IB10410 Mathematical Programming 1
Introductory description
At the end of the module students will be able to recognise, formulate and solve practical resource allocation and planning problems. Module members will also be able to identify the limitations of the approaches. This module serves as a prerequisite for further modules in integer and nonlinear programming, which are available to students in their second and final years.
Module aims
At the end of the module students will be able to recognise, formulate and solve practical resource allocation and planning problems. Module members will also be able to identify the limitations of the approaches. This module serves as a prerequisite for further modules in integer and nonlinear programming, which are available to students in their second and final years.
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.
Introduction to Operational Research
Introduction to Linear Programming
Introduction to basic algorithms for solving linear programming problems
Practical computer work using a Linear Programming computer package
Formulation methods and Interpretation of solutions
Distribution / transportation models
Introduction to Game Theory
Learning outcomes
By the end of the module, students should be able to:
 Recognise, formulate and solve business optimisation problems.
 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
 WL Winston, Operations Research: Applications and Algorithms, Thompson. (Any edition)
 Hillier and G. Lieberman, Introduction to Operations Research. (Any edition)
 Anderson, Sweeney and Williams, An Introduction to Management Science, West. (Any edition)
 Taylor, Introduction to Management Science, Prentice Hall. (Any edition)
 Taha, Operations Research: An introduction. (Any edition)
Interdisciplinary
Core module for key interdisciplinary degree (MORSE).
Subject specific skills
Analytically solve linear optimisation problems.
Transferable skills
Model a business optimisation problem in a suitable mathematical form and interpret optimal mathematical solutions in the business context.
Study time
Type  Required 

Lectures  5 sessions of 4 hours (20%) 
Seminars  4 sessions of 1 hour (4%) 
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 B1
Weighting  Study time  

Online Examination  100%  46 hours 
~Platforms  AEP

Feedback on assessment
Feedback will be provided via my.wbs.
Courses
This module is Core for:

USTAG302 Undergraduate Data Science
 Year 1 of G302 Data Science
 Year 1 of G302 Data Science
 Year 1 of USTAG304 Undergraduate Data Science (MSci)
 Year 1 of USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

USTAY602 Undergraduate Mathematics,Operational Research,Statistics and Economics
 Year 1 of Y602 Mathematics,Operational Research,Stats,Economics
 Year 1 of Y602 Mathematics,Operational Research,Stats,Economics
This module is Optional for:

UCSAG500 Undergraduate Computer Science
 Year 1 of G500 Computer Science
 Year 1 of G500 Computer Science

UCSAG503 Undergraduate Computer Science MEng
 Year 1 of G500 Computer Science
 Year 1 of G503 Computer Science MEng
 Year 1 of G503 Computer Science MEng
 Year 1 of UCSAI1N1 Undergraduate Computer Science with Business Studies
 Year 1 of USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 1 of GG14 Mathematics and Statistics
 Year 1 of GG14 Mathematics and Statistics
This module is Option list B for:
 Year 1 of UMAAG105 Undergraduate Master of Mathematics (with Intercalated Year)

UMAAG100 Undergraduate Mathematics (BSc)
 Year 1 of G100 Mathematics
 Year 1 of G100 Mathematics
 Year 1 of G100 Mathematics

UMAAG103 Undergraduate Mathematics (MMath)
 Year 1 of G100 Mathematics
 Year 1 of G103 Mathematics (MMath)
 Year 1 of G103 Mathematics (MMath)
 Year 1 of UMAAG106 Undergraduate Mathematics (MMath) with Study in Europe
 Year 1 of UMAAG1NC Undergraduate Mathematics and Business Studies
 Year 1 of UMAAG1N2 Undergraduate Mathematics and Business Studies (with Intercalated Year)
 Year 1 of UMAAGL11 Undergraduate Mathematics and Economics
 Year 1 of UECAGL12 Undergraduate Mathematics and Economics (with Intercalated Year)
 Year 1 of UMAAG101 Undergraduate Mathematics with Intercalated Year