OPERATIONS RESEARCH AND OPTIMIZATION - 2019/0

Module Overview

This module introduces a variety of commonly used techniques from Operations Research.  The module leads to a deeper understanding of linear programming problems and the theory that underpins their solving.  Tools such as the Simplex Method are presented and an introduction to nonlinear optimisation methods is also provided.  This module supports and complements other modules where optimisation and constrained optimisation is considered.

Module provider

Mathematics

Module Leader

FISHER David (Maths)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

Vector Calculus (MAT1005) or Linear Algebra and Vector Calculus (MAT1037) Linear Algebra (MAT1034) or Linear Algebra and Vector Calculus (MAT1037)

Module content

• 
  Indicative content includes:
  
  
  
  
  • Problem formulation for linear programming problems;
  
  
  • Simplex Method and sensitivity analysis;
  
  
  • Duality and complementary slackness;
  
  
  • Theory and applications of the Transportation Algorithm;
  
  
  • Convex sets, convex functions, concave functions;
  
  
  • Nonlinear optimization and conditions for local/global optima;
  
  
  • Lagrange multipliers and Lagrange Multiplier Theory.
  
  
  • Lagrangian duality.
  
  
  
  

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

·         Ability to formulate linear programming problems and to use their decision-making skills to identify the most appropriate method of solution. 

·         Subject knowledge through explicit and implicit recall of key definitions and theorems as well as interpreting this theory.

·         Understanding and application of subject knowledge to solve constrained optimisation problems.

 

Thus, the summative assessment for this module consists of:

·         One two-hour examination (three answers from four contribute to exam mark) at the end of the semester; worth 80% of module mark.

·         One in-semester test; worth 20% of module mark.

 

Formative assessment and feedback

Students receive individual written feedback via a number of marked formative coursework assignments over an 11-week period.  The lecturer also provides verbal group feedback during lectures.  (Occasionally group feedback may be provided online when applicable.)

Module aims

• Introduce students to linear programming, the Simplex Method and the Transportation Algorithm.
• Enable students to solve linear programming problems as primal problems or by using duality.
• Illustrate introductory theory for nonlinear programming problems that have equality constraints by demonstrating the application of Lagrange Multiplier Theory and to enable students to solve similar optimisation problems. Introduce students to Lagrangian duality.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Give a detailed introduction to formulating linear and nonlinear programming problems as well as discussing an array of optimisation methods (and underpinning theory) used for solving such problems.


• Ensure experience is gained (through demonstration) of the methods typically used to solve constrained optimisation problems so that students can later apply their own decision-making to solve any viable programming problem that they encounter.

• 31 lectures/tutorials, 2 labs

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAT2009

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.