Modelling rail track deterioration and maintenance: current practices and future needs

As commercialisation and privatisation of railway systems reach the political agendas in a number of countries, including Australia, the separation of infrastructure from operating business dictates that track costs need to be shared on an equitable basis. There is also a world-wide trend towards increased pressures on rail track infrastructure through increases in axle loads and train speeds. Such productivity and customer service driven pressures inevitably lead to reductions in the life of track components and increases in track maintenance costs. This paper provides a state-of-the-art review of track degradation modeling, as well as an overview of track maintenance decision support systems currently in use in North America and Europe. The essential elements of a maintenance optimisation model currently under development are also highlighted

Higher education stimulating creative enterprise

This report summarises the research undertaken by the Business & Community School at the University for the Creative Arts (UCA), analysing ways that higher ediucation (HEIs) can support, and indeed stimulate, the creative economy. The research, in collaboration with the Arts University College Bournemouth (AUCB) and the University of Winchester, serves as a mere snapshot of the numerous ways that Universities engage with the diverse industries under the 'creative' nomenclature and of the very real and poistive ways that the higher education sector contributes to the growth of the creative economy in thhe UK

The optimisation of a strategic business process

The optimisation of a Tendering Process for Warship Refit Contracts is presented. The Pre Contract Award process (PCA) involves all the activities needed to successfully win a Refit Contract, e.g. estimating, planning, tendering and negotiation. Process activities and information flows have been modelled using Integrated computer aided manufacturing DEFinition methodology (IDEF0) and a Design Structure Matrix (DSM) with optimisation performed via a Genetic Algorithm (DSM-GA) search technique [1]. The aim of the DSM-GA is to provide the user with an enhanced sequence of performing process activities. A new process was extracted from the optimised solution, showing an improved sequence with reduced iteration and planned activity concurrency based on carefully considered information requirements. This is of practical benefit to enhance understanding and to provide a guide to implementation. The approach suggests an enhanced sequence of process activities, based on information requirements, and can lead to improved business practice. This Paper discusses the potential benefits and limitations of this approach in a practical setting

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme

Multi-objective optimisation of machine tool error mapping using automated planning

Error mapping of machine tools is a multi-measurement task that is planned based on expert knowledge. There are no intelligent tools aiding the production of optimal measurement plans. In previous work, a method of intelligently constructing measurement plans demonstrated that it is feasible to optimise the plans either to reduce machine tool downtime or the estimated uncertainty of measurement due to the plan schedule. However, production scheduling and a continuously changing environment can impose conflicting constraints on downtime and the uncertainty of measurement. In this paper, the use of the produced measurement model to minimise machine tool downtime, the uncertainty of measurement and the arithmetic mean of both is investigated and discussed through the use of twelve different error mapping instances. The multi-objective search plans on average have a 3% reduction in the time metric when compared to the downtime of the uncertainty optimised plan and a 23% improvement in estimated uncertainty of measurement metric when compared to the uncertainty of the temporally optimised plan. Further experiments on a High Performance Computing (HPC) architecture demonstrated that there is on average a 3% improvement in optimality when compared with the experiments performed on the PC architecture. This demonstrates that even though a 4% improvement is beneficial, in most applications a standard PC architecture will result in valid error mapping plan

Searching for improvement

Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents