Abstract-In this paper, we detail a method for calculating the cost of delays to an airline. The approach extends a EU report that calculated delays for three alternative scenarios (low cost, baseline costs and high costs) and for short delays (under 15 minutes) and long delays (over 65 minutes). Our extension to this report determines the factors that make up the multipliers presented in that report. We next apply the individual cost factor delays to US data. The approach allows one to update the cost whenever any of the factors (crew, fuel, maintenance, ground costs) change. It considers the size of the aircraft when making such calculations, both from the perspective of fuel burn and passenger costs. Our validation methodology evaluates how close our data come to that presented in their report when a conversion is made from dollars to Euros and applies 2003 cost data. Data for Philadelphia airport (PHL) is displayed as a case study to show current delay costs.

Industry strategists, government regulators, and the media have focused on addressing concerns over the performance of the air transportation system with respect to delays. One of the strategies proposed has been to limit the scheduled operations at an airport to a priori feasible capacity limits. This approach has been criticized on the basis that it would reduce the number of markets served and increase airfares. This paper describes a comparison of the behavior of the of the air transportation system (e.g. markets, economics, and performance) during the recent run-up in fuel prices at slot-controlled New York airports and non-slot controlled San Francisco airports. The results indicate that slot controlled airports yielded improved performance (e.g. less delayed and cancelled flights) through reductions in frequency and de-peaking schedules. There was no significant change in markets service. However, there is a marked decrease in the number of markets served by two or more airlines.On the other hand, the non-slot controlled airports in the San Francisco region showed an increase in flight delays and cancellations during the same period. The number of market served and airfares did not change. These results provide some justification for slot-controls at airports that need to manage network congestion. Even in the presence of fluctuations in passenger demand and economic shocks, passengers saw an improvement in service (i.e. a lessening of delays and cancellation) without any significant loss in markets served or frequency to those markets. The only significant reduction in frequency was to the Washington DC, Chicago and Boston markets where there was decrease in frequency and an upguaging of aircraft to accommodate the passenger demand.

Purpose The purpose of this paper is to introduce an efficient algorithm based on a non-linear accepting threshold to solve the redundancy allocation problem (RAP) considering multiple redundancy strategies. In addition to the components reliability, multiple redundancy strategies are simultaneously considered to vary the reliability of the system. The goal is to determine the optimal selection of elements, redundancy levels and redundancy strategy, which maximizes the system reliability under various system-level constraints. Design/methodology/approach The mixed RAP considering the use of active and standby components at the subsystem level belongs to the class of NP-hard problems involving selection of elements and redundancy levels, to maximize a specific system performance under a given set of physical and budget constraints. Generally, the authors recourse to meta-heuristic algorithms to solve this type of optimization problem in a reasonable computational time, especially for large-size problems. A non-linear threshold accepting algorithm (NTAA) is developed to solve the tackled optimization problem. Numerical results for test problems from previous research are reported and analyzed to assess the efficiency of the proposed algorithm. Findings The comparison with the best solutions obtained in previous studies, namely: genetic algorithm, simulated annealing, memetic algorithm and the particle swarm optimization for 33 different instances of the problem, demonstrated the superiority of the proposed algorithm in finding for all considered instances, a high-quality solution in a minimum computational time. Research limitations/implications Considering multiple redundancy strategies helps to achieve higher reliability levels but increases the complexity of the obtained solution leading to infeasible systems in term of physical design. Technological constraints must be integrated into the model to provide a more comprehensive and realistic approach. Practical implications Designing high performant systems which meet customer requirements, under different economic and functional constraints is the main challenge faced by the manufacturers. The proposed algorithm aims to provide a superior solution of the reliability optimization problem by considering the possibility to adopt multiple redundancy strategies at the subsystem level in a minimum computational time. Originality/value A NTAA is expanded to the RAP considering multiple redundancy strategies at the subsystem level subject to weight and cost constraints. A procedure based on a penalized objective function is developed to encourage the algorithm to explore toward the feasible solutions area. By outperforming well-known solving technique, the NTAA provides a powerful tool to reliability designers of complex systems where different varieties of redundancies can be considered to achieve high-reliability systems.

Previous studies of the US Air TransportationSystem have tried to identify rational airline behavior during times of significant economic and regulatory change [1]. That research indicated that even during periods of increased fuel prices and slot controls at the New York Airports, the airlines chose to reduce the size of the aircraft rather than reduce schedule and increase aircraft size [2]. This study uses delaycost modeling to explain such behavior. This paper extends our previous analysis of airline delay costs [3] by applying that methodology to new data and examining the sensitivity of the results to such data changes. We examine the sensitivity of airline delay costs to aircraft fuel burn rates, fuel prices, crew and maintenance costs, and airline market shares. We observe that delay costs are most sensitive to fuel burn rates. We then identify the aircraft that is -best in class‖ and find that the current airline behavior of moving to smaller, more efficient aircraft makes good economic sense because it increases frequency while simultaneously reducing the two highest operational costs: fuel costs and crew costs. This finding has significant impact for those responsible for managing congestion in the airspace and at airports.

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