A review on railway delay management

König, Eva

doi:10.1007/s12469-020-00233-1

Cited by 13 publications

(11 citation statements)

References 66 publications

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“…For future research, the extension of the model by the inclusion of some new real elements and circumstances can be a direction. One idea in this respect is the investigation in delay management and whether and to which extent vessels and trucks have to wait for each other in case of delays; see, e.g., [24] for the case of public transportation. Other metaheuristic solution methodologies can be tested, too.…”

Section: Discussionmentioning

confidence: 99%

“…In order to better compare the goal achievement in the three objectives, we should make them of the same scale. Hence, an objective value z j is normalised to znorm j as: znorm j = z j − Zmin j Zmax j − Zmin j Zmin j = 0; j = 1, 2, 3 (24) where Zmax j and Zmin j are the maximum and minimum value of objective j . Zmax j for the three objectives are calculated as follows:…”

Section: Normalisation Of the Objectivesmentioning

confidence: 99%

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A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

Nourmohammadzadeh

Voß

2021

Ann Math Artif Intell

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The ever increasing demand for container transportation has led to the congestion of maritime container terminals in the world. In this work, the two interrelated problems of berth and quay crane scheduling are considered in an integrated multiobjective mathematical model. A special character of this model is that the arrival times of vessels and the failure (working) times of quay cranes are not deterministic and can vary based on some scenarios. Hence, a robust model is devised for the problem having three objectives of minimising the deviations from target berthing locations and times as well as departure delays of all vessels. This robust optimisation seeks to minimise the value of the objectives regarding all the scenarios. An exact solution approach based on the 𝜖-constraint method by the Gurobi software is applied. Moreover, regarding the complexity of the problem, two Simulated Annealing (SA) based metaheuristics, namely a Multi-Objective Simulated Annealing (MOSA) and a Pareto Simulated Annealing (PSA) approach are adapted with a novel solution encoding scheme. The three methods are compared based on some multiobjective metrics and a statistical test. The advantage of the integration of berth and quay crane scheduling is examined as well.

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Section: Discussionmentioning

confidence: 99%

Section: Normalisation Of the Objectivesmentioning

confidence: 99%

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

Nourmohammadzadeh

Voß

2021

Ann Math Artif Intell

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“…The real-time TS problem is based on the current position of train proposing dynamic solutions in case of disturbances at operational level or on the railway infrastructures and solving, in real time, forthcoming conflicts [11] minimizing, for example, eventual delays as in [12] or [13]. In [11], a branch and bound model introduces an approach to solve the conflict between two trains using alternative graphs.…”

Section: Introductionmentioning

confidence: 99%

“…Constraints (12) assure that the number of passengers traveling on the train does not exceed the maximum allowed capacity at the node i-th.…”

mentioning

confidence: 99%

Min-Max Approach for High-Speed Train Scheduling and Rescheduling Models

2021

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This paper focuses on the operational and real time control of railway management. A passengers centric scheduling and rescheduling models are formalized as a linear mathematical model and by a min max technique. The proposed approach determines the train timetables for scheduling rail services along a multiline rail network according to operational constraints related to train capacity, train speed limits, passenger's train transfers, possible conflict in the track section use, with the main objective to minimize the travel time of a set of passengers' groups. The network is formalized as a set of nodes which represent logistic operations of the trains and arcs which define the sequence of tasks to be carried out by trains. Alternative arcs have been added to solve the Conflict Resolution Problem (CRP) which may appears when two trains require simultaneously to cover the same block section. The timetable resulted by the scheduling problem have been used as the input of a rescheduling model where predefined disturbances have been assumed to some nodes of the network. The effectiveness of the proposed approach have been verified comparing its performances with a traditional minimization rescheduling model both based on the minimization of the passengers' discomfort in terms of delay propagation in the travel times. The model guarantees more resilience for trains classified with higher priority. The proposed models are applied to the real case study of the rail network for high-speed trains in the Northern Italy INDEX TERMS High-speed train, Min-Max approach, Train rescheduling, Train scheduling

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“…If delays occur, these travel times depend on the delay management strategies, e.g., if a train waits for transferring passengers or if it departs on time. This is the topic of delay management, for an overview we refer to Dollevoet et al [11] and König [26]. In this paper we assume the delay management strategy as given and fixed.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Robustness of Public Transport Systems Using Machine Learning

Müller‐Hannemann¹,

Rückert²,

Schiewe³

et al. 2021

Preprint

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The planning of attractive and cost efficient public transport systems is a highly complex optimization process involving many steps. Integrating robustness from a passenger's point of view makes the task even more challenging. With numerous different definitions of robustness in literature, a real-world acceptable evaluation of the robustness of a public transport system is to simulate its performance under a large number of possible scenarios. Unfortunately, this is computationally very expensive.In this paper, we therefore explore a new way of such a scenario-based robustness approximation by using methods from machine learning. We achieve a fast approach with a very high accuracy by gathering a subset of key features of a public transport system and its passenger demand and training an artificial neural network to learn the outcome of a given set of robustness tests. The network is then able to predict the robustness of untrained instances with high accuracy using only its key features, allowing for a robustness oracle for transport planners that approximates the robustness in constant time. Such an oracle can be used as black box to increase the robustness within a local search framework for integrated public transportation planning. In computational experiments with different benchmark instances we demonstrate an excellent quality of our predictions.

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A review on railway delay management

Cited by 13 publications

References 66 publications

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

Min-Max Approach for High-Speed Train Scheduling and Rescheduling Models

Estimating the Robustness of Public Transport Systems Using Machine Learning

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