Module Handbook

 

Module Name:

Optimization

Module Level:

Bachelor

Abbreviation, if applicable:

MAT308

Sub-heading, if applicable:

-

Courses included in the

module, if applicable:

-

Semester/term:

6th/ ThirdYear

Module coordinator(s):

Dr. Herry Suprajitno, M.Si.

Lecturer(s):

Dr. Herry Suprajitno, M.Si.

Language:

BahasaIndonesia

Classification within the

curriculum

Compulsory Course/ Elective Studies

Teaching format / class

hours per week during semester:

3hours lectures (50 min / hour)

 

 

Workload:

3hours lectures, 3 hour structural activities, and 3 hours individual study, 13 week per semester, and total 117 hours per semester 3.9 ECTS.

 

 

 

Credit Points:

3

Requirements:

Linier Programming, Multivariable Calculus, Numerical Method

Learning goals/competencies:

General Competence (Knowledge)

Can implement the optimization principle to the problems

Specific Competence:

1.       Explain optimization principles, convex sets characteristics and quadratic form

2.       Using appropriate method to solve the unconstraints problems

3.       Using appropriate method to solve the constraints problems

4.       Using Genetic Algorithm (GA) to solve a problem

5.       Using Simulated Annealing (SA) to solve a problem

6.       Using Ant Colony Optimization (ACO) to solve a problem

7.       Using GA, SA, ACO to solve a problem

Content:

Introduction to optimization, unconstraints optimization, constraints optimization, global optimization.

Soft skill Attribute

Discipline, honesty, teamworkand good communication

Study/exam achievements:

Students are considered to be competent and pass if at least get 40of maximum mark of the exams (UTS dan UAS), structured activity (group discussion).

Final score (NA) is calculated as follow: 20% assignment + 10% softskill + 10% quiz + 25% UTS + 35% UAS

 

Final index is defined as follow:

A : 75 100

AB : 70 - 74.99

B : 65 - 69.99

BC : 60 - 64.99

C : 55 - 59.99

D : 40 - 54.99

E : 0 - 39.99

Forms of Media:

LCD projectors, whiteboards

 

Learning Methods

Lecture, assessments, presentationand group discussion

 

Literature:

1.     Rao, SS, 2009, Engineering optimization : theory and practice, John Wiley and son, New Jersey

2.     Mital KV, 1979, Optimization method in operations research and system analisys, Wiley Eastern Limited, New Delhi

3.     Peressini AL, Sullivan FE, and Uhl JJ, The Mathematics of Nonlinear Programming, 1987, Springer-Verlag, New York

4.     Yang XZ, 2008, Introduction to Mathematical Optimization, Cambridge International Science Publishing, Cambridge

5.     Gen M and Cheng R, 1997, Genetic Algorithm and Engineering Design, John Wiley and sons, New York

6.     Dorigo M dan Stutzle T, 2004, Ant Colony Optimization, MIT Press, Massachusetts

7.     Sundaram, RK, 1996, A First Course in Optimization Theory, Cambridge University Press, New York.

 

 

Notes: