Huisman, D. (Dennis)
http://repub.eur.nl/ppl/378/
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RePub, Erasmus University RepositoryA Quasi-Robust Optimization Approach for Resource Rescheduling
http://repub.eur.nl/pub/50110/
Fri, 01 Nov 2013 00:00:01 GMT<div>Veelenturf, L.P.</div><div>Potthoff, D.</div><div>Huisman, D.</div><div>Kroon, L.G.</div><div>Maroti, G.</div><div>Wagelmans, A.P.M.</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>Cacchiani, V.</div><div>Huisman, D.</div><div>Kidd, M.</div><div>Kroon, L.G.</div><div>Toth, P.</div><div>Veelenturf, L.P.</div><div>Wagenaar, J.C.</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>Dollevoet, T.A.B.</div><div>Huisman, D.</div><div>Schobel, A.</div><div>Schmidt, M.</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>Louwerse, I.</div><div>Huisman, D.</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>Dollevoet, T.A.B.</div><div>Corman, F.</div><div>D'Ariano, A.</div><div>Huisman, D.</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>Louwerse, I.</div><div>Mijnarends, J.</div><div>Meuffels, I.</div><div>Huisman, D.</div><div>Fleuren, H.A.</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>Veelenturf, L.P.</div><div>Potthoff, D.</div><div>Huisman, D.</div><div>Kroon, L.G.</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>Dollevoet, T.A.B.</div><div>Huisman, D.</div><div>Schmidt, M.</div><div>Schoebel, A.</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>Dollevoet, T.A.B.</div><div>Huisman, D.</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>Abbink, E.J.W.</div><div>Albino, L.</div><div>Dollevoet, T.A.B.</div><div>Huisman, D.</div><div>Roussado, J.</div><div>Saldanha, R.L.</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. Algorithmic Support for Disruption Management at Netherlands Railways
http://repub.eur.nl/pub/22456/
Thu, 10 Feb 2011 00:00:01 GMT<div>Kroon, L.G.</div><div>Huisman, D.</div>
In the Netherlands, relatively large disruptions occur on average about three times per day, each time leading to a temporary and local
unavailability of the railway system. Faster response times and better solutions can be expected by the application of algorithmic support
in the disruption management process. That is, the modified timetable, rolling stock circulation, and crew duties are generated automatically
based on appropriate mathematical models and algorithms for solving these models. In this paper, we present such models and algorithms that were
developed at Erasmus University Rotterdam and are being implemented at Netherlands Railways. Finally, we discuss challenges for research and
implementation in practice.Solving Large Scale Crew Scheduling Problems in Practice
http://repub.eur.nl/pub/21711/
Tue, 07 Dec 2010 00:00:01 GMT<div>Abbink, E.J.W.</div><div>Albino, L.</div><div>Dollevoet, T.A.B.</div><div>Huisman, D.</div><div>Roussado, J.</div><div>Saldanha, R.L.</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.Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling
http://repub.eur.nl/pub/21591/
Mon, 01 Nov 2010 00:00:01 GMT<div>Potthoff, D.</div><div>Huisman, D.</div><div>Desaulniers, G.</div>
The Dutch railway network experiences about three large disruptions per day on average. In this paper, we present an algorithm to reschedule the crews when such a disruption occurs. The algorithm is based on column generation techniques combined with Lagrangian heuristics. Since the number of duties is very large in practical instances, we first define a core problem of tractable size. If some tasks remain uncovered in the solution of the core problem, we perform a neighborhood exploration to improve the solution. Computational experiments with real-life instances show that our method is capable of producing good solutions within a couple of minutes of computation time.Rescheduling in passenger railways: the rolling stock rebalancing problem
http://repub.eur.nl/pub/17385/
Tue, 01 Jun 2010 00:00:01 GMT<div>Budai, G.</div><div>Maróti, G.</div><div>Dekker, R.</div><div>Huisman, D.</div><div>Kroon, L.G.</div>
