Total Credits: 10
Level: Level 3
Target Students: Part II undergraduate students in the School of Computer Science. Also available to students from other Schools with the agreement of the module convenor. Available to JYA/Erasmus students.
|Autumn||Assessed by end of Autumn Semester|
Prerequisites: Knowledge of the basics of: linear algebra and calculus is desirable.
|G52ADS||Algorithms and Data Structures|
|G51PRG||Introduction to Programming|
Summary of Content:
This module is part of the Modelling and Optimisation theme in the School of Computer Science.
The module covers a range of operations research techniques, particularly optimisation, in order to tackle a range of real-world problems. Operations research (OR) is a discipline that uses modelling techniques, analytics and computational methods to solve complex problems in industry and business with the aim of helping to make better decisions. By using operation research techniques to analyse complex situations, decision-makers are able to make more effective decisions and build more productive systems. This module covers a range of operations research techniques including: Introduction to OR, Linear Programming, Spreadsheet Modelling, The simplex Method, Network Optimisation, Integer Programming, Multi-objective Optimisation and Non-linear Programming. Students learn to interpret and create formal models of optimisation problems and then to develop computer-based solutions by means of spreadsheets and programming style systems. Techniques are explained using numerical examples and their application is illustrated using a number of case studies and software tools (e.g. optimisation solvers such as Excel, Lingo, Gams and others).Module Web Links:
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||1 per week||2 hours|
|Computing||11 weeks||1 per week||1 hour|
Method of Assessment:
|Exam 1||50||1 Hour 30 Mins Written examination (problem modelling/solving)|
|Coursework 1||30||Apply modelling and optimisation solvers to tackle and range of real-world problems|
|Practical||20||Weekly practicals (and their report) on the use of optimisation solvers|
Dr D Landa Silva
Education Aims: To develop an understanding of operations research techniques with emphasis on theory, applications and computations. To develop the skills for modelling a range of decision and optimisation problems in business and industry using mathematical models. To implement operation research techniques to solve specific optimisation problems using optimisation software tools.
Learning Outcomes: Knowledge and Understanding: The strengths and weaknesses of computer tools, applications and other resources. Applied mathematics and formal methods in the computer science context. Intellectual Skills: Apply and deploy mathematical ability, practices and tools. Understand complex ideas and relate them to specific problems or questions. Professional Practical Skills: Program in various paradigms. Evaluate available tools, applications, algorithms and data structures, and select those that are fit for purpose within a given domain. Transferable Skills: Solve problems. Utilise mathematics.
Offering School: Computer Science
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