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This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. [ABSTRACT FROM AUTHOR]

This paper studies a dynamic air ticket pricing problem in a strategic and myopic passengers co-existence market. The strategic or myopic passengers can be further divided into high-valuation and low-valuation groups according to how they evaluate their purchases. The strategic passengers have different strategic levels. When the airline sets a ticket price, every passenger makes his or her purchase decision according to his or her type and the strategic level, or might select "wait" or "leave (the market)". The paper firstly proposes a dynamic pricing algorithm in which the utilities of both the airline and passengers are considered. The reinforcement learning (RL) is employed to deal with the progressive or dynamic decision-making framework, in which the dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP) and the Q-learning is adopted to solve the problem. By using this method, the airline can adaptively decide the ticket price based on passengers strategic behaviors and the time-varying demand. The effects of the passenger type proportion and strategic level are analyzed. The computational results show the higher proportion of strategic passengers is, the smaller price increase the airline can adopt, and the higher proportion of high-valuation strategic passengers is, the larger price increase the airline can put to use under the same strategic level. If the proportion of low-valuation strategic passengers is higher, the price increase should be gentle and step by step when the price increase strategy is adopted. If the airline uses price-cut policy, the adjustment should be small. In addition, the high-valuation passenger mainly affects high-price periods and the low-valuation passenger mainly affects low-price periods. When the proportion of strategic passengers is fixed, the lower the passenger strategic level is, the larger the price slope is. These findings can provide some references for the airline to make more precise and flexible pricing decisions. [ABSTRACT FROM AUTHOR]

The dynamic joint pricing and seat inventory control is more practical but complicated in both formulation and solving. This paper presents a three-stage decision approach (TSDA) to attack this problem. In the first stage, the relationship between dynamic prices and their pre-sale periods is built. The game process between passengers and the airline based on maximisation of both passenger utility and airline's network revenue is applied. Passenger's booking and cancellation processes are simulated according to respective distributions. In the second stage, the seat allocation model for different itineraries is built based on presented unified prices. It greatly decreases computation complexity since prices, itinerary legs and pre-sale time periods are excluded from combination. In the third stage, itinerary-based revenue management model is built with embedding the nested control strategy inside. Meanwhile, some practical factors such as cancellation and no-show are considered. The experimental computation results show TSDA is effective. [ABSTRACT FROM AUTHOR]

In order to improve the service level of small and medium-sized airlines, enhance its flexibility to cope with extra flights, the article designs a new crew pairing solutions for extra flights. Based on the traditional model of the crew pairing, meeting the requirements of crew pairing for extra flights, we propose robust crew pairing model. Then through the example analysis, we used CPLEX software to solve the model, obtained the optimal solutions. Finally, through the comparison of different results, revealing the advantage of robust modeling method and showing that the proposed robust models provide natural options to recovery without disrupting the existing flights at a relatively small incremental cost, and providing meaningful reference for the small and medium-sized airline in the problem of coping with extra flights. [ABSTRACT FROM AUTHOR]