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.

Disruption Management in Passenger Railway Transportation

This paper deals with disruption management in passenger railway transportation. In the disruption management process, many actors belonging to different organizations play a role. In this paper we therefore describe the process itself and the roles of the different actors. Furthermore, we discuss the three main subproblems in railway disruption management: timetable adjustment, and rolling stock and crew re-scheduling. Next to a general description of these problems, we give an overview of the existing literature and we present some details of the specific situations at DSB S-tog and NS. These are the 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 Operations Research models for disruption management in the railway context, models and techniques that have been developed for related problems in the airline world are discussed as well. Finally, we address the integration of the re-scheduling processes of the timetable, and the resources rolling stock and crew

Reliable Reserve-Crew Scheduling for Airlines

We study the practical setting in which regular- and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules' infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure

Reliable reserve-crew scheduling for airlines

We study the practical setting in which regular- and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules’ infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure.</p

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines’ on-time performance at its worst level in 2007 since 1995. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the US air-travel infrastructure? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight? The contribution of this paper is two-fold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure

Prediction of Pilot's Absenteeism in an Airline Company

RÉSUMÉ: Les compagnies aériennes sont soumises aux nombreuses sources de perturbations pendant les opérations. Il est essentiel pour ce type d'industrie de prédire les origines des perturbations dans les différents niveaux de gestion pour réduire les coûts de rattrapage du calendrier. Une des sources les plus importantes et coûteuses de perturbation dans les compagnies aériennes est l'absentéisme des pilotes au moment de l'opération des vols. ‎Dans ce mémoire, nous nous concentrons sur l'absentéisme des pilotes pour cause de maladie. Nous proposons une méthode d'apprentissage supervisé qui est capable de prédire la somme mensuelle des heures de maladie chez les pilotes après la publication du calendrier. La méthode proposée utilise les caractéristiques du calendrier mensuel comme les variables explicatives et elle fait la prédiction en utilisant d'un algorithme itératif. La méthode a été vérifiée avec des données réelles et une amélioration considérable a été observée dans les résultats. Pour rendre la méthode en situation réelle, nous avons créé une interface facile à utiliser comme un système d'aide à la décision. Cette interface automatise l'ensemble du processus de prédiction. ABSTRACT: Airline companies are subject to a considerable number of disruptions during operations. It is vital for this type of industry to predict the source of disruptions in different levels of management to reduce the costs of schedule recovery. One of the most important and costly source of disruption in the airlines is absenteeism of the pilots at the time of the flights operation. In this master thesis, we focus on the absenteeism of the pilots because of the sickness. We propose a supervised learning method which is able to predict total monthly sick hours after publishing the schedule. The proposed method uses characteristics of the monthly schedule as the explanatory variables and the prediction is made by using an iterative algorithm. The model was tested with real data and a substantial improvement was observed in the results. For applying this method in business environment, we created a user-friendly web application as the decision support system. This application automates the whole process of prediction

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

Robustness in the personnel shift scheduling problem : the modelling and validation of different proactive and reactive strategies

The personnel planning process in any organization aims to ensure that the organization can offer a desired service level to their customers at a minimal personnel cost and maximal personnel satisfaction. This process consists of three hierarchical phases characterized by differing levels of decision freedom and uncertainty about the future personnel demand and availability. In this respect, the personnel planner faces a large and medium level of uncertainty and decision freedom in the strategic staffing phase and the tactical scheduling phase, respectively. In these phases, the personnel planner manages the uncertainty by making assumptions and predictions about the future. Based on these assumptions and predictions, the personnel planner makes decisions about the number of employees to be hired and assigned to work during specific points in time. These decisions constrain the decision freedom in the operational allocation phase, in which the personnel planner obtains the most recent information on the actual personnel demand and availability. This information may differ from the assumptions and predictions, and affect the service level and personnel cost and satisfaction. As such, the moment the personnel planner faces the lowest level of uncertainty, (s)he also has the lowest level of decision freedom. In this respect, this dissertation aims to propose strategies to anticipate and deal with unexpected divergences between the previously established assumptions and predictions, and the actual personnel demand and availability