MAN201 Introduction to Management Science

Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026

Referencing Curricula

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Management

Academic Year

-

Semester

-

Course Code

MAN201

Weekly Hours

2 Teaching + 1 Practice

ECTS

6

Prerequisites

Teaching Mode Delivery

Prerequisite For

-

Teaching Mode Delivery Notes

-

Cycle

I Cycle

Prof. Jane Doe

TBA

Course Lecturer

Position

-

Email

Phone

033 957

Assistant(s)

-

Assistant E-mail

Course Objectives

The aim of this course is to introduce and teach students about mathematical model construction, spreadsheet modeling using Excel Solver, and interpretation of Solver output. The students will also learn about other decision making tools such as decision trees and simulation.

Learning Outcomes

After successful completion of the course, the student will be able to:

1

Identify and apply appropriate quantitative tools for managerial decision-making in business contexts

2

Explain the fundamental building blocks of quantitative decision-making models

3

Develop, solve, and interpret quantitative models using spreadsheet tools (e.g., Excel Solver).

4

Analyze and evaluate alternative solutions, including sensitivity to changes in model parameters.

5

Communicate findings effectively through professional written reports and oral presentations.

Course Materials

Required Textbook

Render, B., Stair R. M., and Balakrishnan, N. (2003). Managerial Decision Modeling with Spreadsheets, Prentice Hall.

Additional Literature

Teaching Methods

The methods include lectures (which may involve power point presentation

Video and audio aids)

Student presentations

Projects and class discussions.

Weekly Topics

This weekly planning is subject to change with advance notice.

Week	Topic	Readings / References
1	Introduction to Management Science & Modelling	Chapter 1
2	Modelling, cont.	Chapter 1
3	Linear Programming (LP): Graphical Solution Methods	Chapter 2
4	Linear Programming (LP): Computer Solution	Chapter 2
5	Using Excel Solver to solve LP problems / Interim Exam 1	Chapter 2
6	LP Applications	Chapter 3
7	LP Applications cont. / Interim Exam 2	Chapter 3
8	LP Sensitivity Analysis	Chapter 4
9	Mathematical Programming Models	Chapter 6
10	IP, NLP, Goal Programming Models	Chapter 6
11	Network models	Chapter 5
12	Project Management	Chapter 7
13	Simulation	Chapter 10
14	Simulation cont.	Chapter 10
15	Review / Lab Exam

Course Schedule (All Sections)

Course Schedules with all sections will be available here soon.

Office Hours & Room

Course Office hours will be available here soon.

Assessment Methods and Criteria

Assessment Components

25%x1

Final Exam

AI: Not Allowed

Alignment with Learning Outcomes : 1 2 3 4 5 6

30%x2

Interim Exams

AI: Not Allowed

Alignment with Learning Outcomes : 1 2 3 4 5 6

15%x1

Term Project

AI: Not Allowed

Alignment with Learning Outcomes : 1 2 3 4 5 6

15%x1

Lab Exam

AI: Not Allowed

Alignment with Learning Outcomes : 1 2 3 4 5 6

15%x4

Homework

AI: Not Allowed

Alignment with Learning Outcomes : 1 2 3 4 5 6

IUS Grading System

Grading Scale	IUS Grading System	IUS Coeff.	Letter (B&H)	Numerical (B&H)
0 - 44	F	0	F	5
45 - 54	E	1	F	5
55 - 64	C	2	E	6
65 - 69	C+	2.3	D	7
70 -74	B-	2.7	D	7
75 - 79	B	3	C	8
80 - 84	B+	3.3	C	8
85 - 94	A-	3.7	B	9
95 - 100	A	4	A	10

Late Work Policy

Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.

ECTS Credit Calculation

📚 Student Workload

This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:

Lecture Hours

45 hours ⏳ (15 week × 3 h)

Homework

12 hours ⏳ (4 week × 3 h)

Lab Exam

11 hours ⏳ (1 week × 11 h)

Active Tutorials

28 hours ⏳ (14 week × 2 h)

Term Project

10 hours ⏳ (1 week × 10 h)

Interim Exams Study

24 hours ⏳ (2 week × 12 h)

Final Exam Study

20 hours ⏳ (1 week × 20 h)

150 Total Workload Hours

6 ECTS Credits

Course Policies

Academic Integrity

All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.

