A model and optimization-based heuristic for the operational aircraft maintenance routing problem

This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.     

Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem

In this paper, we study two closely related airline planning problems: the robust weekly aircraft maintenance routing problem (RWAMRP) and the tail assignment problem (TAP). In real life operations, the RWAMRP solution is used in tactical planning whereas the TAP solution is implemented in operational planning. The main objective of these two problems is to minimize the total expected propagated delay (EPD) of the aircraft routes. To formulate the RWAMRP, we propose a novel weekly line-of-flights (LOF) network model that can handle complex and nonlinear cost functions of EPD. Because the number of LOFs grows exponentially with the number of flights to be scheduled, we propose a two-stage column generation approach to efficiently solve large-scale real-life RWAMRPs. Because the EPD of an LOF is highly nonlinear and can be very time-consuming to accurately compute, we propose three lower bounds on the EPD to solve the pricing subproblem of the column generation. Our approach is tested on eight real-life test instances. The computational results show that the proposed approach provides very tight LP relaxation (within 0.6% of optimal solutions) and solves the test case with more than 6000 flights per week in less than three hours. We also investigate the solutions obtained by our approach over 500 simulated realizations. The simulation results demonstrate that, in all eight test instances, our solutions result in less EPDs than those obtained from traditional methods. We then extend our model and solution approach to solve realistically simulated TAP instances.     

Dynamic yield management when aircraft assignments are subject to swap

As a practical form of demand driven dispatch at some major airlines in North America, cockpit compatible aircraft of different capacities are paired in the fleet assignment for a possible future swap on the two involved flights. They are paired in such a way that the swap does not affect their aircraft routings on other legs. The swap decision depends on demand realization on the two flights and is made at a predetermined time prior to departure. Yield management on the two flights is studied in this paper. We begin by studying a base problem in which at a certain time before departure, the assignment on a flight is subject to change with a fixed probability. The base problem extends the threshold policy into the case where future capacity is uncertain. Secondly, we propose a heuristic for yield management over two flights with swappable aircraft by repeatedly updating the swap probability as demand unfolds. Our numerical result shows that this policy significantly enhances the airline’s capability to increase revenue under demand driven dispatch. In addition, the base problem may shed lights on derivation of optimal yield management policy in irregular operational settings where final capacity assignment is independent of yield management policy.     

A math-heuristic algorithm for the integrated air service recovery

A sophisticated flight schedule might be easily disrupted due to adverse weather, aircraft mechanical failures, crew absences, etc. Airlines incur huge costs stemming from such flight schedule disruptions in addition to the serious inconveniences experienced by passengers. Therefore, an efficient recovery solution that simultaneously decreases an airline's recovery cost while simultaneously mitigating passenger dissatisfaction is of great importance to the airline industry. In this paper, we study the integrated airline service recovery problem in which the aircraft and passenger schedule recovery problems are simultaneously addressed, with the objective of minimizing aircraft recovery and operating costs, passenger itinerary delay cost, and passenger itinerary cancellation cost.Recognizing the inherent difficulty in modeling the integrated airline service recovery problem within a single formulation (due to its huge solution space and quick response requirement), we propose a three-stage sequential math-heuristic framework to efficiently solve this problem, wherein the flight schedules and aircraft rotations are recovered in the first stage, Then, a flight rescheduling problem and passenger schedule recovery problems are iteratively solved in the next two stages. Time-space network flow representations, along with mixed-integer programming formulations, and algorithms that take advantages of the underlying problem structures, are proposed for each of three stages. This algorithm was tested on realistic data provided by the ROADEF 2009 challenge and the computational results reveal that our algorithm generated the best solution in nearly 72% of the test instances, and a near-optimal solution was achieved in the remaining instances within an acceptable timeframe. Furthermore, we also ran additional computational runs to explore the underlying characteristics of the proposed algorithm, and the recorded insights can serve as a useful guide during practical implementations of this algorithm.     

Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.     

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

Abstract

This paper presents a novel application of a Method of Inequality-based Multi-objective Genetic Algorithm (MMGA) to generate an efficient time-effective multi-fleet aircraft routing algorithm in response to the schedule disruption of short-haul flights. It attempts to optimize objective functions involving ground turn-around times, flight connections, flight swaps, total flight delay time and a 30-minute maximum delay time of original schedules. The MMGA approach, which combines a traditional Genetic Algorithm (GA) with a multi-objective optimization method, can address multiple objectives at the same time, then explore the optimal solution. The airline schedule disruption management problem is traditionally solved by Operations Research (OR) techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model difficult to define. Experimental results based on a real airline flight schedule demonstrate that the proposed method, Multi-objective Optimization Airline Disruption Management by GA, can recover the perturbation efficiently within a very short time. Our results further demonstrate that the application can yield high quality solutions quickly and, consequently, has potential to be employed as a real-time decision support tool for practical complex airline operations.     

