Optimise repair strategy selection and repair knowledge sharing to support aero engine design.

Recent growth in aviation industry, large civil jet engines OEMs (Original Equipment Manufacturer) and MROs ((Maintenance, Repair and overhaul)) have emphasised on decreased profits, poor technology selections and maintenance focused design. This has generated service based approach in their selling, offering all customers’ requirements, known as servitisation. The servitisation has increased profits but did not solve the challenges of poor technology selection and design. The difficulties involved within servitisation entails rationalised decision making often with high risk and very limited information. This thesis assesses the most suitable Multi-criteria decision making (MCDM) in concurrence with OEMs and MRO focus groups that recognises the industrial requirements and proposed a novel selection method which is an AHP algorithm based on MCDM in efforts to address business KPIs in aero engine servitisation. This AHP algorithm based MCDM develops an optimised repair process/technology selection framework which is called ORSS (Optimised Repair Selection Strategy). The ORSS applies the business KPIs (Quality Cost Delivery) as a selection criteria combined with the repair engineer's requirements and expert's evaluation of processes/technologies based on a component and its damage-mode to provide the optimised repair process/technology selection that also compliments the components lifecycle repair strategy. A structured knowledge sharing framework has also been developed. This consists of the information that the designers can update to help repair teams to become more effective and efficient in repair and services critical information tasks. These frameworks were validated successfully by experts within the design, repair and service teams at Rolls Royce. These frameworks have shown high levels of improvements in repair process selection and the key knowledge sharing for designs.Engineering and Physical Sciences (EPSRC)PhD in Manufacturin

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac