IB20712 Mathematical Programming II
Introductory description
N/A
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; convex sets and functions and their role in optimisation; simple optimality conditions for
nonlinear programming problems; use of spreadsheets for the solution of optimisation problems
Learning outcomes
By the end of the module, students should be able to:
 Apply optimisation techniques to the solution of the problems using spreadsheets andother appropriate software;
 Identify the business problems that can be modelled using optimisation techniques andformulate them in a suitable mathematical form;
 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.
 Report on the meaning of the optimal solutions in a manner suited to a business context.
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  10 sessions of 2 hours (17%) 
Tutorials  9 sessions of 1 hour (8%) 
Private study  36 hours (30%) 
Assessment  55 hours (46%) 
Total  120 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 D3
Weighting  Study time  

Individual Assignment  30%  17 hours 
Online Examination  70%  46 hours 
Exam ~Platforms  AEP

Feedback on assessment
Feedback via my.wbs.
Prerequisites
IB10412 Mathematical Programming I
To take this module, you must have passed:
Postrequisite modules
If you pass this module, you can take:
 IB3J215 Decision Making under Uncertainty
 IB9BS15 Supply Chain Analytics
 IB41115 Decision Making under Uncertainty
 IB35215 Applied Optimization Methods
 IB3K215 Financial Optimisation
Courses
This module is Core for:
 Year 2 of USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

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 Core optional for:
 Year 2 of UMAAG1NC Undergraduate Mathematics and Business Studies
 Year 2 of UMAAG1N2 Undergraduate Mathematics and Business Studies (with Intercalated Year)
This module is Optional for:
 Year 2 of UCSAI1N1 Undergraduate Computer Science with Business Studies

USTAG302 Undergraduate Data Science
 Year 2 of G302 Data Science
 Year 2 of G302 Data Science
 Year 2 of USTAG304 Undergraduate Data Science (MSci)
 Year 2 of USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 2 of GG14 Mathematics and Statistics
 Year 2 of GG14 Mathematics and Statistics
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
 Year 2 of UMAAG105 Undergraduate Master of Mathematics (with Intercalated Year)

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

UMAAG103 Undergraduate Mathematics (MMath)
 Year 2 of G100 Mathematics
 Year 2 of G103 Mathematics (MMath)
 Year 2 of G103 Mathematics (MMath)
 Year 2 of UMAAG106 Undergraduate Mathematics (MMath) with Study in Europe
 Year 2 of UMAAG1NC Undergraduate Mathematics and Business Studies
 Year 2 of UMAAG1N2 Undergraduate Mathematics and Business Studies (with Intercalated Year)
 Year 2 of UMAAGL11 Undergraduate Mathematics and Economics
 Year 2 of UECAGL12 Undergraduate Mathematics and Economics (with Intercalated Year)
 Year 2 of UMAAG101 Undergraduate Mathematics with Intercalated Year