Mathematical Programming and Optimisation

Unit code: BMAN60101
Credit Rating: 15
Unit level: Level 7
Teaching period(s): Semester 1
Offered by Alliance Manchester Business School
Available as a free choice unit?: N




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 mathematical programming methods in management and business-related areas.


This course covers the main mathematical programming and optimisation methods, including: linear, non-linear, integer and dynamic programming. Emphasis is placed on solving managerial optimisation problems by using some software tools, such as Excel Solver.

Learning outcomes

At the end of the unit students should be able to understand the main mathematical programming 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.

Assessment Further Information

70% Exam (closed book, 2.5 hours)

30% Coursework (group presentations)

Recommended reading

The  CORE text is:

HILLIER, F and LIEBERMAN, G (2004 or any later edition), Introduction to Operations Research with CD-Rom, McGra Hill

Other readings:

(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 Traveling 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

Feedback methods

  • 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.

Study hours

  • Assessment written exam - 2.5 hours
  • Lectures - 33 hours
  • Independent study hours - 114.5 hours

Teaching staff

Ludmil Mikhailov - Unit coordinator

Dong Xu - Unit coordinator

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