QMBU 501 Introduction to Management Science Course Syllabus

QMBU 501 Introduction to Management Science Course Syllabus Instructor

Dr. Özden Gür Ali, Office: CAS 241, Phone: 212 338 1450, E-mail: [email protected]

Class Time/Room Office Hours

M W 14:00 -16:45 / TBA M W 10:00 - 11:30, or by appointment

Course Description Management in the current competitive and complex business environment calls for excellence in decision making. Decisions are made based on sound analysis of facts. The objective of the course is to enable the student to properly use common quantitative modeling tools to support business decision making. This introductory course covers fundamental quantitative methods for business decision making: problem formulation, analysis and use of management science tools: Optimization (including linear and integer programming), Monte Carlo simulation, and Decision analysis. These techniques are used in a wide variety of realistic applications including, marketing (pricing, product feature selection, sales force allocation, advertising scheduling), operations (workforce scheduling, production planning, logistics), and finance (budgeting, cash flow, investment portfolio). A significant portion of class time will be devoted to examples that are drawn from the functional areas such as operations management, finance and marketing. While involved in modeling, understanding the implications and limitations of the model will be emphasized. The management science techniques are implemented with spreadsheets and spreadsheet add-ins for decision analysis and risk analysis applications to facilitate learning by doing. Homework assignments and suggested problems provide the practice needed for the concepts to solidify. Skills developed include identification of the proper modeling tool for the business problem, conducting proper analysis using the tool and developing recommendations for the original business problem. While most of the course is focused on structured problem solving, the optional project provides an opportunity for students to develop their skills in identifying and structuring problems. Homework Assignments Homework problems will be assigned regularly. Whether electronic or hard-copy, the assignments should be legible, clearly documented and prepared according to instructions. Electronic assignments should be submitted by copying the files to F:\COURSES\GRADS\QMBU\QMBU501\HOMEWORK. Project (Optional, Individual or Team) The objective of this project is to develop the skill to structure a practical problem. The student will identify a business problem (personal business is also acceptable), and use one of the methods covered in this course to structure a spreadsheet that can be used to support decision making to solve this problem. An example of such a problem could be which health insurance plan you should choose in light of their premiums and benefits. Another example could be about optimal financial portfolio analysis of various investment options. Yet another example would be to support decisions for how many items to keep of each SKU in a category in the inventory - when we have an idea of the demand and substitution patterns for these items. It is not absolutely necessary to have actual data for the problem, realistic values for model input parameters can be determined based on qualitative research results. Those students who would like to do a project can discuss their project topic ideas with the instructor to get feedback. The project teams/ individual students will prepare a short project proposal identifying the topic - the decision problem with the objective, decisions to be made, and important issues to be considered including assumptions, constraints, deterministic or probabilistic nature and the planning horizon, by October 10th. The students will present their work in class in the last day of classes. The final report and the spreadsheet are due in the finals week. The report should be a maximum of five pages with the following contents: • definition of the business problem with the key issues and the key question; • description the spreadsheet model and how it helps answer the key question; • description of the data needs – essentially the inputs of the model; • list of model assumptions and an assessment of how appropriate they are for the business situation, including the determination of two model parameters for sensitivity analysis

• sensitivity analysis on the two key parameters of the model • recommendation on the key question based on the model output and sensitivity analysis The students can choose to do this project as a team of two to three, or individually. The students who choose to do the project will be able to collect extra 5% grade points (see the grading section).

Exams There will be one midterm and a final exam. Grading The overall grade will be computed as follows No Project Homework assignments 25% Midterm exam 30% Final exam 40% Project Class participation 5% Total 100%

Project 20% 25% 35% 20% 5% . 105%

Course Material The textbook is: Management Science Modeling, International Student Edition, S. C. Albright and W. L. Winston, Thomson, 2007. Academic Honesty Honesty and trust are important to all of us as individuals. Students are expected to adhere to the principles of academic honesty at Koç University, as outlined in the Student Code of Conduct 4.2., and refrain from cheating, plagiarism, multiple submissions, collusion and impersonating, fabrication, as well as facilitating academic dishonesty. Cheating, plagiarism, and collusion are serious offenses resulting in getting zero (or F) from that component and disciplinary action. For individual assignments, collaboration among students is only acceptable in the brainstorming phase where possible approaches to the assignment are discussed. Once a students starts working on an assignment, that student should cease all inter-student communications related to the assignment. For group assignments, collaboration among groups is only acceptable in the brainstorming phase where possible approaches to the case are discussed. Once a group starts computations on an assignment, that group should cease all inter-group communications related to the case. It is especially highly inappropriate to share (ask for as well as provide) drafts, spreadsheets, or completed reports with anyone who is not authorized to work with you on that assignment before all reports are handed in. (Note the two way responsibility in the above statement.) Such actions constitute collusion and are serious offenses of academic dishonesty. Seeking out (by using any means including searching the internet) and using a previously prepared case report or solution to guide your analysis of these assignments at any stage is equally unacceptable and constitutes cheating. Course Outline (Tentative) Week/Date 1 / Sept.15

Topic Reading Introduction to Mathematical Modeling w. Spreadsheets Ch.1, Ch. 2

1 / Sept 17

Introduction to Mathematical Programming Introduction to Linear Programming (LP) LP Assumptions Infeasibility and Unboundedness Reading solver output and sensitivity analysis Modeling LPs using Excel solver

Ch.3

2 / Sept 22

Linear Programming Models Workforce scheduling models Aggregate planning models Blending models Dynamic financial models

Ch.4

2 / Sept 24

Network Models The transportation model The minimum cost network flow model The shortest path model

Ch. 5

3/ Oct. 6

Network Models cont’d The assignment model The maximum flow model

3/ Oct. 8

Midterm 1

4 / Oct 13

Introduction to Integer Programming Using integer variables Capital budgeting model

Ch.6

4/ Oct 15

Integer Programming ct'd Fixed cost model Either/or constraints Warehouse Location

Ch. 6

5/ Oct 20

Nonlinear Programming Introduction to nonlinear optimization Pricing Models Response Models Facility Location Models Evolutionary Solver

Ch. 7, Ch 8

5/ Oct 22

Introduction to Decision Analysis Decision Trees Bayes' Rule Value of Information Decision Analysis examples

Ch. 10

6 / Oct 27

Decision Analysis Attitudes towards risk Utility Monte Carlo Simulation Introduction Simulation with Excel tools

6 / Oct 29

No class - Republic Day

7 / Nov 3

Monte Carlo Simulation Application examples Analysis Simulation with @Risk

7 / Nov 5

Project Presentations

Ch 11

Ch. 12