TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| INDUSTRIAL ENGINEERING PR. | |
| Code | ENM212 |
| Name | Introduction to Modeling and Optimization |
| Term | 2017-2018 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Üniversite Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ALİ KOKANGÜL |
| Course Instructor |
Doç. Dr. YUSUF KUVVETLİ
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To Define industrial problems and optimize them by using mathematical modelling techniques
Course Content
Identification of industrial problems, establishment of metamatic models, optimum solution derivation and sensitivity analysis by existing computer package programs to established models.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | What is optimization |
| LO02 | Identification of optimization problems |
| LO03 | Verbal description of the problem |
| LO04 | Construct the mathematical model |
| LO05 | Graphical solution of mathematical models |
| LO06 | Linear programming |
| LO07 | Midtermexam |
| LO08 | Graphical solution method to nonlinearl programming models |
| LO09 | Simplex method |
| LO10 | Dual simplex method |
| LO11 | Big-M method |
| LO12 | Sensitivity analysis |
| LO13 | Model building and solution derivation in LINGO package program |
| LO14 | Sensitivity analysis in LINGO program |
| LO15 | Sample optimization applications |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. | |
| PLO02 | - | Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. | |
| PLO03 | - | Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . | |
| PLO04 | - | Gains ability to choose and apply methods and tools for industrial engineering applications. | |
| PLO05 | - | Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. | |
| PLO06 | - | Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. | |
| PLO07 | - | Can access information and to search/use databases and other sources for information gathering. | |
| PLO08 | - | Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. | |
| PLO09 | - | Can use computer software in industrial engineering along with information and communication technologies. | |
| PLO10 | - | Can use oral and written communication efficiently. | |
| PLO11 | - | Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. | |
| PLO12 | - | Has a conscious understanding of professional and ethical responsibilities. | |
| PLO13 | - | Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. | |
| PLO14 | - | Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | What is optimization | Investigation of industrial problems | |
| 2 | Identification of optimization problems | ||
| 3 | Verbal description of the problem | ||
| 4 | Construct the mathematical model | ||
| 5 | Graphical solution of mathematical models | ||
| 6 | Linear programming | ||
| 7 | Midtermexam | ||
| 8 | Graphical solution method to nonlinearl programming models | ||
| 9 | Simplex method | ||
| 10 | Dual simplex method | ||
| 11 | Big-M yöntemi | ||
| 12 | Sensitivity analysis | ||
| 13 | Model building and solution derivation in LINGO package program | ||
| 14 | Sensitivity analysis in LINGO program | ||
| 15 | Sample optimization applications | To find optimum solutions for industrial application problems | |
| 16 | Final exam | ||
| 17 | Final exam |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |