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.     

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.     

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.     

Effectiveness of roadside crash countermeasures

In this article, an "intelligent" airline seat inventory control system is developed. Applications of the system are considered for both nonstop flights and flights with stopovers. The system developed is able to recognize a situation characterized by the number of reservations made by individual passenger classes and the number of canceled reservations at a certain moment in time before departure. The system can also make the appropriate decision without knowing the functional relationships in effect between individual variables. As in other intelligent systems, the "intelligent" airline seat inventory control system proposed here is able to generalize, adapt and learn based on new knowledge and new information. The "intelligent" airline seat inventory control system is based on fuzzy logic. The system makes on-line decisions as to whether to accept or reject any passenger request using established fuzzy rules. The "intelligent" system's results are compared with those of the EMSR model for nonstop flights. The results for flights with stopovers are compared with those obtained using integer programming. The final conclusions are very promising.     

A heuristic model for frequency planning and aircraft routing in small size airlines

The flight schedule of an airline is the primary factor in finding the most effective and efficient deployment of the airline's resources. The flight schedule process aims at finding a set of routes with associated aircraft type, frequency of service and times of departures and arrivals in order to satisfy a specific objective such as profit maximization. In this paper, we develop a two‐phase heuristic model for airline frequency planning and aircraft routing for small size airlines. The first phase develops a frequency plan using an economic equilibrium model between passenger demand for flying a particular route and aircraft operating characteristics. The second phase uses a time‐of‐day model to develop an assignment algorithm for aircraft routing.     

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.     

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.     

Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits – the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.     

Network models for seat allocation on flights

This paper examines the problem of proper (optimal) control over the seat allocation on flights. Given a heterogeneous fleet of aircraft types, multi-leg flights, a number of different passenger categories, and cancelations, an airline's objective is to devise an effective system which aids in setting the seat allocation targets for each category of passengers on each flight. This issue is analyzed by a number of authors in the context of economic, simulation based, probabilistic, and mathematical programming studies. We present an attempt to address this problem from the systems prospective emphasizing characteristics such as: passenger cancelations, multi-leg flights, and rolling tactical planning time horizon. Starting from a very simple network flow models for a single flight with a number of intermediate stops, a number of progressively complex models are presented. The airline flights and the seat allocation system are represented as a generalized network flow model (with gains/losses on arcs) with the objective of flow maximization (profit maximization). This modelling approach does not claim to replace the seat allocation approaches presented in Alstrup et al. (1985), Mayer (1976), Richter (1982), Simpson (1985a), and Wang (1983), but rather construct seat allocations utilizing some of those referenced schemes in a parameter setting mode for a large network model. The objective of this paper is not to report on computational experiments, but to present a modeling approach which seems to be promising, if somewhat speculative.     

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.     

Differences in air passengers’ buying behaviour: findings from Korean and Australian international passengers

Abstract

This paper investigates how air passengers’ expectations, ticket price, airline service quality, value, passenger satisfaction and airline image determine their buying behaviour. To test the conceptual frameworks, path analysis was applied to data collected from Korean and Australian international passengers to examine differences between these two groups. Further analyses were undertaken on different passenger segments between national and foreign airline passengers. The results of the path analysis reveal that air passengers’ buying behaviour differs significantly between Korean and Australian international passengers. Results also reveal that the determinants of air passengers’ buying behaviour differ by airlines.     

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.     

Design of an expert system for aircraft gate assignment

The task of assigning arriving flights at an airport to the available gates is a key activity in airline station operations. With the development of large connecting hub operations, and the resulting volumes of passengers and baggage transferring between flights, the complexity of the task and the number of factors to be considered have increased significantly. Traditional approaches utilizing classical operations research techniques have difficulty with uncertain information and multiple performance criteria, and do not adapt well to the needs of real-time operations support. As a result, several airlines have been exploring the use of expert systems for operational control of ramp activity.This paper discusses the factors that arise in deciding how to allocate flights to gates, and describes the knowledge base structure, data requirements and inference process of an expert system that would recommend gate allocation decisions to ramp control personnel, taking into account the constraints imposed by the available facilities and personnel to handle the aircraft, and the consequences on downstream operations of particular assignment decisions. The paper describes how these concepts have been implemented in a prototype expert system that has been designed to address a restricted set of gate assignment issues within a framework that could be extended to consider a broader range of factors. The operation of the expert system is illustrated through a case study application to a typical flight schedule at a major hub airport.     

