Optimisation (Linear Programming)

The details
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Undergraduate: Level 5
Monday 15 January 2024
Friday 22 March 2024
04 January 2024


Requisites for this module


MA305, MA306

Key module for

BSC N233 Actuarial Science (Including Placement Year),
BSC N323 Actuarial Science,
BSC N323DF Actuarial Science,
BSC N324 Actuarial Science (Including Year Abroad),
BSC N325 Actuarial Science (Including Foundation Year),
BSC L1G1 Economics with Mathematics,
BSC L1G3 Economics with Mathematics (Including Placement Year),
BSC L1G8 Economics with Mathematics (Including Foundation Year),
BSC L1GC Economics with Mathematics (Including Year Abroad),
BSC G1G4 Mathematics with Computing (Including Year Abroad),
BSC G1G8 Mathematics with Computing (Including Foundation Year),
BSC G1GK Mathematics with Computing,
BSC G1IK Mathematics with Computing (Including Placement Year),
BSC I1G3 Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year),
MSCIN399 Actuarial Science and Data Science

Module description

An introduction to the methods of linear programming, including both theoretical and computational aspects.

Module aims

No information available.

Module learning outcomes

By the end of the module, students will be expected to be able to:

  1. Formulate an appropriate linear programming model, from a written description of a problem environment, whose solution would actually solve the problem.

  2. Recognise the scope and limitations of linear programming modelling and appreciate its position within the Operational Research discipline.

  3. Solve any (small) linear programming problem using an appropriate version of the Simplex Algorithm.

  4. Perform sensitivity analysis on an optimal solution.

  5. Use Duality Theory to prove basic theorems of Linear Programming and apply Duality Theory to recognize optimality, infeasibility or unboundedness in a linear program.

  6. Outline the Transportation Simplex Algorithm and find basic feasible solutions.

Module information

Indicative syllabus:

Formulation of linear programming models
Graphical solution
The Simplex Algorithm, Two-Phase Simplex and Revised Simplex
Duality, Complementary Slackness and Dual Simplex
Sensitivity Analysis
Transportation Problem

Learning and teaching methods

Teaching in the School will be delivered using a range of face-to-face lectures, classes, and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.


This module does not appear to have any essential texts. To see non - essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1  23/02/2024   
Coursework   Assignment 2  22/03/2024   
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
20% 80%


Coursework Exam
20% 80%
Module supervisor and teaching staff
Dr Felipe Maldonado, email:
Dr Felipe Maldonado, Dr L Truong



External examiner

Dr Yinghui Wei
University of Plymouth
Dr Murray Pollock
Newcastle University
Director of Statistics / Senior Lecturer
Available via Moodle
Of 20 hours, 20 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information

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