Course Pages:
Office Hours:
Tuesday, 14:00 – 16:00. You may also ask for an appointment for a meeting out of office hours.
Main References:
- Textbook: Winston, W. L., & Goldberg, J. B. (2004). “Operations Research: Applications and
Algorithms”. Belmont, CA: Duxbury press.
- Textbook: Taha, H., (2011). “Operations Research: An Introduction”. NY: Pearson.
You can also benefit from any other textbook, article, note or lecture note, in English or Turkish, which covers the
course material.
Objectives:
This is the first course of the two-step “Operations Research” series, primarily designed for constructing a
background on the quantitative decision making techniques, including mathematically modelling and optimizing
basic decision models. This first step mostly focuses on developing a technical background and understanding of
mathematical modelling techniques, Linear Programming, Integer Programming and Mixed Integer Programming in particular.
Prerequisites:
A basic level knowledge on Excel software is assumed.
Grading Policy:
- Your final grade will be a result of a midterm exam (-%), a group project (-%), participation in
class (-%) and a final exam (-%).
- The group project is compulsory for all students:
- Groups may involve 4-5 students. Students may form their own groups before the due date. The
members of the formed groups should be defined using the link provided on the instructor’s
website. Students who did not attend to a group will be randomly assigned to a group on the due
date.
- The project is on defining and solving a realistic decision problem. You will find a real-life based
decision problem, define it and work on it.
- The problems should be selected based on the topic taught in the lecture (i.e., diet,
work-scheduling, capital budgeting, financial planning, blending, production process, inventory
or transportation). Each topic can be studied by at most 6 groups.
- The decision problem should be realistic and worth to work on. The decision problem may be
found from any source, including newspapers, TV news, a movie or your imagination, and not
obliged to be industrial.
- Once you spot a problem, the instructor will assist you for officially defining and formulating the
problem. If the problem is too large to solve, simplifications/assumptions can be made within the
knowledge of the instructor.
- You can use any solution approach taught in the course (Linear, Integer, Mixed Integer
Programming approaches). Then, you are expected to solve the problem using Excel Solver.
- You are required to write a report.
- The report should be around 1,000 - 1,500 words and should consist of the definition of and solution to
the decision problem:
- Introduction: Context of the problem. Where did you find it?
- Problem definition: What do you need to decide, what is your aim, what limits you within
the problem context.
- Mathematical model: Decision frame (model) of the problem, with explanations (i.e., explain
each member of objective function and each constraint.)
- Solution: Screen shots of the Excel-Solver windows you used, and presentation of results in
detail.
- Conclusion: A summary of what you did and how your results fit in the real-life problem.
- Important deadlines for your project are:
- Forming groups: 16.03.2017 (Students who is not assigned to a group will be randomly
assigned to any group. Groups will not change after this date.)
- Selecting topic: 30.03.2017 (Groups which did not select a topic will be randomly assigned
one. Topics will not change after this date.)
- Submission of the report: 04.05.2017
- Late submission of the reports will be penalized by 10 points out of 100. If the submission is late for
more than one week, each week of lateness will be penalized by 10 points.
- The midterm and final examinations will be open-book written exams with essay questions/problems. In
addition to classical problems, you will also be asked to fill in forms representing Excel spreadsheets. Please
bring your calculators and necessary stationary equipment with you for the exam. Sharing calculators or any
stationary equipment will not be allowed.
- All the submitted-on-time homeworks which are given before the midterm exam and are satisfactorily
worked on will be rewarded by 4 bonus points for the midterm exam. All the submitted-on-time
homeworks which are given after the midterm exam and are satisfactorily worked on will be
rewarded by 3 bonus points for the final exam. Late submission of the homeworks will not be
accepted.
- File names of homeworks and project report should be defined only as “STUDENTNO”. As this is an
automated process, any impropriety may cause losing bonus points, and is not subject to any regular
inspections. Homeworks should be uploaded to the related folder in the link provided on the instructor’s
website.
- Attendance will not be used for grading purposes, except for the related university regulations.
Class Policy:
- Students are obliged to maintain the learning environment.
- When asked, students are expected to participate in in-class activities.
- Lack of knowledge of the rules is not a reasonable explanation for a violation. Any violation of the
rules or causing a disturbance in the classroom will be observed and necessary actions will be taken.
Tentative Course Outline (in paranthesis, corresponding sections from Winston (2004) are presented):
| Week -1- | Introduction to the Course and Operations Research |
| Week -2- | What is a Linear Programming (3.1) |
| Week -3- | Modelling Examples, Graphical Solution of Two-Variable LP Models and the Simplex Algorithm (3.1, 3.2) |
| Week -4- | Special Cases in LPs (3.3) |
| Week -5- | Sensitivity Analyses - Introduction (6.1, 6.3) |
| Week -6- | Sensitivity Analyses - The 100\% Rule (6.4) |
| Week -7- | Sensitivity Analyses - Duality (6.5, 6.6)
|
| Week -8- | MIDTERM WEEK - Course will not take place. |
| Week -9- | LP Examples: Diet and Work-Scheduling Problems (3.4, 3.5) |
| Week -10- | LP Examples: Capital Budgeting and Financial Planning Problems (3.6, 3.7) |
| Week -11- | LP Examples: Blending and Production Process Problems (3.8, 3.9) |
| Week -12- | Modelling Multi-Period Problems: Inventory Problems (3.10) |
| Week -13- | Modelling Multi-Period Problems: Financial Planning Problems(3.11) |
| Week -14- | Modelling Multi-Period Problems: Work
Scheduling Problems (3.12) |
