D. Huisman (Dennis)
http://repub.eur.nl/ppl/378/
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.A Column Generation Approach for Locating Roadside Clinics in Africa based upon Effectiveness and Equity
http://repub.eur.nl/pub/78708/
Sat, 15 Aug 2015 00:00:01 GMT<div>J. Núñez Ares</div><div>H. de Vries</div><div>D. Huisman</div>
Long distance truck drivers in Sub-Saharan Africa are extremely vulnerable to HIV and other infectious diseases. The NGO North Star Alliance aims to alleviate this situation by placing so-called Roadside Wellness Centers (RWCs) at busy truck stops along major truck routes. Currently, locations for new RWCs are chosen so as to maximize the expected patient volume and to ensure continuity of access along the routes. As North Star's network grows larger, the objective to provide equal access to healthcare along the different truck routes gains importance. This paper considers the problem to locate a fixed number of RWCs based on these effectiveness and equity objectives. We come up with a novel, set-partitioning type of formulation for the problem and propose a column generation algorithm to solve it. Additionally, we propose and analyze several state-of-the-art acceleration techniques, including dual stabilization, column pool management, and accelerated pricing, which solves the pricing problem as a sequence of shortest path problems. Though the facility location problem is strongly NP-hard, our algorithm yields near-optimal solutions to large randomly generated problem instances within an acceptable amount of time. Our analysis of the trade-off between the equity criterion and North Star's current criteria shows that solutions that are close to optimal with respect to each of the effectiveness and equity objectives are likely to be attainable.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.Scheduling movements in the network of an express service provider
http://repub.eur.nl/pub/77878/
Mon, 01 Dec 2014 00:00:01 GMT<div>I. Louwerse</div><div>J. Mijnarends</div><div>I. Meuffels</div><div>D. Huisman</div><div>H.A. Fleuren</div>
__Abstract__
Express service providers manage shipments from senders to receivers under strict service level agreements. Such shipments are usually not sufficient to justify a single transportation, so it is preferred to maximize consolidation of these shipments to reduce cost. The consolidation is organized via depots and hubs: depots are local sorting centers that take care of the collection and delivery of the parcels at the customers, and hubs are used to consolidate the transportation between the depots. A single transportation between two locations, carried out by a certain vehicle at a specific time, is defined as a movement. In this paper, we address the problem of scheduling all movements in an express network at minimum cost. Our approach allows to impose restrictions on the number of arriving/departing movements at the hubs so that sufficient handling capacity is ensured. As the movement scheduling problem is complex, it is divided into two parts: one part concerns the movements between depots and hubs; the other part considers the movements between the hubs. We use a column generation approach and a local search algorithm to solve these two subproblems, respectively. Computational experiments show that by using this approach the total transportation costs are decreased.A Branch-and-Price Approach for a Ship
Routing Problem with Multiple Products
and Inventory Constraints
http://repub.eur.nl/pub/77876/
Thu, 10 Jul 2014 00:00:01 GMT<div>R. de Mare</div><div>R. Spliet</div><div>D. Huisman</div>
__Abstract__
In the oil industry, different oil products are blended in a refinery.
Afterwards, these products are transported to different harbors by ship. Due to the
limited storage capacity at the harbors and the undesirability of a stock-out, inventory
levels at the harbors have to be taken into account during the construction of
the ship’s routes. In this paper, we give a detailed description of this problem, which
we call the ship routing problem with multiple products and inventory constraints.
Furthermore, we formulate this problem as a generalized set-covering problem. We
propose a branch-and-price algorithm to solve it and we discuss this briefly.Adjusting a railway timetable in case of partial or complete blockades
http://repub.eur.nl/pub/51310/
Mon, 16 Jun 2014 00:00:01 GMT<div>I. Louwerse</div><div>D. Huisman</div>
__Abstract__
Unexpected events, such as accidents or track damages, can have a significant impact on the railway system so that trains need to be canceled and delayed. In case of a disruption it is important that dispatchers quickly present a good solution in order to minimize the nuisance for the passengers. In this paper, we focus on adjusting the timetable of a passenger railway operator in case of major disruptions. Both a partial and a complete blockade of a railway line are considered. Given a disrupted infrastructure situation and a forecast of the characteristics of the disruption, our goal is to determine a disposition timetable, specifying which trains will still be operated during the disruption and determining the timetable of these trains. Without explicitly taking the rolling stock rescheduling problem into account, we develop our models such that the probability that feasible solutions to this problem exist, is high. The main objective is to maximize the service level offered to the passengers. We present integer programming formulations and test our models using instances from Netherlands Railways.An overview of recovery models and algorithms for real-time railway rescheduling
http://repub.eur.nl/pub/51311/
Thu, 01 May 2014 00:00:01 GMT<div>V. Cacchiani</div><div>D. Huisman</div><div>M.P. Kidd</div><div>L.G. Kroon</div><div>P. Toth</div><div>L.P. Veelenturf</div><div>J.C. Wagenaar</div>
This paper presents an overview of recovery models and algorithms for real-time railway disturbance and disruption management. This area is currently an active research area in Operations Research, including real-time timetable rescheduling and real-time rescheduling of the rolling stock and crew duties. These topics are addressed in this paper. Also research dealing with the integration of more than one rescheduling phase is discussed. Currently, the developed methods have been tested mainly in an experimental setting, thereby showing promising results, both in terms of their solution quality and in terms of their computation times. The application of these models and algorithms in real-life railway systems will be instrumental for increasing the quality of the provided railway services, leading to an increased utilization of the involved railway systems.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.A Quasi-Robust Optimization Approach for Resource Rescheduling
http://repub.eur.nl/pub/50110/
Fri, 01 Nov 2013 00:00:01 GMT<div>L.P. Veelenturf</div><div>D. Potthoff</div><div>D. Huisman</div><div>L.G. Kroon</div><div>G. Maróti</div><div>A.P.M. Wagelmans</div>
If a disruption takes place in a complex task-based system, where tasks are carried out
by a number of resource units or servers, real-time disruption management usually has
to deal with an uncertain duration of the disruption. In this paper we present a novel
approach for rescheduling such systems, thereby taking into account the uncertain duration
of the disruption. We assume that several possibilities for the duration of the
disruption are given.
