IB20710 Mathematical Programming 2
Introductory description
This is an elective module available for nonWBS students.
This module addresses further theoretical and practical problems of mathematical programming, based on the prerequisite knowledge of linear programming and the duality theory. It provides an introduction to the world of discrete and nonlinear optimization with coverage of application context, theoretical basis and methodological skills.
Module aims
This module addresses further theoretical and practical problems of mathematical programming, based on the prerequisite knowledge of linear programming and the duality theory. It provides an introduction to the world of discrete and nonlinear optimization with coverage of application context, theoretical basis and methodological skills.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
This module includes coverage of theoretical and practical aspects of mathematical programming. In particular it covers: linear programming problems with integer variables; the branchandbound algorithm; dynamic programming; network optimisation; approximation algorithms; use of basic software for solutions of optimisation problems.
Learning outcomes
By the end of the module, students should be able to:
 Identify the business problems that can be modelled using optimisation techniques and formulate them in a suitable mathematical form.
 Apply optimisation techniques to the solution of the problems using spreadsheets and other appropriate software.
 Report on the meaning of the optimal solution in a manner suited to a business context.
 List and challenge the assumptions underpinning each of the key models studied.
 Reflect critically on the limitations of each of the models studied.
 Reflect critically on the limitations of each of the models studied.
Indicative reading list
Recommended references:
 Winston, Operations Research: Applications and Algorithms, 4th Ed., 2004 (or later)
 Hillier and G. Lieberman, Introduction to Operations Research, 9th Ed., 2010 (or later)
 H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, Dover Publications, 1998.
Basic terminology and techniques can also be found in the textbooks below:  Anderson, Sweeney and Williams, An Introduction to Management Science, (any edition), West
 Taylor, Introduction to Management Science, (any edition), Prentice Hall
 Taha, Operations Research: An introduction. (Any addition)
Subject specific skills
Spreadsheet modelling skills.
Transferable skills
Model a business optimisation problem and construct spreadsheets to solve an optimisation problem.
Study time
Type  Required 

Lectures  8 sessions of 2 hours (16%) 
Seminars  8 sessions of 1 hour (8%) 
Private study  30 hours (30%) 
Assessment  46 hours (46%) 
Total  100 hours 
Private study description
Private Study.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D
Weighting  Study time  

Individual Assignment  30%  14 hours 
Examination  70%  32 hours 

Feedback on assessment
Feedback provided via my.wbs.
Prerequisites
To take this module, you must have passed:
Courses
This module is Core for:

USTAY602 Undergraduate Mathematics,Operational Research,Statistics and Economics
 Year 2 of Y602 Mathematics,Operational Research,Stats,Economics
 Year 2 of Y602 Mathematics,Operational Research,Stats,Economics
This module is Optional for:
 Year 2 of UCSAI1N1 Undergraduate Computer Science with Business Studies
This module is Option list A for:

USTAG302 Undergraduate Data Science
 Year 2 of G302 Data Science
 Year 2 of G302 Data Science
This module is Option list B for:

UCSAG500 Undergraduate Computer Science
 Year 2 of G500 Computer Science
 Year 2 of G500 Computer Science

UCSAG503 Undergraduate Computer Science MEng
 Year 2 of G503 Computer Science MEng
 Year 2 of G503 Computer Science MEng

UMAAG100 Undergraduate Mathematics (BSc)
 Year 2 of G100 Mathematics
 Year 2 of G100 Mathematics
 Year 2 of G100 Mathematics
 Year 2 of UMAAG103 Undergraduate Mathematics (MMath)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 2 of GG14 Mathematics and Statistics
 Year 2 of GG14 Mathematics and Statistics