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Inland waterways often consist of large numbers of man-made objects to ensure navigability. These objects are of many different types, ages and sizes, and deteriorate in uncountable of different ways. In order to ensure that the deterioration of the objects does not result in a loss of navigability, interventions must be executed. This, however, produces costs, in terms of both labour and material costs and costs of loss of service if the waterway is rendered non-navigable during intervention. In this paper, a methodology is presented to determine optimal multiple time period intervention programmes for inland waterways. The optimal intervention programme is the one that has highest net benefit, i.e. overall benefits minus overall costs, where benefits are the reduction in risk of failure. A genetic algorithm is used to overcome the problem of combinatorial explosion when many objects, in many states, over many time periods are to be considered. The exact formulation of the genome, as well as the genetic fitness function, are presented. They are used to determine an optimal intervention programme for a fictive inland waterway network. The results are presented and discussed, and an outlook is provided on further steps to improve this methodology. [ABSTRACT FROM AUTHOR]

Interventions on infrastructure networks in cities cause disruptions to the services provided by those but also to other networks that have to be at least partially shut down for the interventions executed. Due to these effects, there is substantial benefit to be obtained by grouping interventions on networks that are spatially close to one another. This benefit is principally due to reduced costs of intervention and reduced service disruption. In this paper, two intervention grouping methodologies to develop work programs for infrastructure networks are investigated. The first is based on static, the second is based on dynamic grouping. The two methodologies are investigated by developing work programs on multiple infrastructure networks in an urban area and compared against the same methodologies, albeit without coordination. In the example, interventions on the objects of five different infrastructure networks are grouped based on failure probability of the objects and their closeness. It is found that the dynamic grouping methodology results in work programs that result in a better consideration and prioritisation of objects that are in urgent need for an interventi, while accounting for the synergies that can be created due to efficient coordination. The advantages, disadvantages and future research directions are discussed. [ABSTRACT FROM PUBLISHER]

In this paper, a real option approach to determine the optimal time to execute interventions on rail infrastructure, when it is not known for certain which intervention is to be executed, is presented (i.e. the optimal intervention window). Such an approach is useful in the management of rail infrastructure that belongs to a multi-national rail corridor where multiple railway organizations, ideally, should coordinate their maintenance interventions, years in advance, to minimize service disruptions. The approach is based on an adaptation of the Black and Scholes differential equation model used to value European call options in financial engineering. It is demonstrated by determining the optimal intervention window for infrastructure in a fictive rail corridor. [ABSTRACT FROM AUTHOR]