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COURSE DESCRIPTION
The course focuses on the quantitative analysis of business problems requiring decision making in an
uncertain environment. It also covers some deterministic (that is, without uncertainty) problem-solving
techniques, especially when they are similar to techniques used in the presence of uncertainty. This
course extends and complements the required core course “Operations Management” (33:136:386).
Unlike Operations Management, this course emphasizes not only modeling decision problems, but also
some important concepts relating to how one solves them. The course will be divided into three modules:
the first two modules will cover techniques in which it is possible to examine (perhaps after some
approximation) all the possible outcomes of an uncertain situation. The first module primarily covers
decision tree analysis, a technique in which one draws a steadily unfolding “tree” of all the possibilities
that might occur. It also includes a related technique called critical fractile analysis. The study of
decision trees will incorporate some work on Bayesian probability calculations.
The second module covers dynamic programming. This analytical method is conceptually similar to
decision trees: in fact, decision trees are a special case of dynamic programming. In dynamic
programming, we still consider every possible eventuality that could occur, but we structure the
calculations so that we do not necessarily produce a tree of outcomes that grows rapidly with time. When
applicable, it is a very powerful technique. Dynamic programming calculations tend to be tedious when
performed by hand, and are often painful to implement in spreadsheets, so we will learn how to perform
them using simple computer programs. These programs will be in the Python language, and the module
will include a basic introduction to Python programming.
Finally, to deal with situations in which the kind of exhaustive analysis covered in the first two modules
is too difficult, the last module of the course will deal with simulation methods in which one samples the
possible outcomes and evaluates different decision policies by examining their performance on the
sample. We will cover both Monte Carlo simulation, using the YASAI Excel add-in also used in the
Operations Management course, but solving more complicated problems, and discrete-event simulation
using the Arena package. This module will also cover the basics of queueing theory, the mathematics of
waiting in line.
The course will be taught in a lecture format. I will use the whiteboard to emphasize key points and
perform example calculations, and the computer projector to demonstrate computer techniques such as
Python programming, Excel calculations, and Arena models.
Business Analytics and Information Technology
Course Number: 33:136:400
Course Title: Business Decision Analytics under Uncertainty
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COURSE MATERIALS
- Required textbook: you may use either one of the following:
o Business Analytics 136:400, collection of loose-leaf pages available from the campus
bookstore
o Or a used copy of Introduction to Probability Models — Operations Research, Volume
Two, 4
th
edition, by Wayne L. Winston, Cengage Learning, ISBN 9780534405724.
The two textbook options contain identical material; you may use either one.
- Some Harvard/Darden cases may be assigned online for a small fee (but probably not)
- Other online materials (free of charge) may be assigned
- Occasionally, supplementary course notes will be distributed in class and made available online.
LEARNING GOALS AND OBJECTIVES
The business school dean’s office requires that each course have learning objectives explicitly tied to
those of its academic program (in this case the New Brunswick undergraduate business program).
Students successfully completing this course should be able to demonstrate the following:
- Ability to use standard operations research techniques such as decision trees or simulation to
evaluate business plans and decisions in an uncertain environment [BAIT major objective (d)]
- Ability to apply quantitative modeling techniques to analyze business plans and decisions [The
second part of overall program objective 1(d)]
- Students will demonstrate fundamental computer programming skills in a modern programming
language such as Python, Java, or C++ [in this case, Python; BAIT major objective (a)]
Students will demonstrate these capabilities by correctly solving microeconomic business decision and
planning problems by using appropriate methodology, which could involve applying an appropriate
computer tool or writing a specialized Python program.
PREREQUISITES AND EXPECTED BACKGROUND
- 33:136:386 Operations Management is required
- 33:136:388 Foundations of Business Programming, or similar prior programming experience,
is recommended but not required
- Knowledge of basic probability theory at the level of 01:960:285 Introductory Statistics for
Business is expected (this course is also effectively a prerequisite given the school’s
registration rules).
ACADEMIC INTEGRITY
I do NOT tolerate cheating. Students are responsible for understanding the RU Academic Integrity Policy
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(http://academicintegrity.rutgers.edu/).
I will strongly enforce this Policy and pursue all violations. On all examinations and assignments,
students must sign the RU Honor Pledge, which states, “On my honor, I have neither received nor given
any unauthorized assistance on this examination or assignment.” I will screen all written assignments
through SafeAssign or Turnitin, plagiarism detection services that compare the work against a large
database of past work. Don’t let cheating destroy your hard-earned opportunity to learn. See
business.rutgers.edu/ai for more details.
