Type of Credit: Required
Credit(s)
Number of Students
This is a required course for graduate students in the Department of Management Information Systems. Building on your undergraduate foundation in management science—including mathematical programming, simulation, and forecasting—we now shift the focus toward decision-making under uncertainty. Students will explore decision models where parameters and outcomes are uncertain. We will deepen our discussion of discrete and continuous probability distributions, which are critical tools for evaluating decisions in stochastic environments. A central theme of the course is the use of stochastic simulation modeling to analyze and solve a variety of business decision problems. We will cover advanced topics such as stochastic dependencies and numerical optimization algorithms. The course will conclude with applications to inventory control models, with a focus on both manufacturing systems and service supply chain management.
能力項目說明
The primary goal of this course is to sharpen students’ quantitative reasoning skills, particularly in situations where decision outcomes are uncertain or random. By the end of the course, students are expected to develop a solid understanding of applied probability modeling and Monte Carlo simulation. The focus is on empowering students to confidently apply these techniques to real-world decision-making under uncertainty.
Probabilistic computation will be at the heart of this course. Python will be the primary programming language. The best way to master the material is through hands-on practice—writing and testing your own code will be essential for effective learning. While the use of large language models (LLMs) as programming aids is encouraged, it is crucial that you truly understand the logic and methods behind the code you produce. The ultimate goal is not just to get results, but to deeply grasp the reasoning that drives them.
|
週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1 |
Introduction |
Monte-Carlo simulation |
(9/5) Lecture & Discussion |
|
2 |
Decision analysis |
Bertsimas & Freund 2004 (Chapter 1) |
(9/12) Lecture & Discussion |
|
3 |
Fundamentals of discrete probability |
Bertsimas & Freund 2004 (Chapter 2) |
(9/19) Lecture & Discussion |
|
4 |
Fundamentals of continuous probability |
Bertsimas & Freund 2004 (Chapter 3) |
(9/26) Lecture & Discussion |
|
5 |
Stochastic dependencies |
Bertsimas & Freund 2004 (Chapters 3 & 5) |
(10/3) Lecture & Discussion |
|
6 |
National holiday |
NA |
(10/10) No Class Meeting |
|
7 |
More prob. distributions |
Bertsimas & Freund 2004 (Chapter 5) |
(10/17) Lecture & Discussion |
|
8 |
National Holiday |
Bertsimas & Freund 2004 (Chapter 5) |
(10/24) No Class Meeting |
|
9 |
Stochastic optimization |
NA |
(10/31) Lecture & Discussion |
|
10 |
Exam |
Midterm Exam |
(11/7) In-Class Exam |
|
11 |
Dynamic simulation |
Multi-agent bidding & multi-period stocking |
(11/14) Lecture & Discussion |
|
12 |
Dynamic simulation |
Data-driven newsvendor |
(11/21) Lecture & Discussion |
|
13 |
Simulation & optimization |
Special topic |
(11/28) Lecture & Discussion |
|
14 |
Final project |
Project meeting |
(12/05) Meeting with Groups |
|
15 |
Final project |
Project meeting |
(12/12) Meeting with Groups |
|
16 |
Final project |
Project work |
(12/19) Finish final project |
Homework: 40%
I expect to distribute a total of 4 assignments during the semester.
Midterm: 35%
I will explain the exam logistics in detail.
Final Project: 25%
Don’t be a free rider. Form your team wisely.
Lecture notes and assigned readings will be provided. So NO textbooks are required. Below lists my key references in developing this course.
Bertsimas & Freund 2004 Data, Models, and Decisions: The Fundamentals of Management Science
Myerson & Zambrano 2019 Probability Models for Economic Decisions (2nd Edition).
Powell 2022 Sequential Decision Analytics and Modeling.
https://moodle.nccu.edu.tw/ https://colab.google/