Disruption management in passenger railway transportation.

This paper deals with disruption management in passengerrailway transportation. In the disruption management process, manyactors belonging to different organizations play a role. In this paperwe therefore describe the process itself and the roles of thedifferent actors.Furthermore, we discuss the three main subproblems in railwaydisruption management: timetable adjustment, and rolling stock andcrew re-scheduling. Next to a general description of these problems,we give an overview of the existing literature and we present somedetails of the specific situations at DSB S-tog and NS. These arethe railway operators in the suburban area of Copenhagen, Denmark,and on the main railway lines in the Netherlands, respectively.Since not much research has been carried out yet on OperationsResearch models for disruption management in the railway context,models and techniques that have been developed for related problemsin the airline world are discussed as well.Finally, we address the integration of the re-scheduling processesof the timetable, and the resources rolling stock and crew.

Integrating the fleet assignment model with uncertain demand

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.One of the main challenges facing the airline industry is planning under uncertainty, especially in the context of schedule disruptions. The robust models and solution algorithms that have been proposed and developed to handle the uncertain parameters will be discussed. Fleet assignment models (FAM) are used by many airlines to assign aircraft to fights in a schedule to maximize profit. In the context of FAM, the goal of robustness is to produce solutions that perform well relative to uncertainties in demand and operation. In this thesis, we introduce new FAMs (i.e. DFAM1 and DFAM2) that tackles the common problem associated with aircraft utilization. Subsequently, stochastic programming (SP) is presented as a method of choice for the research. Through the use of a two-stage SP with recourse technique, the DFAMs are extended to SP-FAMs (SP-FAM1 and SP-FAM2). The main distinction of the SP-FAM compared with other FAMs is that, given a stochastic passenger demand, it gives a strategic fleet assignment solution that hedges against all possible tactical solutions. In addition, we have a tactical solution for every scenario. In generating the demand scenarios, we use a network-simulation model embedded with a time-series engine that gives a snapshot of one week that is representative of any other week of the scheduling season. We later outline the approach of solving the SP-FAMs where the schedule is compacted through several preprocessing steps before inputting it into SAS-AMPL converter. The SAS-AMPL converter prepares all the data into readable AMPL format. Finally, we execute the optimizer using a FortMP solver (integrated in AMPL) that invokes branch-and-bound algorithm. We give a proof of concept using real data from a Middle East airline. Our investigations establish clear benefits of the recourse FAM compared to alternative models. Finally, we propose areas of future research to improve SP-FAM robustness through solution algorithms, revenue management (RM) effects, calibration of network-simulation models and system integration

Empirical investigations of properties of robust aircraft routing models

The airline schedule planning process is an important component of airline operations, and it involves considerably complex problems. This research focuses on the aircraft routing phase. We introduce the concept of robustness in aircraft routing problems, and find solutions that can stand uncertainty. We categorize the delays in flight operations into two components – independent delay and propagated delay. In the data driven approach, independent delay can be regarded as constant, but propagated delay can be worked on. An example of aircraft swap is given to show that aircraft routing can potentially reduce the flight delays. To solve robust aircraft routing problems, we propose a list of formulations. They are in three categories – Lan, Clarke, Barnhart’s approach, chance-constrained programming approach, and extreme value approach. We conduct experiments with two airline networks – a 50-flight network and a 165-flight network. The K-fold cross validation approach is incorporated into aircraft routing problems to eliminate overfitting. According to the three evaluation metrics – on time performance, average total propagated delay and passenger disruptions, several good formulations are identified, which are recommended for airline schedule planners. We also explain the reasons behind the solution differences

Resource-Constrained Airline Ground Operations: Optimizing Schedule Recovery under Uncertainty

