IB207-10 Mathematical Programming 2
Introductory description
This module together with its prerequisite IB104 provides an exciting path to the world of Operational Research, Management Science and Analytics. During the course of the module, students will find out "where" (applications), "what" (knowledge) and "how" (skills) about the subject area.
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 non-linear 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 branch-and-bound algorithm; dynamic programming; network optimization; approximation algorithms; and convexity analysis.
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
- Wayne Winston, Operations Research: Applications and Algorithms, 4th Ed., Cengage Learning, 2022
- Frederick Hillier and Gerald Lieberman, Introduction to Operations Research, 11th Ed., McGraw-Hill Education, 2021
- Bernhard Korte and Jens Vygen, Combinatorial Optimization: Theory and Algorithms. 6th Ed., Springer, 2018
Interdisciplinary
Core module for key interdisciplinary degree (MORSE).
Subject specific skills
Analytically solve optimization problems.
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 (20%) |
| Private study | 36 hours (36%) |
| Assessment | 44 hours (44%) |
| 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 B
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination | 100% | 44 hours | No |
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Feedback on assessment
Feedback provided via my.wbs.
Pre-requisites
To take this module, you must have passed:
Courses
This module is Core for:
- Year 2 of USTA-Y602 Undergraduate Mathematics,Operational Research,Statistics and Economics
This module is Optional for:
- Year 2 of UCSA-I1N1 Undergraduate Computer Science with Business Studies
This module is Option list A for:
- Year 2 of USTA-G302 Undergraduate Data Science
This module is Option list B for:
- Year 2 of UCSA-G500 Undergraduate Computer Science
-
UCSA-G503 Undergraduate Computer Science MEng
- Year 2 of G500 Computer Science
- Year 2 of G503 Computer Science MEng
- Year 2 of UMAA-G100 Undergraduate Mathematics (BSc)
-
UMAA-G103 Undergraduate Mathematics (MMath)
- Year 2 of G100 Mathematics
- Year 2 of G103 Mathematics (MMath)
- Year 2 of USTA-GG14 Undergraduate Mathematics and Statistics (BSc)