Monitoring Aircraft Turnaround Operations – Framework Development,Application and Implications for Airline Operations

Abstract

A real-time operation monitoring system – Aircraft Turnaround Monitoring System – is developed based on a system framework to monitor aircraft turnaround operations at an airport. Mobile computing devices (PDAs) and wireless network technology General Packet Radio Service (GPRS) are used to implement the real-time monitoring system for an airline. System implementation and test results indicate that real-time operation monitoring can potentially reduce delays occurring from airline operations. Proactive measures can be taken immediately by ground handling staff to reduce delays, once the risk of delays and potential delay propagation is identified. The availability of detailed operating data can help airlines identify the root delay causes from complex connections among aircraft, flight/cabin crew and passengers. In addition, these operating data also shed some light on the future development of aircraft routing algorithms in order to consider explicitly stochastic disruptions and delay propagation in airline schedule planning.     

Time to burn: Flight delay,terminal efficiency,and fuel consumption in the National Airspace System

Improved Air Traffic Management (ATM) leading to reduced en route and gate delay, greater predictability in flight planning, and reduced terminal inefficiencies has a role to play in reducing aviation fuel consumption. Air navigation service providers are working to quantify this role to help prioritize and justify ATM modernization efforts. In the following study we analyze actual flight-level fuel consumption data reported by a major U.S. based airline to study the possible fuel savings from ATM improvements that allow flights to better adhere to their planned trajectories both en route and in the terminal area. To do so we isolate the contribution of airborne delay, departure delay, excess planned flight time, and terminal area inefficiencies on fuel consumption using econometric techniques. The model results indicate that, for two commonly operated aircraft types, the system-wide averages of flight fuel consumption attributed to ATM delay and terminal inefficiencies are 1.0–1.5% and 1.5–4.5%, respectively. We quantify the fuel impact of predicted delay to be 10–20% that of unanticipated delay, reinforcing the role of flight plan predictability in reducing fuel consumption. We rank terminal areas by quantifying a Terminal Inefficiency metric based on the variation in terminal area fuel consumed across flights. Our results help prioritize ATM modernization investments by quantifying the trade-offs in planned and unplanned delays and identifying terminal areas with high potential for improvement.     

Flight schedule punctuality control and management: a stochastic approach

The insufficiency of infrastructure capacity in an air transport system is usually blamed for poor punctuality performance when implementing flight schedules. However, investigations have revealed that ground operations of airlines have become the second major cause of flight delay at airports. A stochastic approach is used in this paper to model the operation of aircraft turnaround and the departure punctuality of a turnaround aircraft at an airport. The aircraft turnaround model is then used to investigate the punctuality problem of turnaround aircraft. Model results reveal that the departure punctuality of a turnaround aircraft is influenced by the length of scheduled turnaround time, the arrival punctuality of inbound aircraft as well as the operational efficiency of aircraft ground services. The aircraft turnaround model proposed is then employed to evaluate the endogenous schedule punctuality of two turnaround aircraft. Model results, when compared with observation data, show that the operational efficiency of aircraft ground services varies among turnarounds. Hence, it is recommended that the improvement of departure punctuality of turnaround aircraft may be achieved from two approaches: airline scheduling control and the management of operational efficiency of aircraft ground services.     

A bi-objective dynamic programming approach for airline green fleet planning

Ensuring a fleet of green aircraft is a basic step in mitigating aviation pollution issues that are expected to be worsen in the coming years due to rapid air traffic growth. This study proposed a novel methodology in green fleet planning in which both profit and green performance of airline are considered simultaneously and explicitly. To do this, a Green Fleet Index (GFI) is derived as an indicator to quantify the green performance of airline’s fleet. It measures the degree of airline compliance with a standard requirement in terms of emission, noise, and fuel consumption. A bi-objective dynamic programming model is then formulated to find optimal aircraft acquisition (lease or purchase) decision by minimizing GFI and maximizing profit. Several interesting results are obtained: (1) considering environmental issue as secondary objective yields a greener fleet; (2) airline’s profit is affected, but could be recovered from environmental cost savings; (3) increasing load factor is an effective operational improvement strategy to enhance airline’s green performance and raise profit level. It is anticipated that the framework developed in this study could assist airlines to make a smart decision when considering the need to be green.     

