Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty

The environment in which airlines operate is uncertain for many reasons, for example due to the effects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence uncertainty and delay for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. This work proposes a mixed integer programming approach to scheduling the airline’s reserve crew. A simulation of the airline’s operations with stochastic journey time and crew absence inputs (without reserve crew) is used to generate input disruption scenarios for the mixed integer programming simulation scenario model (MIPSSM) formulation. Each disruption scenario corresponds to a record of all of the disruptions that may occur on the day of operation which are solvable by using reserve crew. A set of disruption scenarios form the input of the MIPSSM formulation, which has the objective of finding the reserve crew schedule that minimises the overall level of disruption over the set of input scenarios. Additionally, modifications of the MIPSSM are explored, a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as the quantity of disruption scenarios that are added to the MIPSSM. An investigation into what makes a good set of scenarios is also presented

A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty

Airlines operate in an uncertain environment for many reasons, for example due to the efects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence and journey time uncertainty for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. Given an airline's crew schedule and aircraft routings we propose a Mixed Integer Programming approach to scheduling the airline's reserve crew. A simulation of the airline's operations with stochastic journey time and crew absence inputs and without reserve crew is used to generate disruption scenarios for the MIPSSM formulation (Mixed Integer Programming Simulation Scenario Model). Each disruption scenario corresponds to a record of all of the disruptions in a simulation for which reserve crew use would have been beneficial. For each disruption in a disruption scenario there is a record of all reserve crew that could have been used to solve or reduce the disruption. This information forms the input to the MIPSSM formulation, which has the objective of finding the reserve schedule that minimises the overall level of disruption over a set of scenarios. Additionally, modifications of the MIPSSM are explored, and a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed Mixed Integer Programming Simulation Scenario Model or MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as ensuring that enough disruption scenarios are added to the MIPSSM

A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty

Airlines operate in an uncertain environment for many reasons, for example due to the efects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence and journey time uncertainty for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. Given an airline's crew schedule and aircraft routings we propose a Mixed Integer Programming approach to scheduling the airline's reserve crew. A simulation of the airline's operations with stochastic journey time and crew absence inputs and without reserve crew is used to generate disruption scenarios for the MIPSSM formulation (Mixed Integer Programming Simulation Scenario Model). Each disruption scenario corresponds to a record of all of the disruptions in a simulation for which reserve crew use would have been beneficial. For each disruption in a disruption scenario there is a record of all reserve crew that could have been used to solve or reduce the disruption. This information forms the input to the MIPSSM formulation, which has the objective of finding the reserve schedule that minimises the overall level of disruption over a set of scenarios. Additionally, modifications of the MIPSSM are explored, and a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed Mixed Integer Programming Simulation Scenario Model or MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as ensuring that enough disruption scenarios are added to the MIPSSM

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.

Combining robustness and recovery for airline schedules

In this thesis, we address different aspects of the airline scheduling problem. The main difficulty in this field lies in the combinatorial complexity of the problems. Furthermore, as airline schedules are often faced with perturbations called disruptions (bad weather conditions, technical failures, congestion, crew illness…), planning for better performance under uncertainty is an additional dimension to the complexity of the problem. Our main focus is to develop better schedules that are less sensitive to perturbations and, when severe disruptions occur, are easier to recover. The former property is known as robustness and the latter is called recoverability. We start the thesis by addressing the problem of recovering a disrupted schedule. We present a general model, the constraint-specific recovery network, that encodes all feasible recovery schemes of any unit of the recovery problem. A unit is an aircraft, a crew member or a passenger and its recovery scheme is a new route, pairing or itinerary, respectively. We show how to model the Aircraft Recovery Problem (ARP) and the Passenger Recovery Problem (PRP), and provide computational results for both of them. Next, we present a general framework to solve problems subject to uncertainty: the Uncertainty Feature Optimization (UFO) framework, which implicitly embeds the uncertainty the problem is prone to. We show that UFO is a generalization of existing methods relying on explicit uncertainty models. Furthermore, we show that by implicitly considering uncertainty, we not only save the effort of modeling an explicit uncertainty set: we also protect against possible errors in its modeling. We then show that combining existing methods using explicit uncertainty characterization with UFO leads to more stable solutions with respect to changes in the noise's nature. We illustrate these concepts with extensive simulations on the Multi-Dimensional Knapsack Problem (MDKP). We then apply the UFO to airline scheduling. First, we study how robustness is defined in airline scheduling and then compare robustness of UFO models against existing models in the literature. We observe that the performance of the solutions closely depend on the way the performance is evaluated. UFO solutions seem to perform well globally, but models using explicit uncertainty have a better potential when focusing on a specific metric. Finally, we study the recoverability of UFO solutions with respect to the recovery algorithm we develop. Computational results on a European airline show that UFO solutions are able to significantly reduce recovery costs