COURSE INTRODUCTION AND APPLICATION INFORMATION


Course Name
Art of Mathematical Modelling
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 355
Fall/Spring
2
2
3
6
Prerequisites
 IE 252To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives The aim of this course is to teach students building mathematical models and heuristic solution algorithms of real-life problems and to enable them solving the complex problems encountered in business.
Learning Outcomes The students who succeeded in this course;
  • explain existing mathematical models
  • develop conceptual models for decision making problems
  • transform conceptual models to mathematical model formulations
  • develop heuristic solution algorithms for decision making problems
  • develop mathematical models and heuristic solution algorithms for essential problems in industrial system engineering
  • code mathematical models and heuristic solution algorithms in IBM ILOG OPL Development Studio
Course Description Topics of this course include developing mathematical models and heuristic solution algorithms for essential Industrial Systems Engineering problems. During the course, IBM ILOG OPL Development Studio will be used to code and solve mathematical models and heuristic algorithms.
Related Sustainable Development Goals

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Introduction to Mathematical Modeling and OPL IBM ILOG CPLEX OPTIMIZATION STUDIO (OPL) Documentation version 16, A Short Introduction to OPL
2 Building Linear Programming Models I : Workforce Planning Winston, W. L., Operations Research: Applications and Algorithms, Ch 3, Duxbury Press
3 Building Linear Programming Models II: Supply planning and CPM models Winston, W. L., Operations Research: Applications and Algorithms, Ch 8, Duxbury Press
4 Linearizing Logical Forms with Binary Variables, Quiz I Sierksma, G. Linear and Integer Programming Theory and Practice, Ch 6, Marcel Dekker Inc. Second Edition
5 Building Integer Programming Models: Modeling integer programming models with conditional decisions, set packing, covering and partitioning problems Hillier, F. S., and Lieberman, G. J., Introduction to Operations Research, Ch 11,Ninth Edition, 2010 Mc Graw-Hill
6 Algorithm development and programming with ILOG OPL IBM ILOG CPLEX OPTIMIZATION STUDIO (OPL) Documentation version 16
7 Quadratic Assignment Problem Model Formulations and Heuristic Solution Algorithms Sierksma, G. Linear and Integer Programming Theory and Practice, Ch 6-7, Marcel Dekker Inc. Second Edition
8 Traveling Salesman Problem Model Formulations and Heuristic Solution Algorithms, Cutting Stock Problems Hillier, F. S., and Lieberman, G. J., Introduction to Operations Research, Ch 13, Ninth Edition, 2010 Mc Graw-Hill
9 Industrial Applications of Integer Programming I : Lot Sizing and Scheduling Models, Wagner Whitin Algorithm, Vehicle Routing Problem
10 Industrial Applications of Integer Programming II : Assembly Line Balancing , Dedicated Storage System Models and Heuristic Solution Algorithms
11 Industrial Applications of Integer Programming III : Modeling Machine Scheduling Problems I : Single Machine and Job Shop Scheduling Problems M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems Ch 3-4, 2005, Springer,
12 Industrial Applications of Integer Programming IV : Modeling Machine Scheduling Problems II : Single Machine and Job Shop Scheduling Problems with sequence dependent setup times M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems Ch 3-4, 2005, Springer,
13 Industrial Applications of Integer Programming V : Modeling Machine Scheduling Problems III : Heuristic solution algorithms and constraint programming models to solve single machine and job shop scheduling problems M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems Ch 3-4, 2005, Springer,
14 Project Presentations, Quiz II
15 Review of the semester
16 Final Exam
Course Notes/Textbooks Model Building in Mathematical Programming, Fourth ed., H. Paul Williams, WILEY.
Suggested Readings/Materials Lecture PowerPoint slides, Reading Handouts, Articles from journals, Optimization in Operations Research, Ronald L.Rardin, Prentice Hall, ISBN : 0-02-398415-5, Introduction to Operations Research, Frederick S. Hillier, Gerald J. Lieberman, Ninth Edition, 2010 Mc Graw-Hill, ISBN: 978-007-126767-0 , Operations Research: Applications and Algorithms, Wayne L. Winston, Duxbury Press, ISBN 0-534 20971-8., Linear and Integer Programming Theory and Practice, Gerard Sierksma, Marcel Dekker Inc., Second Edition, ISBN 978-0824706739, Optimization Modeling A Practical Approach, Ruhul A. Sarker, Charles S. Newton, CRC Press, 2008, ISBN 978-1420043105, Applied Integer Programming, Modeling and Solution. Der-San Chen, Robert G. Batson, Yu Dang, Wiley, 2010. ISBN 978-0-470-37306-4, Logic and Integer Programming, H. Paul Williams, Springer, ISBN 978-0387922799, M. L. Pinedo, Scheduling: Theory, Algorithms, and Systems, 2005, Springer, ISBN 978-0387789347, IBM ILOG CPLEX OPTIMIZATION STUDIO (OPL) Documentation.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
15
Presentation / Jury
Project
1
10
Seminar / Workshop
Oral Exam
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
4
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
2
32
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
2
Study Hours Out of Class
14
3
42
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
12
Presentation / Jury
Project
1
22
Seminar / Workshop
Oral Exam
Midterms
1
17
Final Exams
1
23
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

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

To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics.

6

To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; 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 Computer 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 Computer Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized 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

To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Computer Engineering.

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