Flight operations recovery: New approaches considering passenger recovery

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability. Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.     

A conceptual solution to the aircraft gate assignment problem using 0, 1 linear programming

The U.S. commercial airline movement to “hub” operations has increased rapidly over the past several years. This trend will grow with the introduction of the Wayport concept. These giant “people exchange” facilities will feature multiple runways and massive concourses. While the Wayport developers may automate the ground movement of passengers, the sheer size of the facilities and the anticipated close scheduling of flights will confront the passenger with a problem of new dimension in getting from arrival gate to departure gate. One thing that could help alleviate the situation would be “sensible” assignment of flights to gates - sensible in that the assignment would take into account the communion of interest of the passengers. Other things being equal, the positioning of incoming flights should take into account the distribution of passengers among connecting flights. The larger the number of passengers arriving on flight X for destination A, the closer flight X should be positioned to the flight departing for A. This inquiry looks conceptually at this problem, reducing the distance passengers must walk from gate to gate. The criterion selected is minimization of the total passenger-distance travel for a given arrival-departure cycle. The problem can be conveniently cast as a 0,1 Linear Programming (LP) problem. The author discusses derivation of problem parameters and then solves several representative problems. A unique feature of the LP approach is the availability with the optimal solution of cost factors and assignments for suboptimal solutions. Minor changes in the input data do not require reworking the problem. Although the solution is characterized as conceptual, some ideas are given for practical use of the solution methodology in a dynamic airport environment.     

分布式整数规划及其在航线扰动问题的应用

在航空公司的运作中时常会出现干扰它正常运作的现象。在这种情况下,航空公司必须马上制定航线修复计划使受到干扰的航线尽快复原,以防止更大面积的航班取消和航班延误。提出一种基于递增映射迭代方法的分布式整数规划算法来解决由于机场关闭引起的航线扰动问题。整个问题分成了两个子问题：可行航线的生成和飞机的重指派。第一个子问题的问题空间被初始点分割方法分割成了若干片段。然后在一个分布式的计算网络中使用递增映射迭代方法在分得的每个片段上同时求解第一个子问题。得到的可行航线用来求解第二个子问题。最后的算例结果可以发现提出的方法要好于CPLEX和多目标基因算法。     

Solving a robust airline crew pairing problem with column generation

In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this difficult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from local airlines in Turkey.     

Airline planning benchmark problems—Part II: Passenger groups,utility and demand allocation

This paper is the second of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. The former has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. Revenue models in airline planning optimisation only roughly approximate the passenger decision process. However, there is a growing body of literature giving empirical insights into airline passenger choice. Here we propose a new paradigm for passenger modelling, that enriches our representation of passenger revenue, in a form designed to be useful for optimisation. We divide the market demand into market segments, or passenger groups, according to characteristics that differentiate behaviour in terms of airline product selection. Each passenger group has an origin, destination, size (number of passengers), departure time window, and departure time utility curve, indicating willingness to pay for departure in time sub-windows. Taking as input market demand for each origin–destination pair, we describe a process by which we construct realistic passenger group data, based on the analysis of empirical airline data collected by our industry partner. We give the results of that analysis, and describe 33 benchmark instances produced.     

Airline flight schedule planning under competition

This paper presents a modeling framework for airline flight schedule planning under competition. The framework generates an operational flight timetable that maximizes the airline's revenue, while ensuring efficient utilization of the airline's resources (e.g. aircraft and crew). It explicitly considers passenger demand shift due to the network-level competition with other airlines. It also considers minimizing the needless ground time of the resources. The problem is formulated in the form of a bi-level mathematical program where the upper level represents the airline scheduling decisions, while the lower level captures passenger responses in terms of itinerary choices. A solution methodology is developed which integrates a meta- heuristic search algorithm, a network competition analysis model, and a resource (e.g. aircraft and crew) tracking model. The performance of the framework is evaluated through several experiments to develop the schedule for a major U.S. airline. The results demonstrate the success of the framework to develop a competitive schedule with efficient resources.     

Review of real-time vehicle schedule recovery methods in transportation services

This paper presents a comprehensive review on methods for real-time schedule recovery in transportation services. The survey concentrates on published research on recovery of planned schedules in the occurrence of one or several severe disruptions such as vehicle breakdowns, accidents, and delays. Only vehicle assignment and rescheduling are reviewed; crew scheduling and passenger logistics problems during disruptions are not. Real-time vehicle schedule recovery problems (RTVSRP) are classified into three classes: vehicle rescheduling, for road-based services, train-based rescheduling, and airline schedule recovery problems. For each class, a classification of the models is presented based on problem formulations and solution strategies. The paper concludes that RTVSRP is a challenging problem that requires quick and good quality solutions to very difficult and complex situations, involving several different contexts, restrictions, and objectives. The paper also identifies research gaps to be investigated in the future, stimulating research in this area.     

面向乘客策略行为的航空公司舱位控制与动态定价模型

将动态控制舱位开放作为策略,研究乘客具有策略行为时航空公司舱位控制与动态定价问题.通过建立以舱位开放和价格变化为决策变量的多周期动态规划模型,讨论开放舱位和最优定价应满足的条件,并通过比较得出:实行动态舱位控制策略可缓解乘客策略行为对航空公司期望收益的影响.最后应用算例分析乘客策略程度对航空公司价格和期望收益的影响,同时发现:舱位控制不能完全消除乘客策略行为对期望收益的影响,但供应水平越高或乘客策略程度越大,其缓解策略行为的效果越明显.     

Airline planning benchmark problems—Part I:: Characterising networks and demand using limited data

This paper is the first of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on the analysis of airline data. Then a multi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II extends this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.     

区域航空市场航线客流量预测研究

为了有效地控制和合理地分配区域航空市场航线客流量,提高航空机场的效率,为航管决策部门提供制定计划的理论依据,在深入研究国内外航空客流量预测研究成果基础之上,针对区域航空市场的特点,提出了一个自顶向下的航线客流量预测模型。它包括总体趋势预测、中长期预测模型和短期预测模型三部分;并将神经网络和支持向量机构成的组合模型引入中长期预测模型中,使用神经网络实现短期预测模型;并结合A公司实际进行了实证研究,证明了该预测模型的有效性。文中研究成果对所有航管部门具有一定的指导意义。     

A novel column generation strategy for large scale airline crew pairing problems

A crew pairing is a sequence of flight legs beginning and ending at the same crew domicile. Crew pairing planning is the primary cost-determining phase in airline crew scheduling. Optimizing crew pairings of an airline timetable is an extremely important process which helps to minimize operational crew costs and to maximize crew utilization.There are various restrictions imposed by regulations or company policies that must be considered and satisfied in crew pairing generation process. Keeping these restrictions and regulations in mind, the main goal of the optimization is the generation of low cost sets of valid crew pairings which cover all flights in airline's timetable.For this research study, already existing works related to crew pairing optimization are examined and a new column generation strategy, a pricing network design and a pairing elimination heuristic are developed as a contribution to the previous studies. In the proposed strategy, the main problem is modeled and solved as a set-covering problem and the pricing sub problem is modeled as a shortest-path problem which is efficiently solved over a duty-flight overnight connection graph by the combined usage of heuristic and exact algorithms. The proposed strategy has been tested with real world data obtained from Turkish Airlines and it is seen that it is capable of generating very competitive solutions compared to current practices in Turkish Airlines. It is also observed that there are various advantages of proposed solution approach such as sensitivity to penalty coefficients, generating less deadheads, very close solution times with a single threaded software and light weight hardware.     

An integrated scenario-based approach for robust aircraft routing,crew pairing and re-timing

For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (e.g. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision-making process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. (2012) [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of Dunbar et al. (2012) [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially significant reduction in delay propagation.     

Optimizing airline passenger prescreening systems with Bayesian decision models

The Transportation Security Agency provides airline security in the United States using a variety of measures including a computer based passenger prescreening system. This paper develops Bayesian decision models of two prescreening systems: one that places ticketed passengers into two classifications (fly and no-fly), and a three classification system that includes potential flight. Using a parameterized cost structure, and the expected monetary value decision criteria, this paper develops optimal levels of undesirable personal characteristics that should place people into the various categories. The models are explored from both the government perspective and the passenger's perspective.     

Flight schedule design for a charter airline

We consider the problem of designing the flight schedule for a charter airline. Exploiting the network structure of the problem, we develop exact and approximate models and solution approaches, and compare their results using data provided by an airline. We show that quality results can be generated using the exact approach or the heuristic approach, with the heuristic approach capable of generating good solutions very quickly.     

Integrated aircraft-path assignment and robust schedule design with cruise speed control

Assignment of aircraft types, each having different seat capacity, operational expenses and availabilities, critically affects airlines’ overall cost. In this paper, we assign fleet types to paths by considering not only flight timing and passenger demand, as commonly done in the literature, but also operational expenses, such as fuel burn and carbon emission costs associated with adjusting the cruise speed to ensure the passenger connections. In response to flight time uncertainty due to the airport congestions, we allow minor adjustments on the flight departure times in addition to cruise speed control, thereby satisfying the passenger connections at a desired service level. We model the uncertainty in flight duration via a random variable arising in chance constraints to ensure the passenger connections. Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult. To handle them, we use mixed-integer second order cone programming. We compare the performance of a schedule generated by the proposed model to the published schedule for a major U.S. airline. On the average, there exists a 20% overall operational cost saving compared to the published schedule. To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially.     

Integrated airline scheduling

Airline scheduling is composed of fleet assignment, aircraft maintenance routing, and crew scheduling optimization subproblems. It is believed that the full optimization problem is computationally intractable, and hence the constituent subproblems are optimized sequentially so that the output of one is the input of the next. The sequential approach, however, provides an overall suboptimal solution and can also fail to satisfy the maintenance constraints of an otherwise feasible full problem. In this paper several integrated models for the optimization of airline scheduling are presented for the first time, and solved by applying an enhanced Benders decomposition method combined with accelerated column generation. Solutions of several realistic data sets are computed using the integrated models, which are compared with solutions of the best known approaches from the literature. As a result, the integrated approach significantly reduces airline costs. Finally, a comparison of alternative formulations has shown that keeping the crew scheduling problem alone in the Benders subproblem is much more efficient than keeping the aircraft routing problem.     

不正常航班恢复的飞机和乘客优化调配模型

现实生活中有很多因素影响航空公司的正常运营,比如飞机故障、极端天气、航空管制等,这些问题的产生不仅增加了航空公司的运营成本,而且还给乘客带来许多损失。为了降低航空公司的运营成本和乘客的损失,从飞机调配和受干扰乘客的重新调配出发构建模型,通过IBM ILOG CPLEX软件对两个模型进行求解,并获得了备用恢复行程的飞机调配方案和受干扰乘客的重新调配方案。通过实际算例验证了该模型的有效性和实用性。     

Robust planning of airport platform buses

Most airports have two types of gates: gates with an air bridge to the terminal and remote stands. For flights at a remote stand, passengers are transported to and from the aircraft by platform buses. In this paper we investigate the problem of planning platform buses as it appears at Amsterdam Airport Schiphol. We focus on robust planning, i.e. we want to avoid that the bus planning is affected by flight delays and in this way invokes delays in other flights and ground-handling processes. We present a column generation algorithm for planning of platform buses that maximizes robustness. We also present a discrete-event simulation model to compare our algorithm to a first-come-first-served heuristic as is used in current practice. Our computational results with real-life data indicate that our algorithm significantly reduces the number of replanning steps and special recovery measures during the day of operation.     

Aircrew pairings with possible repetitions of the same flight number

A crew pairing is a sequence of flights, connections and rests that starts and ends at a crew base and is assigned to a single crew. The crew pairing problem consists of determining a minimum cost set of feasible crew pairings such that each flight is covered exactly once and side constraints are satisfied. Traditionally, this problem has been solved in the industry by a heuristic three-phase approach that solves sequentially a daily, a weekly, and a monthly problem. This approach prohibits or strongly penalizes the repetition of the same flight number in a pairing. In this paper, we highlight two weaknesses of the three-phase approach and propose alternative solution approaches that exploit flight number repetitions in pairings. First, when the flight schedule is irregular, we show that better quality solutions can be obtained in less computational time if the first two phases are skipped and the monthly problem is solved directly using a rolling horizon approach based on column generation. Second, for completely regular flight schedules, we show that better quality solutions can be derived by skipping the daily problem phase and solving the weekly problem directly.     

基于免疫机制的航班延误快速恢复模型及实现

航班延误是困扰航空运输业的一道世界性难题.在考虑航班延误的经济成本下,借鉴生物免疫系统的免疫应答机制,提出了一种机场大面积航班延误快速恢复模型,并用妥协免疫算法实现B细胞克隆选择来优化航班延误恢复调度过程.针对中国某枢纽机场的航班数据进行了仿真,结果表明,该模型相对于先到先服务调度方法有明显优势,大大提高了航班的整体运转效率,达到了减少航班延误经济损失和快速疏散滞留机场乘客的目的.