We solve the rescheduling problem as a two-stage optimization problem. In the
first stage, at the start of the disruption, we reschedule the plan based on the optimistic
scenario for the duration of the disruption, while taking into account the possibility
that another scenario will be realized. In fact, we require a prescribed number of the
rescheduled resource duties to be recoverable. This means that they can be easily
recovered if it turns out that another scenario than the optimistic one is realized.
We demonstrate the effectiveness of our approach by an application in real-time
railway crew rescheduling. This is an important subproblem in the disruption management
process of a railway company with a lot of uncertainty about the duration of a
disruption. We test our approach on a number of instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. The numerical experiments
show that the approach indeed finds schedules which are easier to adjust if it
turns out that another scenario than the optimistic one is realized.An Overview of Recovery Models for Real-time Railway Rescheduling
http://repub.eur.nl/pub/50112/
Fri, 01 Nov 2013 00:00:01 GMT<div>V. Cacchiani</div><div>D. Huisman</div><div>M.P. Kidd</div><div>L.G. Kroon</div><div>P. Toth</div><div>L.P. Veelenturf</div><div>J.C. Wagenaar</div>
__ Abstract __
This paper presents an overview of recovery models and algorithms for real-time railway
disturbance and disruption management. This area is currently an active research area in
Operations Research, including real-time timetable rescheduling and real-time rescheduling
of the rolling stock and crew duties. These topics are addressed in this paper. Also
research dealing with the integration of more than one rescheduling phase is discussed.
Currently, the developed methods have been tested mainly in an experimental setting,
thereby showing promising results, both in terms of their solution quality and in terms
of their computation times. The application of these models and algorithms in real-life
railway systems will be instrumental for increasing the quality of the provided railway
services, leading to an increased utilization of the involved railway systems.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 stations’ 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.Adjusting a Railway Timetable in case of Partial or Complete Blockades
http://repub.eur.nl/pub/37246/
Sat, 01 Sep 2012 00:00:01 GMT<div>I. Louwerse</div><div>D. Huisman</div>
Unexpected events, such as accidents or track damages, can have a significant impact on the railway system so that trains need to be canceled and delayed. In case of a disruption it is important that dispatchers quickly present a good solution in order to minimize the nuisance for the passengers. In this paper, we focus on adjusting the timetable of a passenger railway operator in case of major disruptions. Both a partial and a complete blockade of a railway line are considered. Given a disrupted infrastructure situation and a forecast of the characteristics of the disruption, our goal is to determine a disposition timetable, specifying which trains will still be operated during the disruption and determining the timetable of these trains. Without explicitly taking the rolling stock rescheduling problem into account, we develop our models such that the probability that feasible solutions to this problem exists, is high. The main objective is to maximize the service level offered to the passengers. We present integer programming formulations and test our models using 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.Scheduling Movements in the Network of an Express Service Provider
http://repub.eur.nl/pub/32409/
Fri, 11 May 2012 00:00:01 GMT<div>I. Louwerse</div><div>J. Mijnarends</div><div>I. Meuffels</div><div>D. Huisman</div><div>H.A. Fleuren</div>
Express service providers manage shipments from senders to receivers under strict service level agreements. Such shipments are usually not sufficient to justify a single transportation, so it is preferred to maximize consolidation of these shipments to reduce cost. The consolidation is organized via depots and hubs: depots are local sorting centers that take care of the collection and delivery of the parcels at the customers, and hubs are used to consolidate the transportation between the depots. A single transportation between two locations, carried out by a certain vehicle at a specific time, is defined as a movement. In this paper, we address the problem of scheduling all movements in an express network at minimum cost. Our approach allows to impose restrictions on the number of arriving/departing movements at the hubs so that sufficient handling capacity is ensured. As the movement scheduling problem is complex, it is divided into two parts: one part concerns the movements between depots and hubs; the other part considers the movements between the hubs. We use a column generation approach and a local search algorithm to solve these two subproblems, respectively. Computational experiments show that by using this approach the total transportation costs are decreased.Railway crew rescheduling with retiming
http://repub.eur.nl/pub/22106/
Wed, 01 Feb 2012 00:00:01 GMT<div>L.P. Veelenturf</div><div>D. Potthoff</div><div>D. Huisman</div><div>L.G. Kroon</div>
Railway operations are disrupted frequently. For example, the Dutch railway network experiences about three large disruptions per day on average. In a disrupted situation, a railway operator needs to quickly adjust the timetable and the resource schedules. Usually the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to slightly delay the departure of some trains, so that some more flexibility in the crew scheduling process is obtained. Our algorithm focuses on rescheduling the duties of the train drivers. It is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computation time, retiming is allowed only for a limited number of trains for which it seems promising. Computational experiments with train driver duties and real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.
Research highlights
► We propose a new column generation based algorithm for railway crew rescheduling. ► The approach is able to solve the problem within minutes. ► We compare our new approach with retiming with an approach without retiming. ► Allowing retiming leads to better crew schedules with less canceled tasks.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. 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.
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.