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. Sorting algorithms. Advanced data structures, queues. Algorithms for some operations research (OR) problems such as probabilistic modelling (Markov chains, discrete event simulation), dynamical systems (differential equations, systems dynamics), emergent behavior (game theory, agent-based simulation) and optimization (linear programming, heuristics).

Course Methodology:

1: Lecture by instructor, 2: Lecture by instructor with class discussion, 3: Problem solving by instructor, 5: Problem solving assignment,7: Laboratory work

Course Evaluation Methods:

A: Written exam, E: Homework, H: Quiz in lab hours

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Writes basic programs as functions using arithmetic operations, variables, arrays, matrices, if/elif/else structures and loops.	1a, 1b, 2a, 4a ,4b, 6c	1,2,3,5,7,12	A,E,F,H
Solves complex programming problems requiring nested loops. May write pseudo codes for this purpose if necessary.	1a, 1b, 2a, 4a ,4b, 6c	1,2,3,5,7,12	A,E,F,H
Applies sorting and queuing algorithms to complex data structures.	1a, 1b, 2a, 4a ,4b, 6c	1,2,3,5,7	A,E,H
Identifies basic operations research problems such as probabilistic modelling, dynamical systems, emergent behavior and optimization.	1a, 6c	1,2,3,5,7	A,E,H
Writes functions to solve for these basic operations research problems.	1a, 1b, 2a, 4a ,4b, 6c	1,2,3,5,7	A,E,H
Writes algorithms for solving these basic OR problems.	1a, 1b, 2a, 4a ,4b, 6c	1,2,3,5,7	A,E,H

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1-2	Variables, arithmetic operations, functions	Lecture notes
3-4	Program control and execution: if/elif/else structures	Lecture notes
5-6	Loops: while/for loops, nested loops	Lecture notes
7	Sorting algorithms	Lecture notes
8	Advanced data structures; FIFO, FILO and priority queues	Lecture notes
9	MIDTERM EXAM
10-14	Operations Research (OR) applications such as probabilistic modelling (Markov chains, discrete event simulation), dynamical systems (differential equations, systems dynamics), emergent behavior (game theory, agent-based simulation) and optimization (linear programming, heuristics).	Lecture notes, TB1: CH01-CH07

Recommended Sources

(OR)RECOMMENDED SOURCES
Textbooks	TB1: Palmer G. and Knight V., (2021). Applied Mathematics with Open-Source Software: Operational Research Problems with Python and R [Online]. Available at: https://github.com/drvinceknight/amwoss/blob/main/src/main.pdf (Accessed: 19 September 2022).
Additional Resources	Lecture Notes

Material Sharing

MATERIAL SHARING
Documents	Lecture notes as presentation slides
Assignments	Lab quizzes, exercise sets
Exams	1 Midterm and 1 Final Exam

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-term	1	73
LAB Quizzes	10	27
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		45
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		55
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check X
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	X
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	X
2a	Ability to identify, formulate, and solve complex engineering problems,	X
2b	Ability to select and apply proper analysis and modeling methods for this purpose.
3a	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,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.	X
4b	Ability to employ information technologies effectively.	X
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.
6a	Ability to work efficiently in intra-disciplinary teams,
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.	X
7a	Ability to communicate effectively both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,
7d	Ability to make effective presentations,
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	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 (Midterm)	1	40	40
Hours for off-the-classroom study (Final)	1	50	50
Hours of studying for quizzes	10	5	50
Midterm examination	1	2	2
Final examination	1	2	2
Total Work Load			210
Total Work Load / 25 (h)			8.4
ECTS Credit of the Course			8

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Computational Methods for Industrial Engineers

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