SemesterFall Semester, 2023
DepartmentMA Program of Management Information Systems, First Year
Course NameDecision Science
Course TypeRequired Management People and Organization、Management Science
Course Objective
Course Description
Course Schedule

Class 1 (Sep 14)

Course introduction

Monte-Carlo simulation using Python & Google Colab

Class 2 (Sep 21)

Decision analysis

Bertsimas & Freund 2004 (Chapter 1)

Class 3 (Sep 28)

Fundamentals of discrete probability with simulation (I)

Bertsimas & Freund 2004 (Chapter 2)

Class 4 (Oct 5)

Fundamentals of discrete probability with simulation (II)

Some important discrete distributions

Class 5 (Oct 12)

Fundamentals of continuous probability with simulation

Bertsimas & Freund 2004 (Chapters 3 & 5)

Class 6 (Oct 19)

Stochastic dependencies

Multivariate normality & distribution distance

Classes 7-8

(Oct 26 & Nov 02)

More probability distributions

Random time-to-event & non-negativity

Class 9 (Nov 09)

Optimization of decision variables

Newsvendor model & revenue management

Derivative-free search algorithms

Class 10 (Nov 16)

Dynamic simulation

Multi-period ordering

Multi-agent bidding 

Class 11 (Nov 23)

Midterm exam

Logistics to be determined & announced

Class 12 (Nov 30)

Monte-Carlo methods for optimization

  Simulated annealing, particle swarm, & differential evolution

Class 13 (Dec 07)

Buy till You Die models

Latent attrition modeling in marketing science

Class 14 (Dec 14)

Special topics in decision-making

  To be decided

Class 15 (Dec 21)

NO class meeting

Final project preparation

Class 16 (Dec 28)

Meetings with groups

  Final project discussion

Class 17 (Jan 04)

NO class meeting

Final project development

Class 18 (Jan 11)

Final report due at 23:59 on Jan 12, 2023

Upload your code & report onto WM5

Teaching Methods
Teaching Assistant

To be decided


This is a tentative plan and I reserve the right to adjust score allocation rules.

Homework: 40% I expect to distribute 4-5 assignments during the semester.

Midterm: 35% I will explain the exam logistics in detail.

Final Project: 25% I will explain the deliverables in detail.

Don’t be a free rider. Form your team wisely.

Textbook & Reference

Lecture notes and assigned readings will be provided. So NO textbooks are required. Below is a list 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).

Kroese et al 2022 Data science and machine learning: Mathematical and statistical methods.

Powell 2022 Reinforcement Learning and Stochastic Optimization.

Urls about Course

Decision Sciences Syllabus_HChuang.pdf