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IB207-10 Mathematical Programming 2

Department
Warwick Business School
Level
Undergraduate Level 2
Module leader
Bo Chen
Credit value
10
Module duration
10 weeks
Assessment
100% exam
Study location
University of Warwick main campus, Coventry

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 web page

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
Examination 100% 44 hours No
  • Answerbook Pink (12 page)
  • Graph paper
Feedback on assessment

Feedback provided via my.wbs.

Past exam papers for IB207

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)