Die zentrale europäische Verkehrsflusssteuerung (englisch: ATFM) und Luftverkehrsgesellschaften (englisch: Airlines) verwenden unterschiedliche Paradigmen für die Priorisierung von Flügen. Während ATFM jeden Flug als individuelle Einheit betrachtet, um die Kapazitätsauslastung aller Sektoren zu steuern, bewerten Airlines jeden Flug als Teilabschnitt eines Flugzeugumlaufes, eines Crew-Einsatzplanes bzw. einer Passagierroute. Infolgedessen sind ATFM-Zeitfenster für Flüge in Kapazitätsengpässen oft schlecht auf die Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks abgestimmt, sodass die Luftfahrzeug-Bodenabfertigung – als Verbindungselement bzw. Bruchstelle zwischen einzelnen Flügen im Netzwerk – als Hauptverursacher primärer und reaktionärer Verspätungen in Europa gilt. Diese Dissertation schließt die Lücke zwischen beiden Paradigmen, indem sie ein integriertes Optimierungsmodell für die Flugplanwiederherstellung entwickelt. Das Modell ermöglicht Airlines die Priorisierung zwischen Flügen, die von einem ATFM-Kapazitätsengpass betroffen sind, und berücksichtigt dabei die begrenzte Verfügbarkeit von Abfertigungsressourcen am Flughafen. Weiterhin werden verschiedene Methoden untersucht, um die errechneten Flugprioritäten vertraulich innerhalb von kooperativen Lösungsverfahren mit externen Stakeholdern austauschen zu können. Das integrierte Optimierungsmodell ist eine Erweiterung des Resource-Constrained Project Scheduling Problems und integriert das Bodenprozessmanagement von Luftfahrzeugen mit bestehenden Ansätzen für die Steuerung von Flugzeugumläufen, Crew-Einsatzplänen und Passagierrouten. Das Modell soll der Verkehrsleitzentrale einer Airline als taktische Entscheidungsunterstützung dienen und arbeitet dabei mit einer Vorlaufzeit von mehr als zwei Stunden bis zur nächsten planmäßigen Verkehrsspitze. Systemimmanente Unsicherheiten über Prozessabweichungen und mögliche zukünftige Störungen werden in der Optimierung in Form von stochastischen Prozesszeiten und mittels des neu-entwickelten Konzeptes stochastischer Verspätungskostenfunktionen berücksichtigt. Diese Funktionen schätzen die Kosten der Verspätungsausbreitung im Airline-Netzwerk flugspezifisch auf der Basis historischer Betriebsdaten ab, sodass knappe Abfertigungsressourcen am Drehkreuz der Airline den kritischsten Flugzeugumläufen zugeordnet werden können. Das Modell wird innerhalb einer Fallstudie angewendet, um die taktischen Kosten einer Airline in Folge von verschiedenen Flugplanstörungen zu minimieren. Die Analyseergebnisse zeigen, dass die optimale Lösung sehr sensitiv in Bezug auf die Art, den Umfang und die Intensität einer Störung reagiert und es folglich keine allgemeingültige optimale Flugplanwiederherstellung für verschiedene Störungen gibt. Umso dringender wird der Einsatz eines flexiblen und effizienten Optimierungsverfahrens empfohlen, welches die komplexen Ressourcenabhängigkeiten innerhalb eines Airline-Netzwerks berücksichtigt und kontextspezifische Lösungen generiert. Um die Effizienz eines solchen Optimierungsverfahrens zu bestimmen, sollte das damit gewonnene Steuerungspotenzial im Vergleich zu aktuell genutzten Verfahren über einen längeren Zeitraum untersucht werden. Aus den in dieser Dissertation analysierten Störungsszenarien kann geschlussfolgert werden, dass die flexible Standplatzvergabe, Passagier-Direkttransporte, beschleunigte Abfertigungsverfahren und die gezielte Verspätung von Abflügen sehr gute Steuerungsoptionen sind und während 95 Prozent der Saison Anwendung finden könnten, um geringe bis mittlere Verspätungen von Einzelflügen effizient aufzulösen. Bei Störungen, die zu hohen Verspätungen im gesamten Airline-Netzwerk führen, ist eine vollständige Integration aller in Betracht gezogenen Steuerungsoptionen erforderlich, um eine erhebliche Reduzierung der taktischen Kosten zu erreichen. Dabei ist insbesondere die Möglichkeit, Ankunfts- und Abflugzeitfenster zu tauschen, von hoher Bedeutung für eine Airline, um die ihr zugewiesenen ATFM-Verspätungen auf die Flugzeugumläufe zu verteilen, welche die geringsten Einschränkungen im weiteren Tagesverlauf aufweisen. Die Berücksichtigung von Unsicherheiten im nachgelagerten Airline-Netzwerk zeigt, dass eine Optimierung auf Basis deterministischer Verspätungskosten die taktischen Kosten für eine Airline überschätzen kann. Die optimale Flugplanwiederherstellung auf Basis stochastischer Verspätungskosten unterscheidet sich deutlich von der deterministischen Lösung und führt zu weniger Passagierumbuchungen am Drehkreuz. Darüber hinaus ist das vorgeschlagene Modell in der Lage, Flugprioritäten und Airline-interne Kostenwerte für ein zugewiesenes ATFM-Zeitfenster zu bestimmen. Die errechneten Flugprioritäten können dabei vertraulich in Form von optimalen Verspätungszeitfenstern pro Flug an das ATFM übermittelt werden, während die Definition von internen Kostenwerten für ATFM-Zeitfenster die Entwicklung von künftigen Handelsmechanismen zwischen Airlines unterstützen kann.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 ConclusionsAir Traffic Flow Management (ATFM) and airlines use different paradigms for the prioritisation of flights. While ATFM regards each flight as individual entity when it controls sector capacity utilization, airlines evaluate each flight as part of an aircraft rotation, crew pairing and passenger itinerary. As a result, ATFM slot regulations during capacity constraints are poorly coordinated with the resource interdependencies within an airline network, such that the aircraft turnaround -- as the connecting element or breaking point between individual flights in an airline schedule -- is the major contributor to primary and reactionary delays in Europe. This dissertation bridges the gap between both paradigms by developing an integrated schedule recovery model that enables airlines to define their optimal flight priorities for schedule disturbances arising from ATFM capacity constraints. These priorities consider constrained airport resources and different methods are studied how to communicate them confidentially to external stakeholders for the usage in collaborative solutions, such as the assignment of reserve resources or ATFM slot swapping. The integrated schedule recovery model is an extension of the Resource-Constrained Project Scheduling Problem and integrates aircraft turnaround operations with existing approaches for aircraft, crew and passenger recovery. The model is supposed to provide tactical decision support for airline operations controllers at look-ahead times of more than two hours prior to a scheduled hub bank. System-inherent uncertainties about process deviations and potential future disruptions are incorporated into the optimization via stochastic turnaround process times and the novel concept of stochastic delay cost functions. These functions estimate the costs of delay propagation and derive flight-specific downstream recovery capacities from historical operations data, such that scarce resources at the hub airport can be allocated to the most critical turnarounds. The model is applied to the case study of a network carrier that aims at minimizing its tactical costs from several disturbance scenarios. The case study analysis reveals that optimal recovery solutions are very sensitive to the type, scope and intensity of a disturbance, such that there is neither a general optimal solution for different types of disturbance nor for disturbances of the same kind. Thus, airlines require a flexible and efficient optimization method, which considers the complex interdependencies among their constrained resources and generates context-specific solutions. To determine the efficiency of such an optimization method, its achieved network resilience should be studied in comparison to current procedures over longer periods of operation. For the sample of analysed scenarios in this dissertation, it can be concluded that stand reallocation, ramp direct services, quick-turnaround procedures and flight retiming are very efficient recovery options when only a few flights obtain low and medium delays, i.e., 95% of the season. For disturbances which induce high delay into the entire airline network, a full integration of all considered recovery options is required to achieve a substantial reduction of tactical costs. Thereby, especially arrival and departure slot swapping are valuable options for the airline to redistribute its assigned ATFM delays onto those aircraft that have the least critical constraints in their downstream rotations. The consideration of uncertainties in the downstream airline network reveals that an optimization based on deterministic delay costs may overestimate the tactical costs for the airline. Optimal recovery solutions based on stochastic delay costs differ significantly from the deterministic approach and are observed to result in less passenger rebooking at the hub airport. Furthermore, the proposed schedule recovery model is able to define flight priorities and internal slot values for the airline. Results show that the priorities can be communicated confidentially to ATFM by using the concept of 'Flight Delay Margins', while slot values may support future inter-airline slot trading mechanisms.:1 Introduction 2 Status Quo on Airline Operations Management 3 Schedule Recovery Optimization Approach with Constrained Resources 4 Implementation and Application 5 Case Study Analysis 6 Conclusion

