1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. INDUSTRIAL ENGINEERING DEPARTMENT
  4. 1261 Industrial Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ENM 501OPTIMIZATION TECHNICS I3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Compulsory
Course Objective In this course the modelling of real life optimization problems and their solutions methods are examined in detail
Course Content Linear programming problems, integer programming problems, knapsack problems, location allocation problems, assembly line balancing problems, traveling salesman problems, scheduling problems
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Understand the concept of modelling
2models the real life problems
3Solve the models by using GAMS

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
LO 0015432211114
LO 0025431111114
LO 0032422111111
Sub Total121285433339
Contribution4432111113

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Assignments4520
Mid-terms16060
Final examination17373
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Fall1ÖZCAN MUTLU


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
ENM 501 OPTIMIZATION TECHNICS I 3 + 0 1 Turkish 2023-2024 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. ÖZCAN MUTLU mutlu@pau.edu.tr MUH A0457 %
Goals In this course the modelling of real life optimization problems and their solutions methods are examined in detail
Content Linear programming problems, integer programming problems, knapsack problems, location allocation problems, assembly line balancing problems, traveling salesman problems, scheduling problems
Topics
WeeksTopics
1 Linear Programming
2 Linear Programming
3 Linear Programming
4 Linear Programming
5 Integer Programming
6 Integer Programming
7 Integer Programming
8 Integer Programming
9 Mid-term Exam
10 GoalProgramming
11 GoalProgramming
12 Non-linear Programming
13 Non-linear Programming
14 Project Presentation
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes