MSc Business Analytics: Operational Research and Risk Analysis / Course details
Year of entry: 2019
Course unit details:
Mathematical Programming and Optimisation
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 1|
|Offered by||Alliance Manchester Business School|
|Available as a free choice unit?||No|
This course covers the main mathematical programming and optimisation methods, including: linear, non-linear, integer and dynamic programming. The course also provides an introduction to meta-heuristics. Emphasis is placed on solving managerial optimisation problems by using software tools such as Excel Solver.
The main aim of this course is to familiarise students with the theory and applications of mathematical programming and optimisation methods. The course aims to provide students with an understanding of the basic mathematical principles and the main technical and computational skills, required for application of various optimization methods in management and business-related areas.
At the end of the unit students should be able to understand the main optimization approaches and their applications for solving managerial decision problems. Students should be able to critically analyse and model appropriate decision problems and solve them analytically, or by using the Excel Solver. They will learn to present solutions and arguments in textual and oral forms, both individually and in groups.
70% Exam (closed book, 2.5 hours)
30% Coursework (group presentations)
- Informal advice and discussion during a lecture, seminar, workshop or lab.
- 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.
The CORE text is:
HILLIER, F and LIEBERMAN, G (2004 or any later edition), Introduction to Operations Research with CD-Rom, McGra Hill
TALBI, El-Ghazali (2009), Metaheuristics: from design to implementation (e-book available through the library)
(some of these may be available via the Blackboard site for this unit)
Burke, E. K. and Kendall, G. (2005/6) Search Methodologies Introductory Tutorials in Optimization and Decision Support Techniques, Springer
Garner, S.G and Gass, S, I. (1999) Stigler’s diet problem revisited. OR Chronicle 1- 13
Hastings, N.A.J (1988) Dynamic Programming with Management Applications, The Butterworth Group, England
Johnson, D and McGeogh, L.A. (1995) The Travelling Salesman Problem: A Case Study in Local Optimization in: Local Search in: Combinatorial Optimization, Aarts E and Lenstra J.K (eds.), John Wiley and Sons, London, 1997, 215-310.
Orman A.P and Williams, H.P (2004) A Survey of Different Integer Programming Formulations of the Travelling Salesman Problem. LSE Working Paper: LSEOR 04.67
Suman, B and Kumar, P (2006) A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society No. 57, 1143–1160.
Waters, D. (2007) Quantitative Methods for Business. 4th ed. Prentice Hall.
Williams, H. P. (1999) Model building in mathematical programming, Chichester, John Wiley & Sons
|Scheduled activity hours|
|Assessment written exam||2.5|
|Independent study hours|
|Dong Xu||Unit coordinator|
Informal Contact Method
Peer Assisted Study Sessions