ATTENDANCE AND CLASS CANCELLATION POLICIES
Attendance:
- I plan to pass around an attendance sheet during most classes and record the results in my
private grading spreadsheet.
- It is not generally necessary to inform me if you will be missing a class due to an illness,
job interview, religious observance or other reason, unless you are missing an exam or
missing more than 4 total class meetings (all of which are strongly discouraged). If
homework is due during a class you plan to miss, please hand it in early at my office or
have another student hand in your paper for you during class.
- If you miss an exam or homework due to illness, please contact me to see whether any
accommodation is possible. Severe illness or personal emergency is typically the only
reason I make accommodations, and there must be written, verifiable documentation.
Otherwise, you will receive a zero score on the missed assignment or exam.
Severe weather and other cancellations:
- Your primary source of information for whether the campus is open is the Rutgers New
Brunswick operating status page.
- Any announcement regarding course will be posted on Canvas.
ASSIGNMENTS, EXAMS
All polices are subject to change at the instructor’s discretion:
Assignments:
- There will be 6 assignments throughout the semester. Assignments are planned to be due one
week after distribution.
- Collaboration in small groups is permitted on homework assignments. However, you should
hand in your own individual assignment even if you collaborated with other students
- There is no credit for late assignments. You will receive a zero score for any late
assignments. However, I will drop your one lowest homework score when computing your
overall course score.
Exams:
- My plan is to administer a midterm exam three classes after the conclusion of each of the first
two modules. The two classes between the end of a module and its midterm exam will be a
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mixture of review and the material beginning the next module. Each midterm exam will cover
only the material in the preceding module.
- The material in the third module will be covered on the final exam, which will be cumulative
(covering all material in the course).
- Plan to bring a calculator to every exam. All exams are planned to be open book and open
notes. No computers, tablets, or phones of any kind are permitted in exams, so if you use a
laptop or tablet to take notes, please print your notes and take only the printout to the exam.
You may not use a smartphone as a calculator during an exam.
- No collaboration with any other individual is permitted on exams.
If I detect any violation of exam policies, I will prosecute them as a violation of the Rutgers
University academic integrity policy outlined here: (http://academicintegrity.rutgers.edu/).
- Students are expected to be familiar with the school’s academic integrity policies. Additional
information may be found at http:/business.rutgers.edu/ai.
- Final exam scheduling conflicts will be managed in strict adherence to university regulations,
with first preference for any students needing make-up exams to take the exam offered for the
other section of the course. I will make announcements about final exam conflicts topic as the
exam period nears. Any student with a final exam conflict must e-mail me a screenshot of
their entire final exam schedule for the semester by the last day of classes or have to take the
exam at the regularly scheduled time.
COURSE TECHNOLOGY
- Websites:
o I will use Canvas for announcements however for any information or assignments you
can use the main website
- During the second and third modules, it is recommended that you bring your laptop to most
classes. The necessary software is Excel and the YASAI add-in (open-source freeware).
o The PyCharm environment for Python programming (freeware).
- If time permits, I am also planning to cover discrete even simulation for which you would require
to have Arena discrete-event simulation package (free student version only)
- Please check your officially registered Rutgers email account regularly for class announcements.
- Portions of some of the assignments will be handed in electronically through Canvas. I will make
announcements when this is the case. When a portion of an assignment is handed in
electronically, there will also still be a hardcopy portion. A common student mistake is to hand in
only the electronic or only the hardcopy part of such an assignment, so please make sure you
hand in all portions of each assignment as instructed.
GRADING POLICIES
I reserve the right to make changes to grading policies.
- No letter grades are assigned to individual assignments or exams, only numeric scores from 0 to
100.
- Your course grade will be based on your overall aggregate score, which combines your scores on
all written class work with following weights.
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o 20% for each midterm exam
o o 40% final exam
o o 20% average homework score
o If your final exam grade is higher than the lower of your two
midterm exam grades, 10% grading weight will be transferred
from the lower midterm score to the final exam (so the weights
will become 10% lower midterm, 20% higher midterm, 50% final,
and 20% average homework). This policy is intended to allow
students to recover from poor performance on one of the
midterms. - I assign letter grades based on your overall aggregate
score. I do not use fixed cutoff values, but instead use my
judgment to select cutoffs that make sense for each particular
course and semester. The cutoffs may vary from other sections
when the exams taken by the sections are not identical.
- I would like to reward students who wake up early and attend the class. Therefore, I am planning
to give extra credits to students whose attendance is above some threshold. I will determine the
amount of extra credit and the threshold through the end of the semester.
