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.     

Evaluation and implications of environmental charges on commercial flights

Environmental charges are one of the economic instruments for controlling externalities. Their application to commercial flights has become a preferred method of encouraging the sustainable development of the air transport industry. Two kinds of externalities, aircraft noise and engine emissions, both generating profound impacts on human beings and on the environment, are considered here. The hedonic price method is applied to calculate the social cost of aircraft noise during the landing and take-off stages of the flight. The marginal impact of each flight with specific aircraft/engine combinations is derived for the allocation of aggregate noise social costs. In contrast, the dose - response method is applied to estimate the social cost of each engine exhaust pollutant during different flight modes. The combination of aircraft noise and engine emissions social costs is then evaluated on the basis of several environmental charge mechanism scenarios, using Amsterdam Airport Schiphol as a case study. It is shown that the current noise or engine emissions related charges at airports are lower than the actual social costs of their respective externalities. The implications of charge mechanism scenarios are subsequently discussed and evaluated in terms of their impacts on airline costs, airfares and passenger demand.     

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.     

Assigning fighter plane formations to enemy aircraft using fuzzy logic

Fighter aircraft protect specific facilities on alert in the air by patrolling expectation zones. These zones are located in the direction from which enemy aircraft attacks are expected; fighter formations are sent from them to intercept enemy aircraft. The problem considered in this paper is to determine the optimum assignment of fighter plane formations to enemy formations. The proposed solution is based on fuzzy logic and integer linear programming. A numerical example is given to illustrate the application possibilities of the proposed solution.     

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.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.     

A hybrid optimization-simulation approach for robust weekly aircraft routing and retiming

We address the robust weekly aircraft routing and retiming problem, which requires determining weekly schedules for a heterogeneous fleet that maximizes the aircraft on-time performance, minimizes the total delay, and minimizes the number of delayed passengers. The fleet is required to serve a set of flights having known departure time windows while satisfying maintenance constraints. All flights are subject to random delays that may propagate through the network. We propose to solve this problem using a hybrid optimization-simulation approach based on a novel mixed-integer nonlinear programming model for the robust weekly aircraft maintenance routing problem. For this model, we provide an equivalent mixed-integer linear programming formulation that can be solved using a commercial solver. Furthermore, we describe a Monte-Carlo-based procedure for sequentially adjusting the flight departure times. We perform an extensive computational study using instances obtained from a major international airline, having up to 3387 flights and 164 aircraft, which demonstrates the efficacy of the proposed approach. Using the simulation software SimAir to assess the robustness of the solutions produced by our approach in comparison with that for the original solutions implemented by the airline, we found that on-time performance was improved by 9.8–16.0%, cumulative delay was reduced by 25.4–33.1%, and the number of delayed passengers was reduced by 8.2–51.6%.     

Aircraft initial mass estimation using Bayesian inference method

Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass.     

A flexible terminal/mobile lounge system for expanding the capacity of existing airports

One of the great dilemmas facing major airports is the problem of capacity and seasonal surges of activity. This paper suggests a system of small, inexpensive Airport Terminal Modules, which together with a new type of Mobile Lounge, could make available a considerable degree of flexibility. The ATM's are designed to allow maximum variations of aircraft type and passenger load as well as high ground utilization. This concept would allow major airports to be extended with a minimum of inconvenience as demand becomes apparent, thereby allowing financial expenditure to be carefully controlled and more evenly spread.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Heuristic approach to the airline schedule disturbances problem

Abstract

When disturbances make it impossible to realise the planned flight schedule, the dispatcher at the airline operational centre defines a new flight schedule based on airline policy, in order to reduce the negative effects of these perturbations. Depending on airline policy, when designing the new flight schedule, the dispatcher delays or cancels some flights and reassigns some flights to available aircraft. In this paper, a decision support system (DSS) for solving the airline schedule disturbances problem is developed aiming to assist decision makers in handling disturbances in real-time. The system is based on a heuristic algorithm, which generates a list of different feasible schedules ordered according to the value of an objective function. The dispatcher can thus select and implement one of them. In this paper, the possibilities of DSS are illustrated by real numerical examples that concern JAT Airways' flight schedule disturbances.     

Integrated transit services for minimum cost operation considering heterogeneous demand

Abstract

In large metropolitan areas, public transit is a major mode choice of commuters for their daily travel, which has an important role in relieving congestion on transportation corridors. The purpose of this study is to develop a model which optimizes service patterns (SPs) and frequencies that yield minimum cost transit operation. Considering a general transit route with given stops and origin-destination demand, the proposed model consists of an objective total cost function and a set of constraints to ensure frequency conservation and sufficient capacity subject to operable fleet size. A numerical example is provided to demonstrate the effectiveness of the developed model, in which the demand and facility data of a rail transit route were given. Results show that the proposed model can be applied to optimize integrated SPs and headways that significantly reduce the total cost, while the resulting performance indicators are generated.     

