Contact Dr Ken McNaught
- Email: k.r.mcnaught@cranfield.ac.uk
Areas of expertise
- Operational Analysis and Simulation
- Safety, Resilience, Risk & Reliability
Background
Dr Ken McNaught is a Senior Lecturer in operational research, with expertise in probabilistic decision analysis, particularly Bayesian networks and decision network influence diagrams, and in simulation modelling.
Ken has a BSc (Hons.) in Physics and Astronomy from Glasgow University, an MSc in Operational Research from Strathclyde University and a PhD in Operational Research from Cranfield University. He spent almost three years in the automotive industry with Austin Rover before joining Cranfield in 1988.
Current activities
Ken's research interests encompass:
probabilistic approaches such as Bayesian networks, influence diagram decision networks, decision trees, Markovian models and probabilistic risk analysis
simulation including system dynamics, hybrid simulation and statistical aspects of simulation modelling.
Ken works in application areas as diverse as:
reliability, maintenance and risk
fault diagnosis and prognostic modelling
intelligence analysis and course of action assessment
adversarial risk analysis
deception detection
information fusion
value of information assessment
emergency preparedness.
Recent EPSRC funded projects have included
Making Sense - concerned with decision support in the intelligence analysis domain
KT Box - where Ken's contribution was concerned with predictive maintenance modelling.
Ken currently supervises three PhD students and is the Postgraduate Research Coordinator for science and engineering students at Cranfield University at Shrivenham.
Clients
EPSRC
Dstl
Centre for Defence Enterprise (formerly the MOD's Research Acquisition Organisation)
Defence Equipment and Support
Optimized Systems and Solutions (OSyS)
BAE Systems
Royal Norwegian Navy
Publications
Articles In Journals
- Read J, McNaught K, Hazael R & Critchley R. (2024). Evaluation of soft tissue simulant performance against economic and environmental impact. Environmental Science: Advances, 3(4)
- M. Isaksen BG & McNaught KR. (2023). Towards a better framework for estimative intelligence – addressing quality through a systematic approach to uncertainty handling. Intelligence and National Security, 38(7)
- Collett G, Ladyman M, Temple T, Hazael R & McNaught K. (2023). Introducing Bayesian belief updating as a method to counter improvised explosive devices: a qualitative case study on identifying human behaviours associated with explosive chemical precursor diversion. Security Journal, Available online 21st August 2023
- Green RN, McNaught KR & Saddington AJ. (2022). Engineering maintenance decision-making with unsupported judgement under operational constraints. Safety Science, 153(September)
- M. Isaksen BG & McNaught KR. (2019). Uncertainty handling in estimative intelligence – challenges and requirements from both analyst and consumer perspectives. Journal of Risk Research, 22(5)
- Boutselis P & McNaught K. (2019). Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context. International Journal of Production Economics, 209
- Swinerd C & McNaught KR. (2015). Comparing a simulation model with various analytic models of the international diffusion of consumer technology. Technological Forecasting and Social Change, 100
- Swinerd C & McNaught KR. (2014). Simulating the diffusion of technological innovation with an integrated hybrid agent-based system dynamics model. Journal of Simulation, 8(3)
- Swinerd C & McNaught KR. (2012). Design classes for hybrid simulations involving agent-based and system dynamics models. Simulation Modelling Practice and Theory, 25
- McNaught K & Chan A. (2011). Bayesian networks in manufacturing. Journal of Manufacturing Technology Management, 22(6)
- Alam FM, McNaught KR & Ringrose TJ. (2006). An Artificial Neural Network Based Metamodel for Analysing a Stochastic Combat Simulation. International Journal of Enterprise Information Systems, 2(4)
- Alam FM, McNaught KR & Ringrose TJ. (2004). A comparison of experimental designs in the development of a neural network simulation metamodel. Simulation Modelling Practice and Theory, 12(7-8)
- McNaught KR. (2002). Markovian models of three‐on‐one combat involving a hidden defender. Naval Research Logistics (NRL), 49(7)
- McNaught KR. (1999). The effects of splitting exponential stochastic Lanchester battles. Journal of the Operational Research Society, 50(3)
Conference Papers
- Boutselis P & McNaught K. (2014). Finite-time horizon logistics decision making/Newsvendor’s problems: consideration of a wider set of factors
- McNaught KR. (2014). Probabilistic influence diagrams for modelling influence operations
- McNaught KR & Sutovsky P. (2012). Representing variable source credibility in intelligence analysis with Bayesian networks
- McNaught KR & Sutovsky P. (2012). Evidence marshalling with inference networks: An application to homeland security
- McNaught KR. (2011). Detecting deception within a probabilistic modelling framework
- McNaught KR, Zagorecki A & Garcia Perez A. (2011). Knowledge elicitation for predictive maintenance modelling with Bayesian networks
- McNaught KR & Zagorecki A. (2010). Developing a decision analytic framework based on influence diagrams in relation to mass evacuations
- McNaught KR & Zagorecki A. (2009). Using dynamic Bayesian networks for prognostic modelling to inform maintenance decision making
- McNaught K & Zagorecki A. (2009). Prognostic Modelling with Dynamic Bayesian Networks
- Chan A & McNaught KR. (2008). Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure
- McNaught KR, Sastry VVSS & Ng B. (2005). Investigating the use of Bayesian networks to provide decision support to military intelligence analysts
- Alam FM, McNaught KR & Ringrose TJ. (2004). Using morris' randomized oat design as a factor screening method for developing simulation metamodels
- Alam FM, McNaught KR & Ringrose TJ. (2004). Using Morris' Randomized Oat Design as a Factor Screening Method for Developing Simulation Metamodels
- Ringrose TJ, Alam MF & McNaught KR. (2002). Investigating Appropriate Experimental Designs for Neural Network Simulation Metamodels
- McNaught KR, Clifford SL, Vaughn ML, Fogg AJB & Foy MA. (2001). A Bayesian belief network for lower back pain diagnosis.
- McNaught KR. (2001). Markov chain models of one-on-one combat
- McNaught KR. (2000). Bayesian Belief Networks and simulation modelling
Books
- McNaught KR & Zagorecki AT. (2011). Modelling Techniques to Support the Adoption of Predictive Maintenance In Ng I, Parry G, McFarlane D, Wild PJ & Tasker P (eds), Complex Engineering Service Systems. Springer London.
- McNaught KR. (2007). A review of Bayesian networks applied to reliability and maintenance modelling. In Martins H, Wang W & Sharples S (eds), Tony Christer 1940-2006: An Incredible Man. University of Salford.
- McNaught KR. (2001). An introduction to Bayesian belief networks In Ranyard J(ed.), OR 43 Keynote Papers. Operational Research Society.
- Bowen KC & McNaught KR. (1996). Mathematics in Warfare - Lanchester Theory In Fletcher J(ed.), The Lanchester Legacy (3). Coventry University Press.
