CUHK-Shenzhen
Course Outline
1. Course Identity
A. Course as listed in CUHK-Shenzhen
The information in this block should be exactly as approved by CUHK Senate. In case
there are any differences, please explain in the table below.
Course code IBA6105
Course title (English) Quantitative Decision Models
Course title (Chinese) 量化决策模型
Units 3
Description (English) This course presents modelling techniques in optimization that are
known as linear programming, integer programming, and nonlinear
programming. Managerial decision problems in different applications
including finance, transportation, operations, etc., will be discussed.
Students are expected to model managerial decision problems using
spreadsheet optimization, e.g., Excel and Excel Solver.
Description (Chinese) 本课程讲述优化论中的建模方法，包括线性规划、整数规划与非线
性规划等。课程中会讨论优化论在金融、交通、运营等不同领域中
的管理决策应用。学生会通过课程练习基于excel与excel求解器的
管理决策问题建模与求解。
B. Corresponding course in CUHK(if had)
2. Prerequisites / Co-requisites(if required)
A. Prerequisites
None.
B. Co-requisites
None.
3. Learning Outcomes
After completing this course, students should:
a. be able to formulate real-world problems as optimization problems by some
modelling tricks, and recognize when problems they consider are linear, integer,
nonlinear programming problems.
b. be able to formulate and solve linear, integer, nonlinear optimization models with
excel and excel solver.
c. be able to perform sensitivity analysis with excel.
4. Course syllabus
The course will cover seven key optimization topics below:
• Generic formulation of optimization problems
• Linear Programming: Allocation, Covering, and Blending Models
• Linear Programming: Network Models
• Sensitivity Analysis in Linear Programs
• Integer Programming
• Nonlinear Programming
Applications of the above optimization methods cover:
• Marketing applications
• Transportation applications
• Employee staffing applications
• Finance applications
• …
5. Assessment Scheme
Component/ method % weight
Participation 10%
Homework 20%
Midterm exam 30%
Final exam 40%
Course Policies:
1. Missing the midterm or final exam without prior notification to and approval of the
instructors will automatically result in the "0" grade for the exam.
2. No make-up exam for Mid-Term Exam. If the Mid-Term Exam is missed under the
approval of the instructor, the Final Exam will take 60% of the assessment.
3. No make-up exam for the Final Exam.
4. There are about 5 assignments. No late homework will be accepted.
6. Grade descriptor
Grades Description
A - Hand in 100% assignments, AND
- Demonstrate fully understanding of the course materials in all quizzes,
assignments, midterm and final, AND
- Outstanding ideas are shown in the quizzes, assignments, midterm or
final
A- - Hand in 100% assignments AND,
- Demonstrate fully understanding of the course materials in all quizzes,
assignments, midterm and final
B - Hand in 100% assignments AND,
range - Demonstrate fully understanding of the course materials in MOST of the
quizzes, assignments, midterm and final
C - Hand in 100% assignments AND,
range - Demonstrate fully understanding of the course materials in SOME parts
of the quizzes, assignments, midterm and final
D - Hand in 100% assignments AND,
range - Demonstrate fully understanding of the course materials in LIMITED
parts of the quizzes, assignments, midterm and final
F - Overall score from assignments is 0 OR,
- Demonstrate fully understanding of the course materials in VERY
LIMITED parts of the quizzes, assignments, midterm and final
7. Feedback for evaluation
- Formal Course and Teaching Evaluation
- Feedback from office hour discussions
- Feedback after class
- Feedback from tutorial sessions
8. Reading
There are no required books for the class. All material will be provided in class slides. Some
of the material in the class is based on the following texts:
• Managerial Decision Modeling with Spreadsheets, 3rd Edition, by Nagraj
Balakrishnan, Barry Render, Jr. Ralph M. Stair.
• Introduction to Management Science: A Modeling and Case Studies Approach with
Spreadsheets, 5th Edition, by Frederick S Hillier and Mark S. Hillier.
The following book may also be useful for reference purposes:
Optimization modeling with spreadsheets, second edition
Baker, Kenneth R
Hoboken, N.J: John Wiley & Sons; 2011
(the electronic version of this book can be found on the library website)
Course components
Activity Hours/week
Lecture 3 hours/week
Tutorial 1 hours/ week
Homework 3 hours/week
9. Indicative teaching plan
Week Content/ topic/ activity
Lecture 1 Introduction to the course and generic formulation of optimization problems
• Linear Programming
• Integer Programming
• Nonlinear Programming
Lecture 2-3 Introduction of Linear Programming and sensitivity analysis
• allocation model
• covering model
• blending model
• mixed model
Lecture 4 Introduction of Network Models
Lecture 5-6 Applications of Linear Programming in business
Lecture 7 Midterm
Lecture 8-9 Introduction of Integer Programming and business applications
Lecture 10-11 Introduction of Nonlinear Programming and business applications
Lecture 12 Introduction of Decision Analysis
Lecture 13 Final review
Final Exam Final Exam
