T.A.B. Dollevoet (Twan)
http://repub.eur.nl/ppl/23105/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryAn Iterative Framework for Real-time Railway Rescheduling
http://repub.eur.nl/pub/78719/
Mon, 05 Oct 2015 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>L.G. Kroon</div><div>L.P. Veelenturf</div><div>J.C. Wagenaar</div>
Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe an iterative framework in which all three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This shows that the framework and the existing rescheduling approaches can be of great value in practice.An iterative optimization framework for delay management and train scheduling
http://repub.eur.nl/pub/66581/
Mon, 01 Dec 2014 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>F. Corman</div><div>A. D'Ariano</div><div>D. Huisman</div>
__Abstract__
Delay management determines which connections should be maintained in case of a delayed feeder train. Recent delay management models incorporate the limited capacity of the railway infrastructure. These models introduce headway constraints to make sure that safety regulations are satisfied. Unfortunately, these headway constraints cannot capture the full details of the railway infrastructure, especially within the stations. We therefore propose an optimization approach that iteratively solves a macroscopic delay management model on the one hand, and a microscopic train scheduling model on the other hand. The macroscopic model determines which connections to maintain and proposes a disposition timetable. This disposition timetable is then validated microscopically for a bottleneck station of the network, proposing a feasible schedule of railway operations. We evaluate our iterative optimization framework using real-world instances around Utrecht in the Netherlands.Delay Management including Capacities of Stations
http://repub.eur.nl/pub/51620/
Tue, 29 Apr 2014 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>L.G. Kroon</div><div>M. Schmidt</div><div>A. Schoebel</div>
__Abstract__
The question of delay management is whether passenger trains should wait for delayed feeder trains or should depart on time. Solutions to this problem strongly depend on the available capacity of the railway infrastructure. Although the limited capacity of the tracks has been considered in delay management models, the limited capacity of the stations has been neglected so far. In this paper, we develop a model for the delay management problem that includes the capacities of the stations. This model allows rescheduling the platform track assignment. Furthermore, we propose an iterative heuristic in which we first solve the delay management model with a fixed platform track assignment, and then improve this platform track assignment in each step. We show that the latter problem can be solved in polynomial time by describing it as a minimum cost flow model. Finally, we present an extension of the model that balances the delay of the passengers on one hand and the number of changes in the platform track assignment on the other. All models are evaluated on real-world instances from Netherlands Railways.Integrating Timetabling and Crew
Scheduling at a Freight Railway Operator
http://repub.eur.nl/pub/51318/
Tue, 01 Apr 2014 00:00:01 GMT<div>L. Bach</div><div>T.A.B. Dollevoet</div><div>D. Huisman</div>
__Abstract__
We investigate to what degree we can integrate a Train Timetabling / Engine Scheduling Problem with a Crew Scheduling Problem. In the Timetabling Problem we design a timetable for the desired lines by fixing the departure and arrival times. Also, we allocate time-slots in the network to secure a feasible timetable. Next, we assign engines in the Engine Scheduling Problem to the lines in accordance with the timetable. The overall integration is achieved by obtaining an optimal solution for the Timetabling / Engine Scheduling Problem. We exploit the fact that numerous optimal, and near optimal solutions exists. We consider all solutions that can be obtained from the optimal engine schedule by altering the timetable, while keeping the order of demands in the schedules intact. The Crew Scheduling model is allowed to re-time the service of demands if the additional cost is outweighed by the crew savings. This information is implemented in a mathematical model for the Crew Scheduling Problem. The model is solved using a column generation scheme. Hereby it is possible for the Crew Scheduling algorithm to adjust the timetable and achieve a better overall solution. We perform computational experiments based on a case at a freight railway operator, DB Schenker Rail Scandinavia, and show that significant cost savings can be achieved.Fast heuristics for delay management with passenger rerouting
http://repub.eur.nl/pub/74369/
Wed, 01 Jan 2014 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div>
