Undergraduate - UnitFIT3022 - Intelligent decision support systems

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Level	Undergraduate
Faculty	Faculty of Information Technology
Offered	Not offered in 2012

Synopsis

This unit will give the students an opportunity to solve some concrete decision-making problems, such as resource allocation and investment planning, using different ways of modelling and solving decision support problems of different size and complexity; strategic, tactical and operational problems; problems involving discrete alternatives and problems involving continuous variables; problems whose constraints and goals are precise and problems which need to be further pinned down. The students will be introduced to a high level problem modelling and solving platform which is supported by a variety of solvers. They will use the platform to model and solve some quite complex decision support problems and experiment with different solvers, and search methods.

Outcomes

At the completion of this unit students will have -
A knowledge and understanding of:

• the role of intelligent decision support in organisations;
• decision support paradigms and applications;
• methods for handling certain and uncertain knowledge;
• issues in the design and construction of intelligent decision support systems;
• correctness, precision and scalability;

Developed attitudes that enable them to:

• recognise the value of intelligent decision support within an organisation;
• adopt a critical approach to the choice of decision support method;
• appreciate the impact of data quality, and business constraints on the behaviour of a decision support system;
• appreciate the limitations of formal decision models and the handling of uncertainty.

Developed the skills to:

• choose appropriate decision support methods;
• separate modelling from solving;
• implement simple decision support tools on a high-level software platform;
• combine methods to meet application requirements;
• assess the limitations in scalability and precision of a solution.

Demonstrated the communication and teamwork skills necessary to:

• document and communicate an intelligent decision support model;
• work in a team during model design and implementation stages;
• present a justification for choosing or combining decision support methods.

Assessment

Examination (2 hours): 60%; In-semester assessment: 40%

Chief examiner(s)

Professor Mark Wallace

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Prerequisites

One of (FIT1006, BUS1100, ETC1000, STA1010) and 24 points at level 1

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu.au/units/fit3022