Solving crew scheduling problem in offshore supply vessels, heuristics and decomposition methods

For the efficient utilisation of resources in various transportation settings, scheduling is a significant area of research. Having crew as the main resource for operation maintenance, scheduling crew have been a powerful decision making tool for optimisation studies. This research provides a detailed real case study analysis regarding the difficulties in planning crew in maritime industry. As a special case study, this thesis researches crew scheduling in offshore supply vessels which are used for specific operations of a global scaled company in oil and gas industry deeply with modified formulations, heuristics and decomposition methods.An extended version of computational study for a simple formulation approach (Task Based Model) is applied as deeper analysis to Leggate (2016). Afterwards, more realistic approach to the same problem is revised. Following the revision, a customized and thorough computational study on the heuristic method with various settings is designed and implemented in C++. After elaborated analysis completed on the suggested models firstly, a modification on Time Windows model is presented to increase the efficacy. This modification provides a sharp decrease in upper bounds within a short time compared to the previously suggested models. Through this suggestion, more economic schedules within a short period of time are generated.Achieving high performances from the modified model, an application of a decomposition algorithm is provided. We implemented a hybrid solution of Benders Decomposition with a customized heuristic for the modified model. Although this hybrid solution does not provide high quality solutions, it evaluates the performance of possible decomposed models with potential improvements for future research. An introduction to robust crew scheduling in maritime context is also given with a description of resources of uncertainty in this concept and initial robust formulations are suggested.For the efficient utilisation of resources in various transportation settings, scheduling is a significant area of research. Having crew as the main resource for operation maintenance, scheduling crew have been a powerful decision making tool for optimisation studies. This research provides a detailed real case study analysis regarding the difficulties in planning crew in maritime industry. As a special case study, this thesis researches crew scheduling in offshore supply vessels which are used for specific operations of a global scaled company in oil and gas industry deeply with modified formulations, heuristics and decomposition methods.An extended version of computational study for a simple formulation approach (Task Based Model) is applied as deeper analysis to Leggate (2016). Afterwards, more realistic approach to the same problem is revised. Following the revision, a customized and thorough computational study on the heuristic method with various settings is designed and implemented in C++. After elaborated analysis completed on the suggested models firstly, a modification on Time Windows model is presented to increase the efficacy. This modification provides a sharp decrease in upper bounds within a short time compared to the previously suggested models. Through this suggestion, more economic schedules within a short period of time are generated.Achieving high performances from the modified model, an application of a decomposition algorithm is provided. We implemented a hybrid solution of Benders Decomposition with a customized heuristic for the modified model. Although this hybrid solution does not provide high quality solutions, it evaluates the performance of possible decomposed models with potential improvements for future research. An introduction to robust crew scheduling in maritime context is also given with a description of resources of uncertainty in this concept and initial robust formulations are suggested

Stochastic Delay Cost Functions to Estimate Delay Propagation under Uncertainty

We provide a mathematical formulation of flight-specific delay cost functions that enables a detailed tactical consideration of how a given flight delay will interact with all downstream constraints in the respective aircraft rotation. These functions are reformulated into stochastic delay cost functions to respect conditional probabilities and increasing uncertainty related to more distant operational constraints. Conditional probabilities are learned from historical operations data, such that typical delay propagation patterns can support the flight prioritization process as a part of tactical airline schedule recovery. A case study compares the impact of deterministic and stochastic cost functions on optimal recovery decisions during an airport constraint. We find that deterministic functions systematically overestimate potential disruption costs as well as optimal schedule recovery costs in high delay situations. Thus, an optimisation based on stochastic costs outperforms the deterministic approach by up to 15%, as it reveals ’hidden’ downstream recovery potentials. This results in different slot allocations and in fewer passengers missing their connections

Scheduling airline reserve crew using a probabilistic crew absence and recovery model

Airlines require reserve crew to replace delayed or absent crew, with the aim of preventing consequent flight cancellations. A reserve crew schedule specifies the duty periods for which different reserve crew will be on standby to replace any absent crew. For both legal and health-and-safety reasons the reserve crew's duty period is limited, so it is vital that these reserve crew are available at the right times, when they are most likely to be needed and will be most effective. Scheduling a reserve crew unnecessarily, or earlier than needed, wastes reserve crew capacity. Scheduling a reserve crew too late means either an unrecoverable cancellation or a delay waiting for the reserve crew to be available. Determining when to schedule these crew can be a complex problem , since one crew member could potentially cover a vacancy on any one of a number of different flights, and flights interact with each other, so a delay or cancellation for one flight can affect a number of later flights. This work develops an enhanced mathematical model for assessing the impact of any given reserve crew schedule, in terms of reduced total expected cancellations and any resultant reserve induced delays, whilst taking all of the available information into account, including the schedule structure and interactions between flights, the uncertainties involved, and the potential for multiple crew absences on a single flight. The interactions between flights have traditionally made it very hard to predict the effects of cancellations or delays, and hence to predict when best to allocate reserve crew and lengthy simulation runs have traditionally been used to make these predictions. This work is motivated by the airline industry's need for improved mathematical models to replace the time-consuming simulation-based approaches. The improved predictive probabilistic model which is introduced here is shown to produce results that match a simulation model to a high degree of accuracy, in a much shorter time, making it an effective and accurate surrogate for simulation. The modelling of the problem also provides insights into the complexity of the problem that a purely simulation based approach would miss. The increased speed enables potential deployment within a real time decision support context, comparing alternative recovery decisions as disruptions occur. To illustrate this, the model is used in this paper as a fitness function in meta-heuristics algorithms to generate disruption minimising reserve crew schedules for a real airline schedule. These are shown to be of a high quality, demonstrating the effectiveness and reliability of the proposed approach

FLIGHT RISK MANAGEMENT AND CREW RESERVE OPTIMIZATION

There are two key concerns in the development process of aviation. One is safety, and the other is cost. An airline running with high safety and low cost must be the most competitive one in the market. This work investigates two research efforts respectively relevant to these two concerns. When building support of a real time Flight Risk Assessment and Mitigation System (FRAMS), a sequential multi-stage approach is developed. The whole risk management process is considered in order to improve the safety of each flight by integrating AHP and FTA technique to describe the framework of all levels of risks through risk score. Unlike traditional fault tree analysis, severity level, time level and synergy effect are taken into account when calculating the risk score for each flight. A risk tree is designed for risk data with flat shape structure and a time sensitive optimization model is developed to support decision making of how to mitigate risk with as little cost as possible. A case study is solved in reasonable time to approve that the model is practical for the real time system. On the other hand, an intense competitive environment makes cost controlling more and more important for airlines. An integrated approach is developed for improving the efficiency of reserve crew scheduling which can contribute to decrease cost. Unlike the other technique, this approach integrates the demand forecasting, reserve pattern generation and optimization. A reserve forecasting tool is developed based on a large data base. The expected value of each type of dropped trip is the output of this tool based on the predicted dropping rate and the total scheduled trips. The rounding step in current applied methods is avoided to keep as much information as possible. The forecasting stage is extended to the optimization stage through the input of these expected values. A novel optimization model with column generation algorithm is developed to generate patterns to cover these expected level reserve demands with minimization to the total cost. The many-to-many covering mode makes the model avoid the influence of forecasting errors caused by high uncertainty as much as possible