This paper addresses the Rolling Stock Rebalancing Problem (RSRP) which arises within a passenger railway operator when the rolling stock has to be rescheduled due to changing circumstances. RSRP is relevant both in the short-term planning stage and in the real-time operations. RSRP has as input a timetable and a rolling stock circulation where the allocation of the rolling stock among the stations at the start or at the end of a certain planning period does not match with the allocation before or after that planning period. The problem is then to modify the input rolling stock circulation in such a way that the number of remaining off-balances is minimal. If all off-balances have been solved, then the obtained rolling stock circulation can be implemented in practice. For practical usage of solution approaches for RSRP, it is important to solve the problem quickly. Since we prove that RSRP is NP-hard, we focus on heuristic solution approaches: we describe two heuristics and compare them with each other on (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get further insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving an existing rolling stock circulation model.Delay Management with Re-Routing of Passengers
http://repub.eur.nl/pub/19445/
Tue, 11 May 2010 00:00:01 GMT<div>Dollevoet, T.A.B.</div><div>Huisman, D.</div><div>Schmidt, M.</div><div>Schobel, A.</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.A Branch-and-Price Approach for a Ship Routing Problem with Multiple Products and Inventory Constraints
http://repub.eur.nl/pub/18255/
Tue, 23 Feb 2010 00:00:01 GMT<div>Mare, R. de</div><div>Spliet, R.</div><div>Huisman, D.</div>
In the oil industry, different oil components are blended in a
refinery to fuel products. 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 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, and we present a
Branch-and-Price algorithm to solve it. The pricing problems have a
very complex nature. We discuss a dynamic programming algorithm to
solve them to optimality.Algorithmic Support for Railway Disruption Management
http://repub.eur.nl/pub/17524/
Thu, 17 Dec 2009 00:00:01 GMT<div>Kroon, L.G.</div><div>Huisman, D.</div>
Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.Railway Crew Rescheduling with Retiming
http://repub.eur.nl/pub/16746/
Tue, 15 Sep 2009 00:00:01 GMT<div>Veelenturf, L.P.</div><div>Potthoff, D.</div><div>Huisman, D.</div><div>Kroon, L.G.</div>
Railway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, 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 delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.Demand-driven scheduling of movies in a multiplex
http://repub.eur.nl/pub/16070/
Mon, 01 Jun 2009 00:00:01 GMT<div>Eliashberg, J.</div><div>Hegie, Q.</div><div>Ho, J.</div><div>Huisman, D.</div><div>Miller, S.J.</div><div>Swami, S.</div><div>Weinberg, C.B.</div><div>Wierenga, B.</div>
This paper is about a marketing decision support system in the movie industry. The decision support system of interest is a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies, for each day of the week, on which screen(s) different movies will be played, and at which time(s). The model integrates elements from marketing (the generation of demand figures) with approaches from operations research (the optimization procedure). Therefore, it consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time, and (ii) a scheduling procedure that quickly finds a near optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule, we formulate the "movie scheduling problem" as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We tested the combined demand forecasting/schedule optimization procedure in a multiplex in Amsterdam, generating movie schedules for fourteen weeks. The proposed model not only makes movie scheduling easier and less time consuming, but also generates schedules that attract more visitors than current "intuition-based" schedules.Decision support for crew rostering at NS
http://repub.eur.nl/pub/18654/
Mon, 01 Jun 2009 00:00:01 GMT<div>Hartog, A.</div><div>Huisman, D.</div><div>Abbink, E.J.W.</div><div>Kroon, L.G.</div>
This paper describes a method for solving the cyclic crew rostering problem (CCRP). This is the problem of cyclically ordering a set of duties for a number of crew members, such that several complex constraints are satisfied and such that the quality of the obtained roster is as high as possible. The described method was tested on a number of instances of NS, the largest operator of passenger trains in the Netherlands. These instances involve the generation of rosters for groups of train drivers or conductors of NS. The tests show that high quality solutions for practical instances of the CCRP can be generated in an acceptable amount of computing time. Finally, we describe an experiment where we constructed rosters in an automatic way for a group of conductors. They preferred our—generated—rosters over their own manually constructed rosters.