FACULTY OF ENGINEERING

Department of Mechatronics Engineering

IE 311 | Course Introduction and Application Information

Course Name
Quantitative Production Planning
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 311
Fall/Spring
3
0
3
6

Prerequisites
  IE 222 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Service Course
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives Industrial Engineers and information technology professionals need to have an understanding of computational optimization models for production planning and scheduling. This course provides an introduction to the subject by presenting the terminology and concepts needed to understand the important issues. Students can view this course either as a tool for understanding production planning and scheduling via optimization modeling or for understanding optimization modeling by using production planning and scheduling as an example. Course will be followed in two phases; while tactical level production plans are presented in the first, detailed scheduling or operational issues are analyzed in the second phase as lot sizing and scheduling models. Implementation of those models with modeling languages are also studied during the course.
Learning Outcomes The students who succeeded in this course;
  • Will be able to classify the current techniques used in industry for production planning and scheduling
  • Will be able to examine advantages and disadvantages of methods learned during the course
  • Will be able to classify models in literature which are suggested for essential production planning and scheduling problems
  • Will be able to describe and to formulize production planning problems
  • Will be able to solve production planning and scheduling models in computer
Course Description Topics of this course includes, describing production planning and scheduling problems, evaluating the current methods used in industry for production planning and scheduling, analyzing essential production planning and scheduling models in literature coding and solving those models using a modeling language software.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to and motivation for Mathematical Modelling Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Chapter 1
2 Optimization in production and inventory systems Lecture notes
3 Optimization in production and inventory systems Lecture notes
4 Starting with an MRP Model Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Chapter 2
5 Starting with an MRP Model Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Chapter 3
6 Extending to an MRP II Model and Further Improvements Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Chapter 4 and 5
7 Software applications Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Chapter 7
8 Midterm exam
9 MIP algorithms Yves Pochet, Laurance A. Wolsey,.Production Planning by Mixed Integer Programming, Springer, ISBN 9780387299594, Chapters 2 and 3
10 MIP Algorithms Yves Pochet, Laurance A. Wolsey,.Production Planning by Mixed Integer Programming, Springer, ISBN 9780387299594, Chapters 2 and 3
11 Capacitated Lot Sizing Problems and Reformulations M Denizel,H Sural. On alternative mixed integer programming formulations and LPbased heuristics for lotsizing with setup times. Journal of the Operational Research Society (2006) 57, 389–399
12 Capacitated Lot Sizing Problems and Reformulations M Denizel,H Sural. On alternative mixed integer programming formulations and LPbased heuristics for lotsizing with setup times. Journal of the Operational Research Society (2006) 57, 389–399
13 Discrete Lot Sizing and Scheduling Problem and Sequence Dependent Setups A. Drexl , A. Kimms. Lot sizing and scheduling Survey and extensions. European Journal of Operational Research 99 (1997) 221–235
14 Continuous Setup and Proportional Lot Sizing and Scheduling Problems A. Drexl , A. Kimms. Lot sizing and scheduling Survey and extensions. European Journal of Operational Research 99 (1997) 221–235
15 Project Presentations
16 Review of the Semester  

 

Course Notes/Textbooks Stefan Voβ, David L. Voodruff. Introduction to Computational Optimization Models for Production Planning in a Supply Chain, Second Edition, Springer, ISBN 9783540298786 Yves Pochet, Laurance A. Wolsey,.Production Planning by Mixed Integer Programming, Springer, ISBN 9780387299594
Suggested Readings/Materials Lecture PowerPoint slides,Reading Handouts

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
3
15
Presentation / Jury
Project
1
15
Seminar / Workshop
Oral Exams
Midterm
1
25
Final Exam
1
35
Total

Weighting of Semester Activities on the Final Grade
30
65
Weighting of End-of-Semester Activities on the Final Grade
1
35
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
15
4
60
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
5
4
20
Presentation / Jury
0
Project
1
18
18
Seminar / Workshop
0
Oral Exam
0
Midterms
1
9
9
Final Exam
1
15
15
    Total
170

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


NEWS |ALL NEWS

Izmir University of Economics
is an establishment of
izto logo
Izmir Chamber of Commerce Health and Education Foundation.
ieu logo

Sakarya Street No:156
35330 Balçova - İzmir / Turkey

kampus izmir

Follow Us

İEU © All rights reserved.