Delay management models determine which connections should be maintained in case of a delayed feeder train. Recently, delay management models are developed that take into account that passengers will adjust their routes when they miss a connection. However, for large-scale real-world instances, these extended models become too large to be solved with standard integer programming techniques. We therefore develop several heuristics to tackle these larger instances. The dispatching rules that are used in practice are our first heuristic. Our second heuristic applies the classical delay management model without passenger rerouting. Finally, the third heuristic updates the parameters of the classical model iteratively. We compare the quality of these heuristic solution methods on real-life instances from Netherlands Railways. In this experimental study, we show that our iterative heuristic can solve large-scale real-world instances within a short computation time. Furthermore, the solutions obtained by this iterative heuristic are of good quality.Delay Management and Dispatching in Railways
http://repub.eur.nl/pub/38241/
Thu, 10 Jan 2013 00:00:01 GMT<div>T.A.B. Dollevoet</div>
Passenger railway transportation plays a crucial role in the mobility in Europe. Since the privatization of the railway sector in the 90s, passenger satisfaction has become an important performance indicator in this sector. A key aspect for passengers is the reliability of transfers between trains. When a train arrives at the station with a delay, passengers might miss their connection if the next train departs on time. These passengers then prefer the connecting train to wait, but this introduces delays for many other passengers. Delay Management is a field in railway operations that deals with this situation. It determines whether a connecting train should wait for the passengers that arrive with a delayed train or should depart on time.
In this thesis, we apply techniques from Operations Research to develop models and solution approaches for Delay Management. The objective in our models is the minimization of passenger delay. First, we extend the classical delay management model with passenger rerouting. This allows us to compute the exact delays for passengers. We develop an exact algorithm and several heuristics to solve this extension. Then, we incorporate the limited capacity of the stations in our models. Stations are the bottlenecks of the railway infrastructure, where delays of one train can easily propagate to other trains. When optimizing the wait-depart decisions, these secondary delays should be considered. We therefore develop an integrated model that includes headway constraints for trains on the same track in the station and an iterative approach that evaluates the timetable microscopically.
Robust UAV mission planning
http://repub.eur.nl/pub/61342/
Wed, 12 Dec 2012 00:00:01 GMT<div>L. Evers</div><div>T.A.B. Dollevoet</div><div>A.I. Barros</div><div>H. Monsuur</div>
Unmanned Aerial Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. Therefore, our model takes into account uncertainty in the fuel usage between targets, for instance due to weather conditions. We report results for using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solutions on one hand and the solution found with average fuel usage on the other. These probabilities are assessed both by simulation and by derivation of problem specific theoretical bounds on the probability of constraint feasibility. In doing so, we show how the sustainability of a UAV mission can be significantly improved. Additionally, we suggest how the robust solution can be operationalized in a realistic setting, by complementing the robust tour with agility principles.Delay Management including Capacities of Stations
http://repub.eur.nl/pub/37239/
Tue, 11 Sep 2012 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>A. Schobel</div><div>M. Schmidt</div>
The question of delay management is whether trains should wait for delayed feeder
trains or should depart on time. Solutions to this problem strongly depend on the available
capacity of the railway infrastructure. While the limited capacity of the tracks has been
considered in delay management models, the limited capacity of the stations has been
neglected so far. In this paper, we develop a model for the delay management problem that
includes the stationsâ€™ capacities. This model allows to reschedule the platform assignment
dynamically. Furthermore, we propose an iterative algorithm in which we first solve the
delay management model with a fixed platform assignment and then improve this platform
assignment in each step. We show that the latter problem can be solved in polynomial
time by presenting a totally unimodular IP formulation. Finally, we present an extension
of the model that balances the delay of the passengers on the one hand and the number of
changes in the platform assignment on the other. All models are evaluated on real-world
instances from Netherlands Railways.An Iterative Optimization Framework for Delay Management and Train Scheduling
http://repub.eur.nl/pub/32416/
Wed, 23 May 2012 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>F. Corman</div><div>A. D'Ariano</div><div>D. Huisman</div>
Delay management determines which connections should be maintained in case of a delayed feeder train. Recent delay management models incorporate the limited capacity of the railway infrastructure. These models introduce headway constraints to make sure that safety regulations are satisfied. Unfortunately, these headway constraints cannot capture the full details of the railway infrastructure, especially within the stations. We therefore propose an iterative optimization approach that iteratively solves a macroscopic delay management model on the one hand, and a microscopic train scheduling model on the other hand. The macroscopic model determines which connections to maintain and proposes a disposition timetable. This disposition timetable is then validated microscopically for a bottleneck station of the network, proposing a feasible schedule of railway operations. This schedule reduces delay propagation and thereby minimizes passenger delays. We evaluate our iterative optimization framework using real-world instances around Utrecht in the Netherlands.Delay management with rerouting of passengers
http://repub.eur.nl/pub/37693/
Wed, 01 Feb 2012 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>M. Schmidt</div><div>A. Schoebel</div>
The question of delay management (DM) is whether trains should wait for a delayed feeder train or should depart on time. In classical DM models, passengers are assumed to take their originally planned routes. After the wait-depart decisions are made, passengers will certainly change to the best-possible route according to these decisions. In this paper, we propose a model where such a rerouting of passengers is incorporated in the DM process. To describe the problem, we represent it as an event-activity network similar to the one used in classical DM, with some additional events to incorporate origin and destination of the passengers. We present an integer programming formulation of this problem. Furthermore, we discuss the variant in which we assume fixed costs for maintaining connections, and we present a polynomial algorithm for the special case of only one origindestination pair that we later use to derive a strong lower bound for the integer program. Finally, computational experiments based on real-world data from Netherlands Railways show that significant improvements with respect to the passengers' traveling times can be obtained by taking the rerouting of passengers into account in the model. Delay management including capacities of stations
http://repub.eur.nl/pub/59175/
Thu, 01 Dec 2011 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>M. Schmidt</div><div>A. SchĂ¶bel</div>
The question of delay management (DM) is whether trains should wait for delayed feeder trains or should depart on time. Solutions to this problem strongly depend on the capacity constraints of the tracks making sure that no two trains can use the same piece of track at the same time. While these capacity constraints have been included in integer programming formulations for DM, the capacity constraints of the stations (only offering a limited number of platforms) have been neglected so far. This can lead to highly infeasible solutions. In order to overcome this problem we suggest two new formulations for DM both including the stations' capacities. We present numerical results showing that the assignment-based formulation is clearly superior to the packing formulation. We furthermore propose an iterative algorithm in which we improve the platform assignment with respect to the current delays of the trains at each station in each step. We will show that this subproblem asks for coloring the nodes of a graph with a given number of colors while minimizing the weight of the conflicts. We show that the graph to be colored is an interval graph and that the problem can be solved in polynomial time by presenting a totally unimodular IP formulation.Fast Heuristics for Delay Management with Passenger Rerouting
http://repub.eur.nl/pub/26866/
Sat, 01 Oct 2011 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div>
Delay management models determine which connections should be maintained in case of a delayed feeder train. Recently, delay management models are developed that take into account that passengers will adjust their routes when they miss a connection. However, for large-scale real-world instances, these extended models become too large to be solved with standard integer programming techniques. We therefore develop several heuristics to tackle these larger instances. The dispatching rules that are used in practice are our first heuristic. Our second heuristic applies the classical delay management model without passenger rerouting. Finally, the third heuristic updates the parameters of the classical model iteratively. We compare the quality of these heuristic solution methods on real-life instances from Netherlands Railways. In this experimental study, we show that our iterative heuristic can solve large real-world instances within a short computation time. Furthermore, the solutions obtained by this iterative heuristic are of good quality.