Modeling Car Ownership in Turkey Using Fuzzy Regression

Abstract

This article develops a model which can be used to determine car ownership in Turkey. Because of the lack of disaggregated data, the model is based on aggregated data. As owning a car is mainly affected by economic, social and demographic factors, the car ownership model has a multi-variable form. In order to explain the effects of these factors on car ownership in Turkey, a fuzzy multiple-regression model is used. The major reason for applying fuzzy regression is to overcome the intercorrelation problem associated with the independent variables. In this study, the urbanization rate, average family size, gross national product per capita, average car cost, gasoline price and total length of roads are selected as independent variables. The results show that, by applying a multi-variable approach to possibilistic regression, the model provides not only a crisp output but also an output range for car ownership in Turkey between 1970 and 2000.     

Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment

Abstract

This paper investigates a transportation scheduling problem in large-scale construction projects under a fuzzy random environment. The problem is formulated as a fuzzy, random multi-objective bilevel optimization model where the construction company decides the transportation quantities from every source to every destination according to the criterion of minimizing total transportation cost and transportation time on the upper level, while the transportation agencies choose their transportation routes such that the total travel cost is minimized on the lower level. Specifically, we model both travel time and travel cost as triangular fuzzy random variables. Then the multi-objective bilevel adaptive particle swarm optimization algorithm is proposed to solve the model. Finally, a case study of transportation scheduling for the Shuibuya Hydropower Project in China is used as a real world example to demonstrate the practicality and efficiency of the optimization model and algorithm.     

Cargo Container Loading Plan Model and Solution Method for International Air Express Carriers

Abstract

In this article, a cargo container loading plan model is developed based on the operations of FedEx, the international air express carrier. The objective is to minimize total container handling cost, subject to related operating constraints. The model is expected to be a useful planning tool whereby international air express carriers such as FedEx can decide on container loading plans that will lead to lower operating costs, thus enhancing profits and market competitiveness. The model is formulated as a non-linear mixed integer program that is characterized as NP-hard. A solution method is then developed, with the use of the mathematical programming solver, CPLEX, to solve the problem efficiently. To evaluate the model and the solution method, we perform a case study using data from FedEx. The preliminary results indicate that the model and the solution method are both efficient and effective.     

Reducing the climate change impacts of aviation by restricting cruise altitudes

Two of the ways in which air travel affects climate are the emission of carbon dioxide and the creation of high-altitude contrails. One possible impact reduction strategy is to significantly reduce the formation of contrails. This could be achieved by limiting the cruise altitude of aircraft. If implemented, this could severely constrain air space capacity, especially in parts of Europe. In addition, carbon emissions would likely be higher due to less efficient aircraft operation at lower cruise altitudes. This paper describes an analysis of these trade-offs using an air space simulation model as applied to European airspace. The model simulates the flight paths and altitudes of each aircraft and is here used to calculate emissions of carbon dioxide and changes in the journey time. For a one-day Western European traffic sample, calculations suggest annual mean CO₂ emissions would increase by only 4% if cruise altitudes were restricted to prevent contrail formation. The change in journey time depended on aircraft type and route, but average changes were less than 1 min. Our analysis demonstrates that altitude restrictions on commercial aircraft could be an effective means of reducing climate change impacts, though it will be necessary to mitigate the increased controller workload conflicts that this will generate.     

Scheduling of airport runway operations using stochastic branch and bound methods

In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5–7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1 min of computation time.     

An assessment of the demand for new transport technology in Australia

This paper summarizes the constraints which a technological innovation in transport must satisfy if it is to achieve widespread application. The existing and likely future travel demands in Australia are reviewed, and for certain types of travel it is concluded that existing modes may become unsuitable or unacceptable. In these areas, it is possible that a new transport technology may find application. In all other areas, it is considered that existing modes, or their functional successors, will continue.

Five possible demand areas in which new transport technology may find application are identified. These are as follows:

1) High speed distribution systems may be introduced in Australian CBD's (Central Business Districts) within the next ten years. These may be progressively extended to provide radial public transport services to the CBD. It is also possible that these systems may eventually be extended to provide a dual‐mode capability in inner‐suburban areas as a partial alternative to freeway construction.

2) New town developments which may be introduced in Australia could include various forms of advanced transportation systems.

3) New aviation modes, possibly including short take off and landing aircraft operating from suburban airports may be introduced to cater for inter‐urban travel.

4) A demand‐responsive transport system to cater for the needs of the transport poor in urban areas may find limited application.

5) In the longer term, possibly by the turn of the century, an improved transport system capable of providing door‐to‐door service over the whole of an urban area and operating under a dual‐mode configuration appears to be possible. It probably would emerge from development of an incremental nature and eventually include the installation of a new technology in the then existing freeway and railway reservations in the city.     

Genetic algorithm-based simulation optimization of the ALINEA ramp metering system: a case study in Atlanta

ABSTRACT

This paper presents a case study of the optimal ALINEA ramp metering system model of a corridor of the metro Atlanta freeway. Based on real-world traffic data, this study estimates the origin-destination matrix for the corridor. Using a stochastic simulation-based optimization framework that combines a micro-simulation model and a genetic algorithm-based optimization module, we determine the optimal parameter values of a combined ALINEA ramp metering system with a queue flush system that minimizes total vehicle travel time. We found that the performance of ramp metering with optimized parameters, which is very sensitive possibly because bottlenecks are correlated, outperforms the no control model with its optimized parameters in terms of reducing total travel time.