Computational Methods for Industrial Engineers

Course Code:

ISE 214

Course Period:

Spring

Course Type:

Core

Credits:

4

Theoric:

3

Practice:

0

Laboratory Hour:

2

ECTS:

8

Prerequisite Courses:

Algorithms and Computer Programming

Course Language:

English

Courses given by:

Emre Çelebi

Course Objectives:

Goals of this course are building solid programming skills and applying these skills to implement algorithms frequently used in Industrial and Systems Engineering discipline.

Course Content:

Modular programing using functions. Program control and loops, algorithms with single loop and algorithms with nested loops. Implementing algorithms for the following methods: simplex method for linear programming, genetic algorithms, clustering.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Lab, 4: Case-study

Course Evaluation Methods:

A: Testing, B: Experiment, C: Homework, D: Project

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Course Learning Outcomes

Course Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
1) Ability to approach to a problem analytically	1, 2, 4	1, 2, 3	A, C
2) Ability to develop an algorithm for a problem	1, 2, 4	1, 2, 3	A, C
3) Ability to write modular programs	1, 2, 4, 5	1, 2, 3	A, C
4) Ability to apply programming skills to implement algorithms used in Industrial and Systems Engineering	1, 2, 4, 5	1, 2, 3	A, C

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1-2	Variables, matrices and basic arithmetic operators. Functions, local scope and storage properties	Lecture Notes
3	Program control and loops: If/else structure, while loop, for loop	Lecture Notes
4-6	Single loop algorithms: Permutation, combination and factorial computations; finding min and max of an array; checking equality of arrays; prime numbers; other algorithms Nested loop algorithms: Pascal’s triangle; search algorithms; sorting (selection sort, bubble sort, merge sort); Gaussian elimination; other algorithms	Lecture Notes
7	MT1
7-10	Simplex algorithm for linear programming and its computer implementation	Lecture Notes & Text book
9-11	Genetic algorithms and its computer implementation	Lecture Notes & Text book
11	MT2
13-14	Clustering	Lecture Notes & Text book

Recommended Sources

RECOMMENDED SOURCES
Textbook	Winston W., Operations Research: Applications and Algorithms, 4th edition, 2003, Duxbury Press Jang, J.R, Sun, C., Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, 1st edition, 1997, Prentice Hall
Additional Resources	Lecture Notes

Material Sharing

MATERIAL SHARING
Documents	Lecture Notes on modular programming, program control, algorithm development, simplex method genetic algorithms and clustering methods.
Assignments	Homework and Lab assignments
Exams	2 Midterms and 1 Final Exam

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-term	2	83
Homework	7	8
LAB	10	9
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.					X
2	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.					X
3	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
4	Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.					X
5	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
6	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.			X
7	Ability to communicate effectively both orally and in writing; knowledge of a minimum of one foreign language.
8	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9	Awareness of professional and ethical responsibility.
10	Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11	Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration: Theory (14 x 3 hours)	14	3	42
Course Duration: Lab (12 x 2 hours)	12	2	24
Hours for off-the-classroom study (Midterms)	2	20	40
Hours for off-the-classroom study (Final)	1	30	30
Hours of studying for the Homework	7	8	56
Midterm examination	2	2	4
Final examination	1	2.5	2.5
Total Work Load			198.5
Total Work Load / 25 (h)			7,9
ECTS Credit of the Course			8

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