Skip to navigation | Skip to main content | Skip to footer
Menu
Menu

COMP37310: Management Support Systems (2008-2009)

This is an archived syllabus from 2008-2009

Management Support Systems
Level: 3
Credit rating: 20
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Lecturers: Ludi Mikhailov
Course lecturer: Ludi Mikhailov

Additional staff: view all staff
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 w1-5,7-12 Lecture 1.4 Mon 11:00 - 13:00 -
Sem 2 w19-26,30-33 Lecture 1.4 Tue 10:00 - 12:00 -
Assessment Breakdown
Exam: 70%
Coursework: 30%
Lab: 0%

Introduction

The course will consider the basic principles of standard decision support systems (DSS) and the main intelligent decision support technologies - knowledge-based systems, fuzzy logic systems, neural networks and genetic algorithms, as well as some techniques for combining them in the framework of the soft computing.

Aims

The main aim of this course unit is to introduce students to the managerial decision-making process and its support by different types of computerised systems, emphasising some of the underlying mathematical theories and methods. The students will also learn how to apply different decision making methods for solving practical managerial problems and how to develop simple managerial decision-support systems by using some programming shells.

Learning Outcomes

On successful completion of this course unit, students should be able to:
Give clear evidence of understanding the main phases of the decision-making process and its computerised support, the modelling techniques and the solution methods that form the core of today's decision support systems.
Exhibit a good knowledge of the main types and components of conventional and intelligent decision support systems.
Apply a range of higher-level cognitive skills including analysis, synthesis and evaluation.
Utilise both qualitative and quantitative problem-solving skills.
Develop and demonstrate practical skills in applying different solution methods and computer-based tools for various practical decision-making problems.


Assessment of Learning outcomes

Examination: 70%, Coursework: 30%.
The coursework will consist in critical analysis and assessment of specific managerial problems, which require decision-making support and solving some of them. The students will be required to submit a short report on their findings.

Syllabus

Decision making, systems, modelling and support.
Introduction to DSS: characteristics and capabilities of DSS, components of DSS.
Group DSS and executive support systems.
Mathematical methods for decision making; classical optimisation techniques; linear programming.
Decision analysis: decision making under risk and uncertainty; decision making based on expected utility.
Multiple criteria decision making.
Web-based decision support systems.
Knowledge based decision support; fundamentals of expert systems. Knowledge engineering, acquisition and representation. Reasoning in knowledge based systems.
Basic concepts of fuzzy set theory; fuzzy decision making.
Basic concepts of neural networks: network structures, supervised and unsupervised learning; applications in decision making.
Basic concepts of genetic algorithms and evolutionary algorithms.
Intelligent decision support systems. Hybrid intelligent systems: genetic algorithms for fuzzy systems; neuro-fuzzy systems.

Reading List

Title: Applied Decision Analysis
Author: D. Bunn
ISBN:
Publisher: McGraw-Hill
Edition:
Year: 1984


Title: Decision Support Systems and Intelligent Systems
Author: E. Turban,, J. Aronson
ISBN:
Publisher: Prentice Hall
Edition: 6th
Year: 2001