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%.     

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.     

A decision support system for port selection

Abstract

A number of studies have been carried out on the factors determining port choice, derived from the perspectives of shippers, carriers or both. Recently, some studies using multi-criteria analysis, more specifically Saaty's analytical hierarchy process (AHP), have been undertaken to address port competitiveness and port selection by shipping lines. Based on a review of the literature on port selection, this article proposes a decision support system (DSS) for port selection using AHP methodology. The proposed DSS is web-based and thus it can be accessed by more decision makers and data collection can be carried out faster. Moreover, AHP addresses the issue of how to structure a complex decision problem, identify its criteria, measure the interaction among them and finally synthesise all the information to arrive at priorities, which depict preferences. AHP is able to assist port managers in obtaining a detailed understanding of the criteria and address the port selection problem utilising multi-criteria analysis. This article presents the architecture and the port selection procedure of the web-based DSS, and then illustrates three different cases. It shows how technology advancement can bring positive effects of strategic planning to shipping firms.     

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.     

A probabilistic framework for weather-based rerouting and delay estimations within an Airspace Planning model

In this paper, we develop a novel severe weather-modeling paradigm to be applied within the context of a large-scale Airspace Planning and collaborative decision-making model in order to reroute flights with respect to a specified probability threshold of encountering severe weather, subject to collision safety, airline equity, and sector workload considerations. This approach serves as an alternative to the current practice adopted by the Federal Aviation Administration (FAA) of adjusting flight routes in accordance with the guidelines specified in the National Playbook. Our innovative contributions in this paper include (a) the concept of “Probability-Nets” and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service; (c) the generation of flight plans that circumvent severe weather phenomena with specified probability threshold levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Additionally, we conduct an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. Computational results and insights are presented based on flight test cases derived from the Enhanced Traffic Management System data provided by the FAA and using weather scenarios derived from the Model Output Statistics forecast data provided by the National Weather Service.     

Environmental charges in airline markets

Over the last two decades many airline markets have been deregulated, resulting in increased competition and use of different types of networks. At the same time there has been an intense discussion on environmental taxation of airline traffic. It is likely that an optimal environmental charge and the effects of a charge differ between different types of aviation markets. In this paper, we derive optimal flight (environmental) charges for different types of airline markets. The first type of market is a multiproduct monopoly airline operating either a point-to-point network or a hub-and-spoke network. The optimal charge is shown to be similar in construction to an optimal charge for a monopolist. We also compare the environmental impact of the two types of networks. Given no differences in marginal damages between airports we find that an airline will always choose the network with the highest environmental damages. The second type of market we investigate is a multiproduct duopoly, where two airlines compete in both passengers and flights. The formulation of the optimal charge is similar to the optimal charge of a single product oligopoly. However, we also show that it is, because of strategic effects, difficult to determine the effects of the charge on the number of flights.     

Competition and public service obligations in European aviation markets

This paper analyzes the effect of universal service policies on the airline markets of five European Union countries (France, Germany, Italy, Spain and the United Kingdom) in the period 2002–2010. Results show that airfare discount schemes for island residents raise demand and positively affect competition and the number of flights at the route level. These effects are evident in France and Italy, but are particularly marked in Spain. By contrast, public service obligations (PSOs) reduce competition on the protected routes, while their effect on the number of flights differs depending on national regulations. In Spain, routes protected with PSOs have greater flight frequencies than those on unprotected routes of similar characteristics, but in France, Italy and the UK the opposite result is found. The empirical model also finds that on routes with low-cost airlines market concentration is smaller and there is a larger number of flights. This result is relevant for the design of universal service policy, since in recent years low-cost airlines have entered a number of thin routes and have increased access to air transportation.