The fleet size and mix dial-a-ride problem with reconfigurable vehicle capacity

This paper introduces a fleet size and mix dial-a-ride problem with multiple passenger types and a heterogeneous fleet of reconfigurable vehicles. In this new variant of the dial-a-ride problem, en-route modifications of the vehicle’s inner configuration are allowed. The main consequence is that the vehicle capacity is defined by a set of configurations and the choice of vehicle configuration is associated with binary decision variables.The problem is modeled as a mixed-integer program derived from the model of the heterogeneous dial-a-ride problem. Vehicle reconfiguration is a lever to efficiently reduce transportation costs, but the number of passengers and vehicle fleet setting make this problem intractable for exact solution methods. A large neighborhood search metaheuristic combined with a set covering component with a reactive mechanism to automatically adjust its parameters is therefore proposed. The resulting framework is evaluated against benchmarks from the literature, used for similar routing problems. It is also applied to a real case, in the context of the transportation of disabled children from their home to medical centers in the city of Lyon, France.     

An optimization model for aircraft maintenance scheduling and re-assignment

The aircraft maintenance scheduling is one among the major decisions an airline has to make during its operation. Though maintenance scheduling comes as an end stage in an airline operation, it has potential for cost savings. Maintenance scheduling is an easily understood but difficult to solve problem. Given a flight schedule with aircraft assigned to it, the aircraft maintenance-scheduling problem is to determine which aircraft should fly which segment and when and where each aircraft should undergo different levels of maintenance check required by the Federal Aviation Administration. The objective is to minimize the maintenance cost and any costs incurred during the re-assignment of aircraft to the flight segments.This paper provides a complete formulation for maintenance scheduling and a heuristic approach to solve the problem. The heuristic procedure provides good solutions in reasonable computation time. This model can be used by mid-sized airline corporations to optimize their maintenance costs.     

Tarmac delay policies: A passenger-centric analysis

In this paper, we analyze the effectiveness of the 2010 Tarmac Delay Rule from a passenger-centric point of view. The Tarmac Delay Rule stipulates that aircraft lift-off, or an opportunity for passengers to deplane, must occur no later than 3 h after the cabin door closure at the gate of the departure airport; and that an opportunity for passengers to deplane must occur no later than 3 h after the touchdown at the arrival airport. The Tarmac Delay Rule aims to protect enplaned passengers on commercial aircraft from excessively long delays on the tarmac upon taxi-out or taxi-in, and monetarily penalizes airlines that violate the stipulated 3-h tarmac time limit. Comparing the actual flight schedule and delay data after the Tarmac Delay Rule was in effect with that before, we find that the Rule has been highly effective in reducing the frequency of occurrence of long tarmac times. However, another significant effect of the rule has been the rise in flight cancellation rates. Cancellations result in passengers requiring rebooking, and often lead to extensive delay in reaching their final destinations. Using an algorithm to estimate passenger delay, we quantify delays to passengers in 2007, before the Tarmac Delay Rule was enacted, and compare these delays to those estimated for hypothetical scenarios with the Tarmac Delay Rule in effect for that same year. Our delay estimates are calculated using U.S. Department of Transportation data from 2007. Through our results and several sensitivity analyses, we show that the overall impact of the current Tarmac Delay Rule is a significant increase in passenger delays, especially for passengers scheduled to travel on the flights which are at risk of long tarmac delays. We evaluate the impacts on passengers of a number of rule variations, including changes to the maximum time on the tarmac, and variations in that maximum by time-of-day. Through extensive scenario analyses, we conclude that a better balance between the conflicting objectives of reducing the frequency of long tarmac times and reducing total passenger delays can be achieved through a modified version of the existing rule. This modified version involves increasing the tarmac time limit to 3.5 h and only applying the rule to flights with planned departure times before 5pm. Finally, in order to implement the Rule more effectively, we suggest the tarmac time limit to be defined in terms of the time when the aircraft begin returning to the gate instead of being defined in terms of the time when passengers are allowed to deplane.     

Is it the labor unions’ fault? Dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States

Data envelopment analysis is used to evaluate the technical efficiencies of a number of major passenger airlines in the United States at transforming their inputs (labor, fuel and fleet-wide seating capacity) into available seat-miles. A tobit regression model is then used to identify the underlying drivers of airline efficiency, as measured by the data envelopment analysis efficiency score. The impact of unionization on airline efficiency is found to be statistically insignificant, controlling for the influences of other hypothesized determinants of airline efficiency: the average age of an airline’s fleet, the average size of its aircraft, its average stage length, the extent to which the airline relies of hubbing within its route structure, the percent of its passenger enplanements that are international, and whether the airline is a legacy carrier. The statistically significant drivers of airline efficiency, at a ten percent level of significance, are average aircraft size, average stage length and the extent to which the airline relies on hubbing and connecting flights within its route structure. The stage length variable is not significant at a five percent level of significance, however. An increase in average aircraft size or in average stage length enhances an airline’s efficiency whereas an increase in hubbing reduces it.     

