KST412 – Special Topics in Operations Research and Computation

Module designation

Special topics in Operations Research and computation (KST412)

Semester(s) in which the module is taught

7th

Person responsible for the module

Dr. Herry Suprajitno

Language

Indonesian

Relation to curriculum

Compulsory / elective / specialisation

Teaching methods

Lecture, lesson, presentation and group discussion.

Workload (incl. contact hours, self-study hours)

2×170 minutes (2×50 minutes lecture and lesson, 2×60 minutes structural activities, 2×60 minutes self-study) per week for 16 weeks

Credit points

2 CP (3,2 ECTS)

Required and recommended prerequisites for joining the module

Module objectives/intended learning outcomes

General Competence (Knowledge):

Preparing the undergraduate thesis proposal

Specific Competence: students capable of

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

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

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

4.      Using Particle Swarm Optimization (PSO) to solve a problem

5.      Using Artificial Bee Colony (ABC) to solve a problem

6.      Using Firefly Algorithm (FA) to solve a problem

7.      Using Data Mining Concepts to solve a problem

8.      Using Cryptography Concepts to solve a problem

9.      Using Firefly Algorithm (FA) to solve a problem

10.  Developing a proposal which continued into the undergraduated Thesis

Content

Introduction to Operations Research, Transportation Problems, Assignment Problems, Network Analysis, PERT-CPM, Dynamic Programming.

Examination forms

Papers (Proposed Thesis Topics) and Presentation

Study and examination requirements

Students are considered to pass if they at least have got a final score 40 (D).

Final score is calculated as follow: 10% softskill+20% assignment + 10% Quiz + 25% midterm + 35% final exam.

Final index is defined as follow:

Reading list

1.       Gen M dan Cheng R, 2000, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York.

2.       Castro LN, 2006, Fundamentals of Natural Computing, Chapman & Hall, Boca Raton.

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

4.       Lazinica A, 2009, Particle Swarm Optimization, In-Tech, Vienna.

5.       Xin-She Yang, 2010, Firefly Algorithms for Multimodal Optimization, University of Cambridge.

6.       Karaboga  D. dan Akay, B., 2009, A Comparative Study of Artificial Bee Colony algorithm, Applied Mathematics and Computation, 214, 108–132

7.       Han J dan Kamber M, 2006, Data Mining Concepts and Techniques, 2^nd edition, Elsevier, Oxford.

8.       Prasetyo E, 2012, Data Mining Konsep dan Aplikasi Menggunakan Matlab, Penerbit Andi, Yogyakarta.

9.      Stamp, M. dan Low, R M., 2007, Applied Cryptanalysis Breaking Ciphers in the Real World, John Wiley & Sons, New Jersey.