Decision support for crew rostering at NS

The railway crew scheduling problem is to determine an optimal crew assignment for the railway timetable data by minimizing the required number of crew members in order to satisfy the set covering constraints. Column generation is one of the optimization methods that can solve the problem efficiently. In order to improve the convergence of column generation, we propose dual inequalities for a DantzigWolfe decomposition of railway crew scheduling problems to reduce the number of replications in the column generation procedure. The dual inequalities are generated from the characteristic of the given railway timetable data. Computational results demonstrate the effectiveness of the proposed method compared with the conventional column generation for an actual railway timetable data.

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“…The sequences are assigned to the individual crew members. The problem is called crew rostering (Hartog et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Column generation with dual inequalities for railway crew scheduling problems

2011

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“…Also Xu et al (2006) use holidays as input for scheduling flight instructors but add soft constraints to avoid scheduling instructing periods immediately before or after holidays of instructors. Hartog et al (2009) recognise that if rosters are made for individual crew members specific characteristics like holidays can be considered and they distinguish between popular and unpopular work-a concept we enlarge for popular and unpopular days for holidays. Gärtner et al (1998) took into account in shift rota-design that in some periods of the year more workers wish to go on leave.…”

Section: Introductionmentioning

confidence: 99%

Socially acceptable annual holiday planning for the crew of a local public transport company in Germany

Dewess

2010

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We consider the problem of socially acceptable annual holiday planning. A new model is developed taking into account legal, company and driver issues. Among others, it includes capacity constraints concerning different qualifications, holiday entitlements and connections between drivers. For each application for leave benefit values depending on family situations (e.g. driver has children of school age), other social criteria and priorities of applications are defined for each possible day of the application.The problem is solved by a heuristic two-stage algorithm. In the first stage we assume that applications for leave are approved, resolve capacity conflicts and arrange applications for leave to get a feasible solution with a high benefit. In the second stage we try to improve the gained feasible solution. Computational results show, that instances with up to 10,000 drivers can be solved within a reasonable amount of time.

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“…Though the cases of train or underground crew rostering are not that different from bus driver rostering, they have also been tackled on a cyclic basis (Caprara et al 1997(Caprara et al , 1998Dornberger et al 2008;Hartog et al 2009;Lezaun et al 2006;Sodhi and Norris 2004).…”

Section: Introductionmentioning

confidence: 99%

Bi-objective evolutionary heuristics for bus driver rostering

2009

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The Bus Driver Rostering Problem (BRP) refers to the assignment of drivers to the daily crew duties that cover a set of schedules for buses of a company during a planning period of a given duration, e.g., a month. An assignment such as this, denoted as roster, must comply with legal and institutional rules, namely Labour Law, labour agreements and the company's regulations. This paper presents a new bi-objective model for the BRP, assuming a non-cyclic rostering context. One such model is appropriate to deal with the specific and diverse requirements of individual drivers, e.g. absences. Two evolutionary heuristics, differing as to the strategies adopted to approach the Pareto frontier, are described for the BRP. The first one, following a utopian strategy, extends elitism to include an infeasible (utopic) and two potential lexicographic individuals in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance was studied through computational tests on BRP instances generated from the solution of integrated vehicle-crew scheduling problems, along with the rules of a public transit company operating in Portugal. This research shows that both methodologies are adequate to tackle these instances. However, the second one is, in general, the more favourable. In reasonable computation

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Decision support for crew rostering at NS

Cited by 31 publications

References 6 publications

Column generation with dual inequalities for railway crew scheduling problems

Column generation with dual inequalities for railway crew scheduling problems

Socially acceptable annual holiday planning for the crew of a local public transport company in Germany

Bi-objective evolutionary heuristics for bus driver rostering

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