M, 6:00pm to 9:00pm
Class will not meet on:
Class will meet on:
This course is designed for students who have taken Decision Models (B60.2350) and would like develop further their quantitative modeling skills for managerial decision making. Students will learn more advanced modeling tools including: static stochastic optimization, two-stage stochastic optimization with recourse, chance-constrained stochastic optimization, and dynamic programming. We explore their applications in various business domains, such as marketing, finance, inventory management, revenue management, supply chain management, project management, among others. Students will learn how these models can be solved using Risk Solver Platform for Excel, a powerful tool for risk analysis, simulation, and optimization. The emphasis throughout the course will be model formulation, solution methods, and managerial interpretation of the results, rather than on the mathematical algorithms used to solve models.
From this course, you should be able to
· Recognize the types of modeling tools most adapted to a given situation;
· Understand their main benefits and limitations
· Structure real life managerial problems, build and analyze models to address the problems
· Identify opportunities for benefiting from use of the models
B60.2350: Decision Models or
C70.0007: Decision Models
The following books are very good references for this course. They are recommended, not required.
Material, including Excel solution models, software, optional readings and lecture slides, will be distributed electronically through the course web site (Blackboard). Hard copies of lecture slides will be distributed in class.
See "Grading" below.
Your course grade will be based on:
· Group Assignments (60% - three assignments: 20% each). There will be four graded group assignment studies. You are asked to work in groups of two/three people. One copy of the final report should be handed in, and all members of the group will get the same grade.
· Group project (20%). This involves creating, modeling, and analyzing a business case of your choice. It may be based on your work experience, a case from another course, a magazine article, or even your own imagination! You should then develop a spreadsheet model of this case to illustrate a new application of the modeling concepts and methods learned in this course. The grade will be based on the project deliverable: a self-contained PowerPoint presentation describing the studied problem, its institutional context and importance, available and required data, as well as all relevant decisions.
· Class Participation (20%). This fraction of the grade will be assigned on the basis of class participation and individual professional conduct. Class participation includes class discussions of assignments and cases, presentation of an exercise solution, as well as active participation in lectures. I expect all class participants to arrive to class on-time and prepared, and to stay involved during class sessions. Every conceivable effort should be made to avoid absences, late arrivals or early departures. In cases when these are unavoidable, they need to be communicated to me in advance.
The process of modeling is the most important and difficult problem solving skill. It involves developing a structure to conceptualize, formalize and analyze a given problem. It seems deceptively simple to watch someone else do it, but the only way to learn this skill is by practicing it yourself. Therefore, this course involves a hand-on, in-class learning experience. Attending each class and bringing a laptop computer to class are essential. Preparation for each class involves reading and thinking about the problems to be covered in class. Excel files of the problems modeled and analyzed in class should be downloaded from Blackboard before (not during) the class.
Your class may be recorded for educational purposes
Cell phones, smartphones and other electronic devices are a disturbance to both students and professors. All electronic devices (except laptops) must be turned off prior to the start of each class meeting.
You are expected to bring a laptop to each class, unless otherwise instructed. But we will not use it throughout each class. Please close your laptop until you are asked to use it.
Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:
The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:
Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505
To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin. Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications. Additionally, your document will become part of the Turnitin database.
If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend. If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.
Topic 1: Real Option Approach to the Valuation of Investment Opportunities: Simulation
Topic 2: Stochastic Optimization Models
Topic 3: Stochastic Optimization with Chance Constraints
Topic 4: Two-Stage Stochastic Optimization with Recourse
Topic 5: Deterministic Dynamic Programming
Topic 6: Stochastic Dynamic Programming
Topic 7: Structure of Optimal Policies in Stochastic Optimization
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior. Students who fail to do so may be asked to leave the classroom.
Collaboration on Graded Assignments
Students may not work together on graded assignment unless the instructor gives express permission.
Course evaluations are important to us and to students who come after you. Please complete them thoughtfully.