Subject Description Form
Subject Code
ISE5019
Subject Title
Optimization Modeling and Applications
Credit Value
3
Level
5
Pre-requisite/Corequisite/Exclusion
Nil
Objectives
This subject provides students with
Intended Learning
Outcomes
Subject Synopsis/
Indicative Syllabus
1.
the value of optimization and mathematical modeling in real life;
2.
basic modeling techniques to formulate a real problem into a
mathematical model;
3.
various techniques and tools to solve mathematical models;
4.
the ability to apply various optimization models, such as linear
programming and other mathematical programming, to solve practical
problems, especially, logistics problems.
Upon completion of the subject, students will be able to
a.
analyze real-life problems, especially, logistics problems, through the use
of mathematical modeling techniques;
b.
gain familiarity with various modeling techniques to build mathematical
models for real problems;
c.
employ some optimization methods and techniques and apply them to
some practical problems.
1.
Introduction
Introduction to optimization and mathematical modeling: concepts,
modeling, various mathematical models.
2.
Modeling Techniques
Techniques in building mathematical models; Various methods to
interpret a real-world problem in mathematical terms and formats.
3.
Solution Methods
Methods to solve mathematical models: linear programming, integer
programming, and network models.
4.
15.7.2010
Applications
Applications of optimization models and methods: the traveling salesman
problem, the vehicle routing problem, and others.
Teaching/Learning
Methodology
A mixture of lectures, tutorial exercises, and laboratories are used to deliver the
various topics in this subject, some of which are covered in a problem-based
format where the learning objectives are enhanced. Others are covered through
directed study to enhance the students’ “learning to learn” ability. Some case
examples, largely based on consultancy experience, are used to integrate these
topics and thus demonstrate to students how the various techniques are
interrelated and how they can be applied to real-life situations or logistics
operations.
Teaching/Learning
Methodologies
Lecture
Seminars
Project/case studies
Assessment Methods
in Alignment with
Intended Learning
Outcomes
Specific assessment
methods/tasks
Intended Subject Learning Outcomes to
be assessed
a
b
c









%
weighting
Intended subject learning outcomes to
be assessed
a
b
c
1. Exercises
30%



2. Project report
10%



3. Presentation
10%



4. Lab and report
20%



5. Test
30%



Total
100%
Continuous assessment comprises of tasks with individual and group
components, usually several exercises, a mini-project with oral presentation
and written report, a laboratory, and a test. All assessment components require
students to apply and demonstrate what they have learned from the subject to
address issues related to optimization modeling and applications.
Student Study
Effort Expected
Class contact:

Lectures

Laboratory, presentation, test
30 Hrs.
9 Hrs.
Other student study effort:

15.7.2010
Preparation and review, self-study
63 Hrs.

Project report writing
Total student study effort
Reading List and
References
15.7.2010
18 Hrs.
120 Hrs.
1.
Williams, H, P. 1993, Model Building in Mathematical Programming,
John Wiley & Sons
2.
Schrage, L. 1997, Optimization Modeling with Lindo, 5th edn, Thomson
3.
Rardin, R. 2000, Optimization in Operations Research, Prentice Hall
4.
Nash, S and Sofer, A. 1996, Linear and Nonlinear Programming,
McGraw-Hill
5.
Nemhauser, G and Wolsey, L, A. 1999, Integer and Combinatorial
Optimization, John Wiley & Sons
6.
Griva, I, Nash, S, G and Sofer, A. 2009, Linear and Nonlinear
Optimization, Society of Industrial and Applied Mathematics
7.
Alba, E. 2009, Optimization Techniques for Solving Complex Problems,
John Wiley
8.
Floudas, C, A and Pardalos, P, M. 2009, Encyclopedia of Optimization,
Springer