Integrating robust timetabling in line plan optimization for railway systems

Burggraeve, Sofie; Bull, Simon Henry; Vansteenwegen, Pieter; Lusby, Richard Martin

doi:10.1016/j.trc.2017.01.015

Cited by 63 publications

(30 citation statements)

References 22 publications

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“…Formula ( 5) is a max-min objective function. According to Burggraeve [28], we introduce an auxiliary constraint as follows:…”

Section: Optimization From the Perspective Of Operationmentioning

confidence: 99%

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Zhang

2022

Discrete Dynamics in Nature and Society

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Trains can be optimally spread over the period of the cyclic timetable. By integrating sequencing issue with headway time together, this paper studies the structure optimization of mixed-speed train traffic for a cyclic timetable. Firstly, by taking it as a job-shop problem with sequence-dependent setup times on one machine, in the type of infinite capacity resource with headway (ICR + H), the problem is transformed to alternative graph (AG) and then recast to the mixed-speed train traffic planning (MSTTP) model. For the multiobjective in MSTTP, three indicators are optimized, i.e., heterogeneity, cycle time, and buffer time, which correspond to diversity of train service toward passenger, capacity consumption of rail network, and stability of train operation, respectively. Secondly, the random-key genetic algorithm (RKGA) is proposed to tackle the sequence and headway simultaneously. Finally, RKGA is coded with visual studio C# and the proposed method is validated with a case study. The rail system considered is a line section encompassing a territory of 180 km with 15 mixed-speed trains in each cycle of the timetable. Results indicate the comprehensively balanced train plan for all stakeholders from random variations of train sequence and headway time. Both the quantitative proportion of heterogeneity/homogeneity (e.g., 2.5) about the optimized distribution of the mixed train traffic and the link between train headway time and the sequence for each traffic scenario are found. All the findings can be used to arrange the mixed-speed train traffic more scientifically.

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“…Formula ( 5) is a max-min objective function. According to Burggraeve [28], we introduce an auxiliary constraint as follows:…”

Section: Optimization From the Perspective Of Operationmentioning

confidence: 99%

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Zhang

2022

Discrete Dynamics in Nature and Society

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“…• Representing the desired production; • Providing room for the disperded energy, in the production process. Once we have specificed correctly the rules of such a production system, the optimization techniques that classically apply to plans (see, for instance, [25,3] for recent advancements) can generate perfect plans to execute. However, in this configurations, the practical cases should identify exceptional configurations in which it is not possible to forecast in a correct way the energy absorbed (or other numerical variables associated to the plan, such as delays of time, or money waste).…”

Section: Scenario 2 Energy Production Waste Controlmentioning

confidence: 99%

Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes

Olivieri

Governatori

Tomazzoli

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors. Two applications emerged that are very relevant: energy management and business process management. We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts.

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“…Cacchiani and Toth (2012) published a literature review on macroscopic timetable models, like the Periodic Event Scheduling Problem (e.g. Serafini and Ukovich, 1989;Liebchen and Möhring , 2007;Schmidt and Schöbel, 2015;Burggraeve et al, 2017) and the Train Timetabling Problem (e.g. Caprara et al, 2002;Cacchiani et al, 2016).…”

Section: State Of the Artmentioning

confidence: 99%

Optimization of supplements and buffer times in passenger robust timetabling

Burggraeve

Vansteenwegen

2017

Journal of Rail Transport Planning & Management

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This paper proposes an iterative approach to construct a passenger robust railway routing plan and timetable from scratch that also takes supplement and buffer time allocation into account on the signaling level. Each iteration is based on four pillars, which are subsequently executed. First a routing plan that optimizes infrastructure usage is constructed. Secondly, a timetable is constructed that optimizes buffer times between trains while taking passenger numbers into account. Thirdly, simulation is used to evaluate the passenger robustness of the routing plan and the timetable. Finally, the simulation outcome is used to make a new supplement assignment for the next iteration. The algorithm is tested on a case study for the complex station area of Brussels (Belgium). The resulting timetable and routing plan are much more passenger robust than the existing combinations of timetables and routing plans for this case, developed during research or implemented in practice.

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Integrating robust timetabling in line plan optimization for railway systems

Cited by 63 publications

References 22 publications

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes

Optimization of supplements and buffer times in passenger robust timetabling

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