Clemens Kielhauser

Investigation of a static and a dynamic neighbourhood methodology to develop work programs for multiple close municipal infrastructure networks

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.

Performance measures for road managers facing diverse environments

Purpose A key difficulty that plagues benchmarking in the public sector is heterogeneity in the production process. The purpose of this paper is to present a strategy for overcoming that difficulty using physical production models and demonstrate it using road renewal management as an example. Design/methodology/approach A physical production model is used to linking required prices, inputs and exposures to environmental factors to the desired services to be delivered. A measure is derived from this that adjusts for the additional expected costs from operating in a more difficult environments. A case study is used to present methods for addressing specific parameterization issues that arise in an empirical application. Findings The method was found to be implementable and empirically better than naive ratio measures commonly found in practice. Research limitations/implications Data and modeling issues were identified that can be addressed by public supervisors that are expected to greatly improve the quality of the measures. Social implications According to the raw data and simple ratios, a very large degree of inefficiency can potentially be eliminated by applying the recommended measures. In all likelihood the real potential is much smaller, but still significant. Originality/value Most applied benchmarking exercises use simple ratios as KPI’s. These are easily dismissed where environments are heterogeneous. Data envelopment analysis and stochastic frontier analysis are generally difficult to relate to KPI’s. The use of an explicit and specific process model with an engineering content is therefore exceptional.

Determination of intervention programs for multiple municipal infrastructure networks: considering network operator and service costs

ABSTRACT In order to find the optimal intervention program for municipal infrastructure networks (e.g. road, gas or water distribution), a combined methodology is necessary to take into consideration the costs of interventions in terms of owner costs, but also potential losses of service before, during and after the execution of the interventions, and the costs due to the loss of service when interventions are being executed. It is also necessary to take into consideration: 1) intervention cost reductions are possible when interventions are executed on multiple networks simultaneously and 2) interventions on one network can increase the failure probability of another network. This paper presents a methodology to determine optimal intervention programs for multiple infrastructure networks simultaneously using a single mathematical representation, taking into consideration these issues. The service loss is modeled using a new generic model applicable to all networks. The methodology is discussed, and recommendations for future work are provided.

Development of intervention programs for inland waterway networks using genetic algorithms

Abstract 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.