Integrating line planning, timetabling, and vehicle scheduling: a customer-oriented heuristic

Michaelis, Mathias; Schöbel, Anita

doi:10.1007/s12469-009-0014-9

Cited by 90 publications

(54 citation statements)

References 29 publications

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“…The arcs are used to define the new lines to be constructed: L is the set of new lines. The overall planning problem for a transport network is usually sequentially solved (Cadarso and Mann 2012a;Michaelis and Schobel 2009). The paper presented here deals with the network design problem.…”

Section: The Network Infrastructurementioning

confidence: 99%

Combining robustness and recovery in rapid transit network design

Cadarso

Marı́n

2016

Transportmetrica A: Transport Science

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When designing a transport network, decisions are made according to an expected value for network state variables, such as infrastructure and vehicle conditions, which are uncertain at a planning horizon of up to decades. Because disruptions, such as infrastructure breakdowns, will arise and affect the network on the day of operations, actions must be taken from the network design. Robust network designs may be implemented but they are extremely expensive if disruptions do not realise. In this paper, we propose a novel approach to the network design problem where robustness and recovery are combined. We look for the trade-off between efficiency and robustness accounting for the possibility of recovering from disruptions: recoverable robust network design. Computational experiments drawn from fictitious and realistic networks show how the presented approach reduces the price of robustness and recovery costs as compared to traditional robust and non-robust rapid transit network design approaches.

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Section: The Network Infrastructurementioning

confidence: 99%

Combining robustness and recovery in rapid transit network design

Cadarso

Marı́n

2016

Transportmetrica A: Transport Science

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“…Further applications include railway crew scheduling (see Caprara et al 2007 for a survey on passenger railway applications) and crew scheduling in urban mass transit (Haase et al 2001;Borndörfer et al 2002;Michaelis and Schöbel 2009;Steinzen et al 2009). While some railway applications deal with assigning all personnel to trains (e.g., drivers, conductors, and mechanics), others solely focus on the assignment of train drivers.…”

Section: Literature Reviewmentioning

confidence: 99%

A graph partitioning strategy for solving large-scale crew scheduling problems

Jütte

Thonemann

2014

OR Spectrum

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Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing highquality solution schedules while significantly cutting runtimes compared to the nondecomposition solution algorithm. We are following a "greenfield" approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc.

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“…The three planning steps line planning, timetabling, and vehicle scheduling have been considered in Israeli and Ceder (1995) and Quak (2003), while Liebchen (2008) even investigates the integration of the four planning steps line planning, timetabling, vehicle-and crew-scheduling. An integrated model and a heuristic changing the order of the classical planning steps has been proposed in Michaelis and Schöbel (2009). On the other hand, line planning has been also considered within timetabling; see Liebchen and Möhring (2007) and Lindner (2000).…”

Section: Integration With Other Planning Phasesmentioning

confidence: 99%

Line planning in public transportation: models and methods

2011

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The problem of defining suitable lines in a public transportation system (bus, railway, tram, or underground) is an important real-world problem that has also been well researched in theory. Driven by applications, it often lacks a clear description, but is rather stated in an informal way. This leads to a variety of different published line planning models. In this paper, we introduce some of the basic line planning models, identify their characteristics, and review literature on models, mathematical approaches, and algorithms for line planning. Moreover, we point out related topics as well as current and future directions of research.Keywords Line planning · Public transportation · Mathematical programming The line planning problem in public transportationGiven the increasing demand for mobility, an efficient organization of public (passenger) transportation becomes more and more important. This is reflected not only in practice but also by an increasing number of research papers dealing with the optimization of public transport. The goal of the optimization process is on the one hand, to offer a high quality of service for the passengers while, on the other hand, the costs for setting up and running the transit system should be small.As noted by many authors (see e.g., Ceder and Wilson 1986;Liebchen and Möhring 2007;Desaulniers and Hickman 2007), the planning process in public transportation consists of several consecutive planning phases. As shown in Fig. 1, the process starts A. Schöbel (B)

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Integrating line planning, timetabling, and vehicle scheduling: a customer-oriented heuristic

Cited by 90 publications

References 29 publications

Combining robustness and recovery in rapid transit network design

Combining robustness and recovery in rapid transit network design

A graph partitioning strategy for solving large-scale crew scheduling problems

Line planning in public transportation: models and methods

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