Computer Applications for Industrial Engineers

Course Code:

ISE 316

Course Period:

Spring

Course Type:

Core

Credits:

3

Theoric:

2

Practice:

0

Laboratory Hour:

2

ECTS:

5

Prerequisite Courses:

Computational Methods for Industrial Engineers

Course Language:

English

Course Objectives:

• Understanding basic programming concepts and approaches • Learn how to design an algorithm • To be able to design optimization algorithms • Learning how to model and solve MIP using an optimization solver • To have an idea about the solution methods used in industrial engineering • To reach the level of running optimization algorithms

Course Content:

To create heuristic and metaheuristic algorithms and solve optimization problems. To model and solve mixed integer programming using an optimization solver.

Course Methodology:

1: Lecture by instructor, 2: Lecture by instructor with class discussion, 3: Problem solving by instructor, 4: Use of simulations, 5: Problem solving assignment

Course Evaluation Methods:

A: Written exam, C: In class exercise , E: Homework, F: Project

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Have an idea about what algorithms can be used for problems in the field of industrial engineering.	1a, 2a	1, 2, 3	A, C
Gaining the skill to use Python programming language at a good level.	1a, 4a, 4b, 6c	1, 2, 3	A, C, E
Programming skill to run algorithms written in Python programming language	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b, 6c	1, 2, 3, 5	A, C, E
Learn the structure and working principles of routing models and algorithms	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b, 6c	1, 2, 3	A, C, E
Learn the structure and working principles of facility location models and algorithms	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b, 6c	1, 2, 3	A, C, E
Develop and program mathematical models using a Gurobi for optimization problems	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b, 6a ,6c	1, 2, 3, 4, 5	A, C, E, F
Learn to develop and program heuristic algorithms	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b, 6a, 6c	1,2,3, 5	A, C, E, F
Learn basic knowledge about metaheuristic algorithms and advanced programming techniques	1a, 1b, 2a, 2b, 3a, 3b, 4a, 4b	1,2,5	A
Use of DOE for data analysis	5a	1	F

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction to Programming in Python	Textbook, Lecture Notes
2	Introduction to Programming in Python and Shortest Path Problem	Textbook, Lecture Notes
3	Short Haul Freight Transportation (TSP)	Textbook, Lecture Notes
4	Designing and implementing heuristic methods for TSP in Python	Textbook, Lecture Notes
5	Designing and implementing heuristic methods for TSP in Python	Textbook, Lecture Notes
6	Short Haul Freight Transportation (VRP)	Textbook, Lecture Notes
7	Designing and implementing heuristic methods for VRP in Python	Textbook, Lecture Notes
8	Facility location: Discrete space	Textbook, Lecture Notes
9	Designing and implementing heuristic methods for facility location problem in Python	Textbook, Lecture Notes
10	Development and solving MIP models using Gurobi	Textbook, Lecture Notes
11	Location Covering, p-median and p-center problem	Textbook, Lecture Notes
12	Designing and implementing heuristic methods for facility Location Covering, p-median and p-center problems in Python	Textbook, Lecture Notes
13	Meta-Heuristic Methods for optimization problems	Textbook, Lecture Notes
14	Meta-Heuristic Methods for optimization problems	Textbook, Lecture Notes

Recommended Sources

RECOMMENDED SOURCES

Textbook

Introduction to Logistics Systems Management. G. Ghiani, G. Laporte and R. Musmanno. John Wiley & Sons, 2013.

Additional Resources

Introduction to Logistics Systems Planning and Control. G. Ghiani, G. Laporte and R. Musmanno. John Wiley & Sons, 2004.

Facilities Design, 3rd edition. S. Heragu. CRC Press, 2008.

Material Sharing

MATERIAL SHARING
Documents
In class Assignments
Exams	Homework, Midterm, Project, Final

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Midterm	1	25
Homework	2	15
Project	1	20
Total		60
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 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.	X
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,	X
3b	Ability to apply modern design methods for this purpose.	X
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,	X
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.	X
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	14	3	42
Hours for off-the-classroom study (Pre-study, practice, project)	14	5	70
Midterm	1	5	5
Final	1	15	15
Total Work Load			132
Total Work Load / 25 (h)			5.28
ECTS Credit of the Course			5

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Computer Applications for Industrial Engineers

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