- I plan to post homework and exam grades on Canvas.
- I will attempt to return graded homework assignments at the beginning of class, usually one week
after they are handed in. Any unclaimed homework will eventually go into a box outside my
office door. Any homework still in this box 30 days after the end of the semester will be recycled.
- I will review the results of midterm exams in class, with each student receiving a copy of the
exam, their answers, and a copy of my official solutions. I generally plan to review midterm
results one week after each exam, but I will announce the definite date once the exams are
graded. You must return the exam, your answers, and my solutions to me before leaving the
classroom. If you miss the class in which exams were reviewed or want to look at your exam
further, you may inspect your exam by appointment.
- If you believe there is a mistake in grading, feel free to get in contact with me. I will be happy to
regrade any assignment or exam on which you believe there might have been an error.
- I cannot allow external considerations such scholarships with GPA requirements to affect my
grading.
COURSE SCHEDULE
The planned lecture, assignment and readings schedule
Class
#
Topics
Readings
Homework
Distributed
Homework
Due
1
Introduction and procedures, introduction to decision
making under uncertainty
2
Introduction to decision trees, probability theory review
2.4 (except "incorporating risk
aversion"), 1.4
3
Conditional Probability and Bayes' formula
1.5-1.6
1
4
Bayes' formula and decision trees, start revisiting non-
EMV decision-making
2.5
1
5
Risk aversion and utility functions
2.1, supplementary readings
6
Critical fractile analysis
4.1-4.4
7
Critical fractile case study
Supplementary readings
2
8
Deterministic dynamic programming: shortest path.
6.1-6.2
2
6
9
Review for first midterm exam
10
First midterm exam
11
More deterministic dynamic programming: knapsack
and inventory
6.3-6.4
12
Introduction to elementary Python: loops, lists, and
Pycharm
Online resources
13
Exam results, numpy arrays, simple deterministic
dynamic programming in Python
Online resources
3
14
More deterministic dynamic programming with Python
6.5
3
15
Introduction stochastic dynamic programming
7.1,7.2
-
NO CLASS
-
NO CLASS
16
Stochastic DP with net present value, elementary
stochastic processes
7.3,7.4
17
More elementary stochastic processes, and their use with
stochastic DP
4
18
The curse of dimensionality, introduction/review for
spreadsheet-based simulation
Online resources; concepts in
chapter 11
4
19
Dynamic spreadsheet-based simulation (inventory),
review for second midterm
20
Second midterm exam
21
Exam results, mode dynamic Monte Carlo simulation
(part replacement)
22
Monte Carlo simulation of queue-like systems
23
Discrete-event simulation with Excel: an M/G/1 queue
5
24
Queuing - Little's law and the Pollaczek-Khinchin
formulas
8.1, 8.2
5
25
Pollaczek-Khinchin examples and introduction to
discrete-event simulation
8.2, excepts from 8.4,8.8
26
Learning to use the Arena discrete-event simulator
9.1,9.2,9.7,9.8,10.1,10.2
27
More complicated Arena Problems
10.3-10.6
6
28
Review for final exam
6
-
Final Exam
Topics: Cumulative - entire course
SUPPORT SERVICES
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[Rutgers University-New Brunswick ODS phone (848)445-6800 or email [email protected]tgers.edu]
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[Rutgers University-New Brunswick Dean of Students phone (848)932-2300 or email
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https://cm.maxient.com/reportingform.php?RutgersUniv&layout_id=7 . You may also contact the Office
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to speak with a staff member who is confidential and does not have a reporting responsibility, you may
contact the Office for Violence Prevention and Victim Assistance at (973)353-1918 or email
run.vpva@rutgers.edu]
If students who have experienced a temporary condition or injury that is adversely affecting their ability
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If you are in need of mental health services, please use our readily available services.
[Rutgers University-Newark Counseling Center: http://counseling.newark.rutgers.edu/]
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Students experiencing difficulty in courses due to English as a second language (ESL) should contact
the Program in American Language Studies for supports.
[Rutgers–Newark: PA[email protected]s.edu]
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If you are in need of additional academic assistance, please use our readily available services.
[Rutgers University-Newark Learning Center: http://www.ncas.rutgers.edu/rlc
[Rutgers University-Newark Writing Center: http://www.ncas.rutgers.edu/writingcenter]
[Rutgers University-New Brunswick Learning Center: https://rlc.rutgers.edu/]
[Optional items that many faculty include:
- Students must sign, date, and return a statement declaring that they understand the RU Academic
Integrity Policy.
- Students must sign, date, and return a statement declaring that they understand this syllabus.]