Spare parts inventory control for an aircraft component repair shop
http://repub.eur.nl/pub/25605/
Mon, 11 Jul 2011 00:00:01 GMT<div>W.L. van Jaarsveld</div><div>T.A.B. Dollevoet</div>
We study spare parts inventory control for a repair shop for aircraft components. Defect components that are removed from the aircraft are sent to such a shop for repair. Only after inspection of the component, it becomes clear which specific spare parts are needed to repair it, and in what quantity they are needed. Market requirements on shop performance are reflected in fill rate requirements on the turn around times of the repairs for each component type. The inventory for spare parts is controlled by independent min-max policies. Because parts may be used in the repair of different component types, the resulting optimization problem has a combinatorial nature. Practical instances may consist of 500 component types and 4000 parts, and thus pose a significant computational challenge. We propose a solution algorithm based on column generation. We study the pricing problem, and develop a method that is very efficient in (repeatedly) solving this pricing problem. With this method, it becomes feasible to solve practical instances of the problem in minutes.Solving large scale crew scheduling problems in practice
http://repub.eur.nl/pub/32050/
Wed, 01 Jun 2011 00:00:01 GMT<div>E.J.W. Abbink</div><div>L. Albino</div><div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>J. Roussado</div><div>R.L. Saldanha</div>
This paper deals with large-scale crew scheduling problems arising at the main Dutch railway operator, Netherlands Railways (NS). NS operates about 30000 trains a week. All these trains need a driver and a certain number of guards. Some labor rules restrict the duties of a certain crew base over the complete week. Therefore, splitting the problem in several subproblems per day leads to suboptimal solutions. In this paper, we present an algorithm, called LUCIA, which can solve such huge instances without splitting. This algorithm combines Lagrangian heuristics, column generation and fixing techniques. We compare the results with existing practice. The results show that the new method significantly improves the solution. Robust UAV Mission Planning
http://repub.eur.nl/pub/22802/
Fri, 25 Feb 2011 00:00:01 GMT<div>L. Evers</div><div>T.A.B. Dollevoet</div><div>A.I. Barros</div><div>H. Monsuur</div>
Unmanned Areal Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage between target locations, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. As such, our model takes into account uncertainty in the fuel usage between targets (for instance due to weather conditions) as well as uncertainty in the importance of visiting specific target locations. We report results using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solution on one hand and the solution found with average fuel usage and expected value of information on the other. In doing so, we show how the sustainability of a UAV mission can be significantly improved.Solving Large Scale Crew Scheduling Problems in Practice
http://repub.eur.nl/pub/21711/
Tue, 07 Dec 2010 00:00:01 GMT<div>E.J.W. Abbink</div><div>L. Albino</div><div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>J. Roussado</div><div>R.L. Saldanha</div>
This paper deals with large-scale crew scheduling problems arising at the Dutch railway operator, Netherlands Railways (NS). NS operates about 30,000 trains a week. All these trains need a driver and a certain number of guards. Some labor rules restrict the duties of a certain crew base over the complete week. Therefore splitting the problem in several subproblems per day leads to suboptimal solutions.
In this paper, we present an algorithm, called LUCIA, which can solve such huge instances without splitting. This algorithm combines Lagrangian heuristics, column generation and fixing techniques. We compare the results with existing practice. The results show that the new method significantly improves the solution.Delay Management with Re-Routing of Passengers
http://repub.eur.nl/pub/19445/
Tue, 11 May 2010 00:00:01 GMT<div>T.A.B. Dollevoet</div><div>D. Huisman</div><div>M. Schmidt</div><div>A. Schobel</div>
The question of delay management is whether trains should wait for a delayed feeder train
or should depart on time. In classical delay management models passengers always take
their originally planned route. In this paper, we propose a model where re-routing of
passengers is incorporated.
To describe the problem we represent it as an event-activity network similar to the one
used in classical delay management, with some additional events to incorporate origin
and destination of the passengers. We present an integer programming formulation of
this problem. Furthermore, we discuss the variant in which we assume fixed costs for
maintaining connections and we present a polynomial algorithm for the special case of
only one origin-destination pair. Finally, computational experiments based on real-world
data from Netherlands Railways show that significant improvements can be obtained by
taking the re-routing of passengers into account in the model.