Syllabus for System Analysis and Optimization
| School/college: School of Electronics and Information Engineering | |
| No. of Course: 1080066 | Course Teacher：Li Li |
| Language：English | Students： Master & Doctor candidates |
| Total Hours：34 | Credits: 2 |
| Number of students :≤ 30 | Semester：First |
| Instructor：Li Li | Examiner：Xu Weisheng |
1. Course Description
System Analysis & Optimization is one of the degree courses of the students major in Control Science and Engineering. Nowadays, automation system has made a big step from the equipment automation to integrated automation system, and from classic control theories to their integration with intelligent optimization. It is necessary to learn and grasp systematical analysis and optimization technologies to follow those developments of automation. The objective of this course to let the students grasp computer intelligence optimization methods, have the ability to apply these methods to solve the optimization problems of manufacturing systems, and develop their analytical and critical thinking ability on the manufacturing system analysis and optimization.
2. Course Objectives and Requirements
1. Course Objectives：
By the end of the course, students will be able to:
(1). Understand the basic principles, theories and methodologies of computational intelligence optimization methods.
(2). Have the abilities to solve the problems in manufacturing systems with computational intelligence optimization methods.
2. Requirements： The students are expected to prepare lectures and finish the assignments with computer programming.
3. Course Arrangement
Chapter 1. Manufacturing System Analysis (14 hour)
l Performance measures of manufacturing system
l Single workstation model
l Single product multi-stage factory model
l Multiple product factory model.
Chapter 2. Manufacturing System Optimization (14 hours)
l Introduction to optimal methods
l Basic concepts, issues, design and realization of intelligent algorithms
l Introduction to simulation-based optimization
Chapter 3. Case Study (6 hours)
l Case study on the scheduling of semiconductor manufacturing
4. Experiment or Computer Operation
None.
5. Teaching Methods
Lectures、Discussions、Case Analysis, etc.
6. Preparatory Course Requirement
Operational Research
7. Learning Outcomes Expected
| Category | Learning Outcomes |
| Master of Knowledge | 1. Understand the basic principles, theories and methodologies of computational intelligence optimization methods. 2. Apply computational intelligence optimization methods to solve the problems in manufacturing systems. |
| Intellectual abilities learned | 1. Have the ability to apply computational intelligence optimization methods to research. 2. Cooperate with colleagues well in a teamwork. |
| Practical skills learned | 1. Use the computers to solve the problems with computational intelligence optimization methods. |
| Personal competences and characters Cultivated | 1. The competence of implementing research independently. 2. Foster the ability of integrated theoretical study and experiments study together |
8. Performance Evaluation: Means & Ratio
| Evaluation Means | Ratio(%) | Link with learning outcomes expected |
| Attendance, Engagement and Homework | 20 | Attendance and Engage in the class, the quality of homework |
| Final Exam | 80 | Knowledge grasp and ability to solve the problems with knowledge |
9. Textbooks and Main Reference Books
[1] Manufacturing Systems Modeling and Analysis. Guy L.Curry, Richard M.Feldman (Eds.), Springer, 2009
[2] Handbook of Memetic Algorithms. Ferrante Neri, Carlos Cotta, and Pablo Moscato (Eds.), Springer, 2011
[3] Production Planning and Control for Semiconductor Wafer Fabrication Facilities—Modeling, Analysis and Systems. Lars Monch, John W.Fowler and Scott J.Mason, Springer, 2013
10. Assignment Requirements
[1] Find related data self-dependently and finish the homework with computers.