D trajectories as strategies. An efficient maximum improvement distributed algorithm is developed to find equilibrium at which every aircraft cannot unilaterally improve further, without enumerating all possible 4D trajectories in advance. Proof of existence of the equilibrium and convergence of the algorithm are given. A case study based on real air traffic data shows that the algorithm is able to solve 4D trajectories for online application with estimated 16.7% reduction in monetary costs, and allocate abundant buffer safety distance at minimum separation point. Scalability of the algorithm is verified by computational experiments. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

Aircraft emissions are the main cause of airport air pollution. One of the keys to achieving airport energy conservation and emission reduction is to optimize aircraft taxiing paths. The traditional optimization method based on the shortest taxi time is to model the aircraft under the assumption of uniform speed taxiing. Although it is easy to solve, it does not take into account the change of the velocity profile when the aircraft turns. In view of this, this paper comprehensively considered the aircraft's taxiing distance, the number of large steering times and collision avoidance in the taxi, and established a path optimization model for aircraft taxiing at airport surface with the shortest total taxi time as the target. The genetic algorithm was used to solve the model. The experimental results show that the total fuel consumption and emissions of the aircraft are reduced by 35% and 46%, respectively, before optimization, and the taxi time is greatly reduced, which effectively avoids the taxiing conflict and reduces the pollutant emissions during the taxiing phase. Compared with traditional optimization methods that do not consider turning factors, energy saving and emission reduction effects are more significant. The proposed method is faster than other complex algorithms considering multiple factors, and has higher practical application value. It is expected to be applied in the more accurate airport surface real-time running trajectory optimization in the future. Future research will increase the actual interference factors of the airport, comprehensively analyze the actual situation of the airport's inbound and outbound flights, dynamically adjust the taxiing path of the aircraft and maintain the real-time performance of the system, and further optimize the algorithm to improve the performance of the algorithm. [ABSTRACT FROM AUTHOR]