The problem

The running costs of an asset are a function of its capabilities – the more resources that are employed, the greater the operational scope of the asset. In reality, funds are limited; the question then becomes ‘how do we decide where to allocate funds to return the greatest operational capability?’

An assessment of the ‘trade-off’ between the ranges of asset capabilities and their predicted costs is required in order to find feasible operational strategies best suited to address likely operational scenarios.

This problem arises often within the procurement of military assets. For example, currently the Royal Navy is in the process of developing new aircraft carriers, and many questions exist with regard to their specification and consequent operational costs. Many factors will have to be considered during the project lifecycle and decisions are being and will need to be taken - here there is scope for increasing the fidelity of the information upon which decisions will be made by employing mathematical models to capture the complex interactions between decision variables.

“It has been a really great experience being able to take my research and contextualise it within an industrial application. I believe the validity of my PhD work will be improved by the insights I have gained of the current industrial viewpoint and requirements of my research area - reliability modelling; I look forward to completing my thesis with this newfound source of inspiration! I now feel better prepared for life post-PhD, for this I am grateful to LSC for providing me with the opportunity, and my colleagues at LSC over the past six months for their support", said intern Rhys Kearney, University of Salford.

The approach

The combined modelling environment was developed, with a military deployment of land based vehicles as a test case: A troop of vehicles on deployment may undertake missions of varying nature for which spare parts must be carried to repair any systems which might fail whilst away from base.

Cost factors to be considered were the investment required in spare parts and manpower required to undertake preventive maintenance tasks of the fleet to allay failure. The performance of the fleet under different strategies was measured in terms of operational cost, fleet utilisation, and the rates vehicles returned successfully from mission.

The modelling was undertaken in a discrete event simulation package (Witness [1]) incorporating analysis of spare-part supply line delays. Rather than use exponentially distributed failure rate or renewal processes to simulate the system failures, age-dependent intensity processes [2] were coded within the software package enabling the assessment of the effects of an increasing likelihood of failure with age on fleet performance. The reliability of each vehicle was also conditioned on its maintenance history and the load it had experience whilst undertaking missions on varying types of terrain to provide greater realism in the assessment of the reliability data.

References

[1] http://www.lanner.com/en/witness/witness.php
[2] Percy, D.F. and Alkali, B.M. (2006) Generalized proportional intensities models for repairable systems. I.M.A. Journal of Management Mathematics, 17, 171-185.

related resources:

	Capital asset maintenance and support
»	Technical summary

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