Optimisation in network asset management
Boudemagh, N 2007, Optimisation in network asset management , PhD thesis, Salford : University of Salford.
Restricted to Repository staff only until 01 March 2015.
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This thesis considers the role of mathematical programming in asset management. Large, extensive distribution networks are the focus of the work. In particular, we look at how to determine the optimal policy for project release. Projects relating to the replacement of existing assets and network re-design may be prioritized, given capital rationing and/or performance improvement requirements in a regulated economic environment. We consider the role of two approaches to modelling under uncertainty in determining an optimal policy for project release in network asset management. These are Monte Carlo simulation and fuzzy linear programming. We focus on the maintenance and replacement issues of a large distribution network (network structured system) and consider the application of these modelling approaches to electricity distribution networks. The electricity companies who own the UK electricity distribution networks are under pressure to provide a high quality supply to customers at a minimum cost. For this particular replacement problem, a zero-one integer linear programming model is proposed for selecting an optimal project portfolio, based on the objectives and constraints of the network owner. The modelling approach described in this doctoral study would extend to the financial investment appraisal of capital projects to a broad range of manufacturing and energy-related industries such as power generation, refining in terms of environment issues and water supply. This thesis presents mathematical programming models, using case studies to illustrate some of the appropriate techniques for developing such models. These case study models consider uncertainty and use Monte Carlo simulation to generate more representative results. The models demonstrate the usefulness of Monte Carlo simulation from which we can make recommendations about this and alternative approaches.
|Item Type:||Thesis (PhD)|
|Schools:||Colleges and Schools > College of Business & Law > Salford Business School > Management Science and Statistics|
Colleges and Schools > College of Business & Law > Salford Business School
|Depositing User:||Institutional Repository|
|Date Deposited:||03 Oct 2012 14:34|
|Last Modified:||03 Jan 2015 23:23|
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