Optimisation in network asset management

Boudemagh, N

Abstract

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.

Citation

Boudemagh, N. Optimisation in network asset management. (Thesis). Salford : University of Salford

• Thesis Type: Thesis
• Deposit Date: Oct 3, 2012
• Award Date: Jan 1, 2007