MSc Business Analytics: Operational Research and Risk Analysis / Course details

Year of entry: 2024

Course unit details:
Risk, Performance and Decision Analysis

Course unit fact file
Unit code BMAN60092
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No


This course is designed to provide an advanced level of introduction to the topics in decision analysis and performance improvement. It consistent of two main parts: risk analysis (RS) and decision analysis (DS) under uncertainty, multiple criteria decision analysis (MCDA), data envelopment analysis (DEA) and multiple objective linear programming (MOLP). The emphasise of the course is on how data and human judgement s can be combined to support decision and performance analysis in a robust way and what models, methods, algorithms and software systems can be used in the process to improve the quality of the analysis.


BMAN60092 Programme Req: BMAN60092 is only available as a core unit to students on MSc Business Analytics, and as an elective to students on MSc Quantitative Finance and MSc Data Science (Business & Management pathway)


This course unit covers risk, performance and decision modelling and analysis, including risk modelling and assessment, both single and multiple criteria decision modelling and analysis, data envelopment analysis and multiple objective optimisation. Emphasis will be placed on the integrated applications of these methods and tools to performance and efficiency analysis and planning. The aim is to familiarise students with the applications of decision modelling and performance analysis methodologies

Learning outcomes

At the end of the course unit students should be familiar with concepts, methods and tools for decision tree analysis, multiple criteria decision analysis, data envelopment analysis and multiple objective optimisation, which they can apply to support decision making and deal with performance assessment and efficiency analysis problems. They should also be able to use appropriate software tools such as Excel and IDS Multicriteria Assessor.

Teaching and learning methods

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

Assessment methods

70% Exam

30% group project (including group presentation and group report of the project)


Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.
  • Online exercises and quizzes delivered through the Blackboard course space.
  • Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.
  • Written and/or verbal comments on assessed or non-assessed coursework. Written and/or verbal comments after students have given a group or individual presentation.
  • Generic feedback posted on Blackboard regarding overall examination performance.


Recommended reading

Hillier, F.S. & Lieberman, G.J. (2015) Introduction to Operations Research 10th Edition, McGraw-Hill, Precinct. Earlier or later editions are fine. Search topics in Index of the book to find the right pages to read about the relevant topics.

Belton, V., Stewart, T. J. (2002), Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers: Dordrecht.

Keeney, R.L. and Raiffa, H. (1993), Decision with Multiple Objectives: Preference and Value Tradeoffs. Cambridge University Press.

Cooper, W. W, Seiford, L. M. and Tone, K. (2007), Data Envelopment analysis: a comprehensive text with models, applications, references and DEA Solver software. 2nd edition, Springer.

Liu G. P., Yang J. B. and Whidborne, J. F. (2002), Multiobjective Optimisation and Control. Engineering Systems Modelling and Control Series, Research Studies Press Limited, Baldock, Hertfordshire, England.

Saaty, T. L. (1988), The Analytic Hierarchy Process. University of Pittsburgh, 1988.

Sen, P. and Yang, J. B. (1998), Multiple Criteria Decision Support in Engineering Design, Springer. London, ISBN 3540199322.

Yang, J. B. (2001), Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty, European Journal of Operational Research, Vol. 131, No.1, pp.31-61.

Yang, J. B. and Xu, D. L. (2002), On the evidential reasoning algorithm for multi-attribute decision analysis under uncertainty, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol.32, No.3, pp.289-304.

Xu, D. L. and Yang, J. B. (2003), Intelligent decision system for self-assessment, Journal of Multiple Criteria Decision Analysis, Vol.12, 43-60.

Xu, D. L., McCarthy, G. and Yang, J. B., (2006) Intelligent decision system and its application in business innovative capability assessment, Decision Support Systems, Vol.42, pp.664-673.

Yang, J. B., Wang, Y. M., Xu, D. L. and Chin, K. S. (2006), The evidential reasoning approach for MCDA under both probabilistic and fuzzy uncertainties, European Journal of Operational Research, Vol. 171, No.1, pp.309-343.

Yang, J. B., Wong, Y. H., Xu, D. L. and Stewart, T. J. (2009), Integrating DEA-oriented performance assessment and target setting using interactive MCDA methods, European Journal of Operational Research, Vol.195, pp.205–222.

Yang, J. B., Xu, D. L., Xie, X. L. and Maddulapalli, A.K. (2011), Multicriteria evidential reasoning decision modelling and analysis – prioritising voices of customer, Journal of the Operational Research Society, Vol.62, pp.1638–1654.

Yang, J. B. and Xu, D. L. (2013), Evidential reasoning rule for evidence combination, Artificial Intelligence, Vol.205, pp.1-29, 2013.


Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 33
Independent study hours
Independent study 115

Teaching staff

Staff member Role
Jian-Bo Yang Unit coordinator

Additional notes

Informal Contact Method

  • Office Hours

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