Decision Analysis

Course Code:

ISE 432

Course Period:

Spring

Course Type:

Core

Credits:

3

Theoric:

2

Practice:

1

Laboratory Hour:

1

ECTS:

6

Prerequisite Courses:

Operations Research II

Statistical Applications for Industrial Engineers

Course Language:

English

Course Objectives:

The purpose of this course is to provide an in depth coverage and applications of Decision Making to complex real-world problems. Major objectives of this course include; • Applying statistical models to complex decision problems • Rational reasoning in critical thinking, modeling and decision making • Analysis and structuring of the decision problem • To understand the limits of the scientific theories (probability theory, etc.) that we use to find solutions to complex decision problems of the real world.

Course Content:

1. Modeling of the Decision Problems: Elements of Decision problems, Structuring Decisions and Sensitivity Analysis

2. Modeling Uncertainty: Basics of Probability theory, Probability Models, Data Analysis and Value of Information

3. Modeling Preferences : Basic concepts of Preference Model

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, 6: Reading assignment, 7: Laboratory work, 8: Term research paper, 9: Presentation by guest s

Course Evaluation Methods:

A: Written exam, B: Multiple-choice exam C: Take-home quiz, D: Experiment report, E: Homework, F: Project, G: Presentation by student

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Identifies the modelling steps in decision theory and recognizes the related basic concepts.	1a, 2a, 6c	1, 2, 3, 12	A, C
Constructs the structure of a decision problem via decision trees.	2a, 2b, 3a, 3b, 6c	1, 2, 3	A, C, F
Applies software (top rank or Precision tree) to construct and solve decision trees and risk profiles.	4a, 4b	1, 3, 7, 8	F
Identifies the best decision alternative by evaluating expectations and Risk analysis results simultaneously.	1a, 1b, 2a, 2b, 6c	1, 2, 3, 8, 12	A, C, F
Utilises tornado diagrams to identify the variables that are critical for the objective function and performs sensitivity analysis on these critical variables.	1a, 1b, 2a, 2b, 5a, 5b, 6c	1, 2, 3, 8, 12	A, C, F
Reviews basic probability theory and statistical concepts including data analysis, estimation, correlation and linear regression. Uses all these concepts in the uncertainty modelling phase of Decision process.	1a, 1b, 2a, 2b, 6c	1, 2, 3, 8	A, C, F
Calculates the value of Perfect Information and Imperfect information and determines the cost of reducing uncertainty in a decision problem.	2a, 2b, 6c	1, 2, 3, 8, 12	A, C, F
Examines and finalises a real world decision problem by applying all stages that take place in a decision process	2a, 2b, 3a, 3b, 4a, 4b, 5a, 5b, 6a, 7b, 7c, 7d, 7e, 8b, 9a, 11b	1, 2, 3, 4, 7, 8	F, G

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	OVERVIEW OF THE COURSE, DECISION MAKING; UNCERTAINTY, PREFERENCES, AND ACTIONS; DECISION SUPPORT SYSTEMS. DECISION MODELING TOOLS; DECISION TREES (INFLUENCE DIAGRAMS)	Textbooks, Course Notes
2	ANALYTIC HIERARCHY PROCESS	Textbooks, Course Notes
3	DECISION TREES, EMV, RISK PROFILES, DOMINANCE	Textbooks, Course Notes
4	MAKING CHOICES AND CASE STUDIES	Textbooks, Course Notes
5	SENSITIVITY ANALYSIS/CASE STUDIES	Textbooks, Course Notes
6	VALUE OF INFORMATION: VALUE OF PERFECT INFORMATION	Textbooks, Course Notes
7	VALUE OF INFORMATION: VALUE OF IMPERFECT INFORMATION	Textbooks, Course Notes
8	CASE STUDIES	Textbooks, Course Notes
9	MODELING UNCERTAINTY:INTRODUCTION TO PROBABILITY	Textbooks, Course Notes
10	REVIEW AND MIDTERM	Textbooks, Course Notes
11	MODELING UNCERTAINTY: DISTRIBUTIONS	Textbooks, Course Notes
12	MODELING UNCERTAINTY: USING DATA	Textbooks, Course Notes
13	MODELING UNCERTAINTY: REGRESSION ANALYSIS	Textbooks, Course Notes
14	GENERAL REVIEW AND PROJECT PRESENTATIONS	Textbooks, Course Notes

Recommended Sources

RECOMMENDED SOURCES

Textbook

Making Hard Decisions: An Introduction to Decision Analysis

by Robert T. Clemen& T. Reilly

South –Western Cengage Learning

Academic Press. ISBN 0-495-01508-

Additional Resources

Statistics and Decisions, An introduction to Foundations

by Steven Kim

Decision Analysis, A Bayesian Approach

by J. Q. Smith.

Material Sharing

MATERIAL SHARING
Documents	Lecture notes, lab notes, https://coadsys.yeditepe.edu.tr/
Assignments	Team project report
Exams	Midterm exam, project presentation, quizzes (1-6), final exam

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Attendance	1	5
Midterm	1	25
Project	1	25
Quizzes	1	10
Total		65
Contribution of final examination to overall grade		35
Contribution of in-term studies to overall grade		65
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check √
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	√
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	√
2a	Ability to identify, formulate, and solve complex engineering problems,	√
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.	√
4b	Ability to employ information technologies effectively.	√
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.	√
7a	Ability to communicate effectively in Turkish, 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	14	2	28
Laboratory	12	2	24
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Midterm	1	2	2
Homework	4	4	16
Project	1	40	40
Quiz	6	0.5	3
Final	1	2	2
Total Work Load			157
Total Work Load / 25 (h)			6.2
ECTS Credit of the Course			6

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Decision Analysis

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