Attendance Policy

Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.

Technology & AI Policy

Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.

Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.

Communication Policy

All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [MAN201] in the subject line.

Academic Quality Assurance Policy

Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.

Learning Tips

Engage Actively

Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.

Read and Review Purposefully

Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.

Think Critically in Assignments

Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.

Ask Questions Early

Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.

Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo

Referencing Curricula Print this page

Course Code	Course Title					Weekly Hours*		ECTS		Weekly Class Schedule
						T	P
MAN201	Introduction to Management Science					2	1	6		Tuesday 11:00-12:50 Thursday 10:00-10:50
Prerequisite	MAN102				It is a prerequisite to	-
Lecturer					Office Hours / Room / Phone	Currently not available
E-mail
Assistant					Assistant E-mail
Course Objectives	The aim of this course is to introduce and teach students about mathematical model construction, spreadsheet modeling using Excel Solver, and interpretation of Solver output. The students will also learn about other decision making tools such as decision trees and simulation.
Textbook	Render, B., Stair R. M., and Balakrishnan, N. (2003). Managerial Decision Modeling with Spreadsheets, Prentice Hall.
Additional Literature
Learning Outcomes	After successful completion of the course, the student will be able to:
	Identify and apply appropriate quantitative tools for managerial decision-making in business contexts Explain the fundamental building blocks of quantitative decision-making models Develop, solve, and interpret quantitative models using spreadsheet tools (e.g., Excel Solver). Analyze and evaluate alternative solutions, including sensitivity to changes in model parameters. Communicate findings effectively through professional written reports and oral presentations.
Teaching Methods	The methods include lectures (which may involve power point presentation, video and audio aids), student presentations, projects and class discussions.
Teaching Method Delivery				Teaching Method Delivery Notes
WEEK	TOPIC								REFERENCE
Week 1	Introduction to Management Science & Modelling								Chapter 1
Week 2	Modelling, cont.								Chapter 1
Week 3	Linear Programming (LP): Graphical Solution Methods								Chapter 2
Week 4	Linear Programming (LP): Computer Solution								Chapter 2
Week 5	Using Excel Solver to solve LP problems / Interim Exam 1								Chapter 2
Week 6	LP Applications								Chapter 3
Week 7	LP Applications cont. / Interim Exam 2								Chapter 3
Week 8	LP Sensitivity Analysis								Chapter 4
Week 9	Mathematical Programming Models								Chapter 6
Week 10	IP, NLP, Goal Programming Models								Chapter 6
Week 11	Network models								Chapter 5
Week 12	Project Management								Chapter 7
Week 13	Simulation								Chapter 10
Week 14	Simulation cont.								Chapter 10
Week 15	Review / Lab Exam

Assessment Methods and Criteria	Evaluation Tool	Quantity	Weight	Alignment with LOs	AI Usage
	Final Exam	1	25	1,2,3,4,5,6	Not Allowed
	Semester Evaluation Components
	Interim Exams	2	30	1,2,3,4,5,6	Not Allowed
	Term Project	1	15	1,2,3,4,5,6	Not Allowed
	Lab Exam	1	15	1,2,3,4,5,6	Not Allowed
	Homework	4	15	1,2,3,4,5,6	Not Allowed
* ECTS Credit Calculation *

Activity	Hours	Weeks	Student Workload Hours	Activity				Hours	Weeks	Student Workload Hours
Lecture Hours	3	15	45	Homework				3	4	12
Lab Exam	11	1	11	Active Tutorials				2	14	28
Term Project	10	1	10	Interim Exams Study				12	2	24
Final Exam Study	20	1	20
				Total Workload Hours =						150
*T= Teaching, P= Practice				ECTS Credit =						6
Course Academic Quality Assurance: Semester Student Survey									Last Update Date: 27/03/2026

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