The airline perturbation problem: considering disrupted passengers

Abstract

When airlines are faced with some unforeseen short-term events, they have to reconstruct their flight schedules. Although aircraft recovery decisions affect passengers, these disrupted passengers and recovering them have not been explicitly considered in most previous aircraft recovery models. This paper presents an assignment model for airline schedule recovery which recovers both aircraft and disrupted passengers simultaneously, using a rolling horizon time framework. Our model examines possible flight retiming, aircraft swapping, over-flying, ferrying, utilization of reserve aircraft, cancellation and passenger reassignment to generate an efficient schedule recovery plan. The model ensures that the schedule returns to normal within a certain time and the objective is to minimize operational recovery aircraft cost, cancellation and delay cost as well as disrupted passenger cost. The model is tested using a data-set with two disruption scenarios. The computational results show that it is capable of handling the integrated aircraft and passenger recovery problem successfully.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

Airline flight scheduling for oligopolistic competition with direct flights and a point to point network

In this research, we consider a flight scheduling problem for oligopolistic competition with direct flights and a point to point network. In this type of market situation, passengers are sensitive to the departure time of a flight rather than the transfer time. The airline needs to carefully consider the departure times of their competitors when determining their own. Therefore, unlike past approaches which have only considered one departure time for a competitor's flight, a flight scheduling framework is developed which takes into consideration possible competitor departure times. The framework includes two dependent stages which are repeatedly solved during the solution process. In addition, an upper bound model is also designed to evaluate the solution quality. Numerical tests are performed using data for Taiwan's outlying island route which is characterized by the above market situation. Satisfactory results are obtained, showing the good performance of the framework. Copyright © 2017 John Wiley & Sons, Ltd.     

Heuristic search for the coupled runway sequencing and taxiway routing problem

This paper presents the first local search heuristic for the coupled runway sequencing (arrival & departure) and taxiway routing problems, based on the receding horizon (RH) scheme that takes into account the dynamic nature of the problem. As test case, we use Manchester Airport, the third busiest airport in the UK. From the ground movement perspective, the airport layout requires that departing aircraft taxi across the arrivals runway. This makes it impossible to separate arrival from departure sequencing in practice. Operationally, interactions between aircraft on the taxiways could prevent aircraft from taking off from, or landing on, runways during the slots assigned to them by an algorithm optimizing runway use alone. We thus consider the interactions between arrival and departure aircraft on the airport surface. Compared to sequentially optimized solutions, the results obtained with our approach indicate a significant decrease in the taxiway routing delay, with generally no loss in performance in terms of the sequencing delay for a regular day of operations. Another benefit of such a simultaneous optimization approach is the possibility of holding aircraft at the stands for longer, without the engines running. This significantly reduces the fuel burn, as well as bottlenecks and traffic congestion during peak hours that are often the cause of flight delays due to the limited amount of airport surface space available. Given that the maximum computing time per horizon is around 95 s, real-time operation might be practical with increased computing power.     

A stochastic passenger loading model of airline schedule performance

The passenger loading problem is defined as that of determining the distribution of passengers that will be carried on each flight in a given market over the course of the day. This problem is stochastic in nature, and must take into account the fact that as some flights become booked up, passengers may spill to adjacent flights, or may choose not to fly on that airline. A precise model of the loading process is developed and solved using an efficient numerical procedure. Several approximations are introduced and tested which further improve the overall efficiency. The model represents a more rigorous solution approach than has appeared previously in the literature, and as such could be used to evaluate simpler approximations. It is fast enough, however, to be used as an interactive schedule evaluation and design tool.     

The impact of strategic management and fleet planning on airline efficiency - A random effects Tobit model based on DEA efficiency scores

As a result of the liberalisation of airline markets; the strong growth of low cost carriers; the high volatility in fuel prices; and the recent global financial crisis, the cost pressure that airlines face is very substantial. In order to survive in these very competitive environments, information on what factors impact on costs and efficiency of airlines is crucial in guiding strategic change. To evaluate key determinants of 58 passenger airlines’ efficiency, this paper applies a two-stage Data Envelopment Analysis (DEA) approach, with partially bootstrapped random effects Tobit regressions in the second stage. Our results suggest that the effects of route optimisation, in the sense of average stage length of the fleet, are limited to airline technical efficiency. We show that airline size and key fleet mix characteristics, such as aircraft size and number of different aircraft families in the fleet, are more relevant to successful cost management of airlines since they have significant impacts on all three types of airline efficiency: technical, allocative and, ultimately, cost efficiency. Our results also show that despite the fuel saving benefits of younger aircraft, the age of an airline’s fleet has no significant impact on its technical efficiency, but does have a positive impact on its allocative and cost efficiency.